Product Key Windows - Microsoft Office - Other Softwares | Latest

Speed Up My PC 2010 crack serial keygen

Speed Up My PC 2010 crack serial keygen

In this post, you will find free updated product keys you can use to activate the Windows 8 Professional. Why Use Windows 8 Professional? In most cases, serial programs run on modern computers "waste" potential 95% of your code to be parallel, and never achieve better than 20x speedup no. You can find Windows Keys or Microsoft Office Product Keys Or Other Software keys latest , this is a free place to find out!

Speed Up My PC 2010 crack serial keygen - for

CCleaner®

New: Driver UpdaterBoost the performance of PC hardware and devices

NoYesYes

Faster ComputerControl which apps use your computer's resources

YesYesYes

Privacy ProtectionRemoves tracking files and browsing data

YesYesYes

PC Health CheckAutomatically analyzes, fixes and tunes your PC's performance

YesYesYes

Software UpdaterQuickly updates apps to reduce security vulnerabilities

NoYesYes

Cleans EverywhereEven places other cleaners can't reach

NoYesYes

Guards Against Junk FilesMonitors junk in real-time

NoYesYes

Automatically Clears HistoryCleans your browser when you close it

NoYesYes

Faster, Longer-lasting Hard DrivesIncludes Defraggler, to keep hard disks healthy and running efficiently

  Yes

File RecoveryIncludes Recuva, so you never have to worry about losing a file again

  Yes

See Inside your PCIncludes Speccy, so you can spot issues or find compatible upgrades

  Yes
Источник: [arenaqq.us]

Take the frustration out of updating drivers!

Fixes Driver Problems - FAST!

Hardware Driver Stop Working? Get Fast Fixes For Your Driver Problems. DriverDoc will get your computer up and running in no time. Click here to start your driver scan. Download Now »

Driver Update Wizard!

Use the Driver Update Wizard with simple One Click Updates for your old drivers. DriverDoc is the right prescription to get your driver problems fixed fast, so your computer runs like new. Fix it now! Download Now »


Smart Scan - 1 Click Repair

Do You Get Cryptic Error Messages? DriverDoc Smart Scan your system and fix the error messages that are driving you crazy. Even custom computer systems are no problem with DriverDoc. Click to start your driver scan. Download Now »

Live Driver Tech Support

Get support for over 16,, drivers inside DriverDoc. Call us for Live Driver Tech Support included in your driver update software. Click here for your driver scan and get help today!
Download Now »


Источник: [arenaqq.us]

This is the ultimate guide to Windows 8 Pro product key and activation.

 

WindowsProfessional-Product-Key


In this post, you will find free updated product keys you can use to activate the Windows 8 Professional. 

 

Why Use Windows 8 Professional?

Here are the incredible features of Windows 8:

  • Easy to use interface 
  • Comes with SkyDrive 
  • Split-screen app
  • Metro-style
  • Improved Windows store 
  • Upgraded app search function
  • Fast boot
  • Comes with Xbox games
  • Live synching system
  • Upgraded Internet Explorer and Antivirus program
  • Improved security

Windows 8 was received with much anticipation from Windows users.

It became the preferred Operating System among many who found it user-friendly and appealing. It came with interactive tiles and was pleasing to the eye.

Microsoft changed the look of Windows with the launch of Windows 8.

Eventually, the new metro style tiled interface became the accepted style for desktop. 

Even Windows 10, which is the latest and last version of Windows OS, uses the same style that was introduced in Windows 8 for the very first time. 

 

What Is Windows 8 Pro Product Key?

Windows 8 professional product key is a digit character code used to activate a copy of Windows 8 professional. Without a working pro activation key, you will not be able to activate the Operating System.

The product key code looks like this: XXXXX-XXXXX-XXXXX-XXXXX-XXXXX

If you have obtained a copy of Windows 8 professional, you should see the Windows 8 professional product key inside the product box. 

Given you have purchased Windows 8 pro online, you will receive your Windows 8 professional product key via email. 

In case you have lost your product key, you can retrieve it using a key finder. 

However, if you have purchased the Operating System, you can download and activate it using free product keys on this post. 

We have a wide range of keys you can use.

 

Free List of Windows 8 Professional Product Activation Keys 

Here is a free list of % free working Windows 8 pro product serial keys you can use to activate your copy of Windows 8 professional.

Simply copy one of these product keys and use them.

 

84NRV-6CJR6-DBDXH-FYTBF-4X49V

QGR4NPMD-KCRQBXT-YG

ND8P2-BD2PB-DD8HMR-CRYQH

T3NJK-3PT7BJ-2X27F-8B2KV

YMMV-FVDXB-QP6XF-9FTRT-P7F9V

 

BTNJ7-FFMBR-FF9BH-7QMJ9-H49T7

HB39N-V9K6F-PV-KWBTC-Q3R9V

XWCHQ-CDMYC-9WN2C-BWWTV-YY2KV

RRYGR-8JNBY-V2RJ9-TJP4PT7

4Y8N3-H7MMW-C76VJ-YD3XV-MBDKV

 

28VNV-HF42G-K2WM9-JXRJQ-2WBQW

BDDNV-BQ27P-9P9JJ-BQJKTJXV

CR8NGKCR-X2MPD-G7M7P-GQ4DH

6PNR4BBH-XX8K2-DCKVMFDH

9XNM-YYYR9HM-YFPTX-T8XT7

 

NTTX3-RV7VB-T7X7F-WQYYY-9Y92F

MBFBV-W3DPMVKN-PJCQD-KKTF7

DNJXJ-7XBWT-X22TX-BKG7J

6RH4V-HNTWC-JQKG8-RFR3R

Y8N3-H7MMW-C76VJ-YD3XV-MBDKV

 

6RH4V-HNTWC-JQKG8-RFR3R

XKY4K-2NRWR-8F6PRF-CRYQH

TK8TP-9JN6P-7X7WW-RFFTV-B7QPF

NF32V-Q9P3W-7DR7Y-JGWRW-JFCK8

DNJXJ-7XBWT-X22TX-BKG7J


Each Windows 8 professional product key on this list is genuine and should work for most users. However, if you don’t find a working serial key, then someone else has probably used it. 

Note that you can only use a product key Windows on one computer. If none of the keys work for you, feel free to bookmark this page and come back tomorrow for updated keys. 

We update these keys regularly; therefore, you can be sure you find a working Windows 8 professional product key at the end of the day.

 

FAQ

How Do I Get Windows 8 Pro?

If your PC is running a genuine copy of Windows 7, you can easily upgrade to Windows 8 without paying the additional license fee.

Otherwise, you will be required to buy a genuine copy of Windows 8 and get a pro product key along with it. 

The method involves these simple steps:

  1. Launch the Windows 8 Upgrade Assistant on your computer 
  2. The upgrade wizard will determine the hardware of your computer 
  3. If your Windows PC meets the system requirements, you will be able to download updated files
  4. Wait for Windows update files to download and install 

If your PC doesn’t qualify for the upgrade, you have the following options:

  • Purchase and install Windows from Microsoft using the provided pro key 
  • Purchase a computer with pre-installed Windows
  • Download and install Windows 8 professional using a free pro product key on this page

 

What Do You Need in Windows 8 Professional Product Key?

When installing Windows 8, you will be required to provide an activation key. You must provide a working Windows 8 professional product key to proceed with the installation. 

If you don’t have a product key, it might be impossible to install and activate the Operating System. Luckily, you can find free product keys online. 

You can also install the software with a generic Windows 8 professional product key and continue using it without activating it. However, you will not be able to access its premium features.

 

How Can I Activate My Windows 8 Pro?

You can activate Windows 8 from the PC settings app. To do this, press the Windows key + C to access the charms bar. Then click Settings and then Change PC settings. 

If your copy of Windows is not activated, you will see the option Activate Windows. Alternatively, you can go to PC and devices, then PC info to see if you have an activated Windows. 

Click the activate button to activate your Windows over the internet. If you see an error preventing you from activating, perform a search form the error to find more information. 

 

Where Do I Find My Windows 8 Pro Product Key on My HP Laptop?

When Microsoft unveiled Windows 8, they changed from including a sticker with a serial key to BIOS embedded product keys. 

According to the tech giant, by eliminating the sticker, they are able to eliminate one of the easiest ways for reprehensible people to get a genuine activation key. 

The move also enables them to eliminate the worry the sticker could be damaged and the irritating process of having to type all the numbers and letters when installing Windows 8 professional. 

With the product key in the BIOS, whenever you want to reinstall the operating system on the same computer it can with, the installation wizard will grab the product key automatically from the BIOS. That’s why you cannot find the activation key on a sticker on your HP laptop.

 

Can I Download Windows 8 Pro for Free?

Here are a few ways to get Windows 8 for free:

  • Obtain a preview version of the Windows 8 OS. Go to this page and download an ISO file. Burn the ISO file to a CD/DVD. 
  • Get a copy of Windows 8 for students. Visit this page to get a copy of Windows 8 if you’re a student. You may have to pay a small price. 
  • Simply upgrade to Windows 8 using a product key. Use this page if you have previously purchased Windows 8. 
petr kudlacek arenaqq.us

Petr Kudlacek

Petr is a serial tech entrepreneur and the CEO of Apro Software, a machine learning company. Whenever he’s not blogging about technology for arenaqq.us or arenaqq.us, Petr enjoys playing sports and going to the movies. He’s also deeply interested about mediation, Buddhism and biohacking.

Categories WindowsИсточник: [arenaqq.us]

AVG TuneUp for PC

Speed up, clean up, and fix your PC
with our advanced PC performance optimizer.

Get your PC running like new:

Clean out junk for more storage space.
Enjoy faster performance and startup speeds.
Update your programs automatically and avoid security risks.

See all features

Automatically fix and maintain your PC

Tired of bugs, crashes, and freezes? AVG’s Improved Automatic Maintenance tunes your PC every week for you, so you can enjoy better performance every time you turn it on.

Speed up and tune up your PC

Get your programs running faster, your PC starting quicker, and your games running smoother with AVG TuneUp and our patented Sleep Mode technology. Here’s how it works:

Remove bloatware and junk programs

Unnecessary programs, old toolbars and trial versions, and software that came preinstalled in your PC can take up space and cause trouble down the road. Which is why we make it easy to get rid of them.

Get more room for the stuff that matters

Your PC starts accumulating junk from the very first day: leftover Windows files, junk from the web, and more. You don’t need any of it, so AVG TuneUp cleans it out so your PC has the space for the things you need.

79%quicker startup

(in seconds)

30%faster work performance

(in points)

71 GBcleaned up

(in GB of free space)

Usage

For personal and family use only. Not for business or commercial use.


System Requirements

  • WindowsWindows 10, 8, and Windows 7 (Windows XP can be found here)
  • Apple MacOS (Mavericks) or above
  • Android Android (Lollipop, API 21) or above

Languages

For Windows: Chinese (simplified), Chinese (traditional), Czech, Danish, Dutch, English, French, German, Hungarian, Indonesian, Italian, Japanese, Korean, Malay, Polish, Portuguese (Brazil), Portuguese (Portugal), Russian, Serbian, Slovak, Spanish, and Turkish.

For Mac: English only.

For Android: Arabic, Chinese (simplified), Chinese (traditional), Czech, Danish, Dutch, English, Finnish, French, German, Greek, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Malay, Norwegian, Polish, Portuguese (Brazil), Portuguese (Portugal), Russian, Serbian, Slovak, Spanish, Swedish, Thai, Turkish, Ukrainian, and Vietnamese.

Frequently Asked Questions

How will AVG TuneUp improve the performance of my PC?

AVG TuneUp speeds up and cleans your PC by detecting and safely removing bloatware (unnecessary software) from your system. AVG TuneUp also addresses the issues that can cause system crashes and unpredictable behavior, including junk files, unnecessary programs installed on your PC, outdated software, and unusual system settings.

Over time, the reliability and performance of your PC will decline. You may notice issues such as:

  • Programs that run very slowly, crash, or freeze.
  • A lack of disk space.
  • Pop-ups from “bloatware”

AVG TuneUp optimizes your PC to restore its performance and keep it in top shape. To see AVG TuneUp in action, take a look at the results of our PC speed and cleaning tests.

How long will it take for AVG TuneUp to scan and clean my PC?

It typically takes only a few minutes for AVG TuneUp to scan and clean your PC, depending on the scan type and the amount of data being analyzed. But you can continue to use your PC normally while AVG TuneUp scans and cleans. The latest version of AVG TuneUp includes Automatic Maintenance, which runs silently in the background when needed to remove unnecessary items from your PC.

When you configure Automatic Maintenance for the first time, you can specify which item types you want AVG TuneUp to remove. AVG TuneUp is designed to run automatically, so you don’t need to worry about manually tuning your PC.

Is AVG TuneUp free to download?

Download a free trial of AVG TuneUp to enjoy our premium product completely free for 30 days. You’ll be able to scan your machine to identify bloatware and other unnecessary files that may be slowing you down, then remove them with just a single click. Optimize your PC absolutely free with our free trial today.

How can I test my PC performance?

A computer performance test works by first setting a benchmark and then running another test one to two months later to see how performance has changed. You can also use this test to measure your PC’s battery life.

Benchmarking your PC’s performance allows you to measure how fast your PC handles various operations by simulating normal processes, including Microsoft Office and Adobe products, playing games, browsing the web, and editing multimedia files.

Is AVG TuneUp an antivirus?

No. AVG TuneUp is a PC optimization tool that speeds up and cleans up your PC while fixing issues that may cause system crashes and other unexpected behavior. There is no “AVG TuneUp antivirus.”

Although AVG TuneUp does not actively protect your PC against viruses, you can use the application alongside trusted antivirus software to identify and remove unwanted programs that may have been installed on your PC as a result of malware. If you're interested in antivirus software in addition to cleanup software, we recommend getting an AVG Ultimate subscription, which includes both AVG Internet Security and AVG TuneUp.

Is AVG TuneUp the same as AVG TuneUp Utilities?

AVG TuneUp Utilities is an older version of AVG TuneUp that is no longer supported. If AVG TuneUp Utilities is installed on your PC, you can continue to use the product, but it no longer receives updates or bug fixes.

We strongly recommend downloading or upgrading to the latest version of AVG TuneUp, which includes extra features, improvements to existing features, and an updated user interface. After you download or upgrade, you’ll be able to install AVG TuneUp on an unlimited number of devices to optimize every PC in your household.

AVG TuneUp for PC AVG TuneUp for PC

The powerful, easy way to make your PC faster, cleaner, and better.

Get your PC running like new:

Clean out junk for more storage space.
Enjoy faster performance and startup speeds.
Update your programs automatically and avoid security risks.

See all features

Looks like you’re using arenaqq.us you like this app for Mac or Windows?

Looks like you’re using arenaqq.us the Google Play button to get antivirus for arenaqq.us download it for arenaqq.us like you’re using arenaqq.us antivirus file won't work on your arenaqq.usad it for arenaqq.us like you’re using arenaqq.us you like this app for Windows or Mac?Looks like you’re using arenaqq.us the Google Play button to get antivirus for arenaqq.us download it for arenaqq.us like you’re using arenaqq.us the App Store button to get antivirus for arenaqq.us download it for arenaqq.us antivirus file is for Android and won't work on your arenaqq.us antivirus file is for Android and won't work on your arenaqq.us antivirus file is for Android and won't work on your arenaqq.us antivirus file is for iOS and won't work on your arenaqq.us antivirus file is for iOS and won't work on your arenaqq.us antivirus file is for iOS and won't work on your arenaqq.us antivirus file is for PC and won’t work on your arenaqq.us antivirus file is for Mac and won’t work on your arenaqq.us antivirus file is for Android and won’t work on your arenaqq.us antivirus file is for iOS and won’t work on your arenaqq.us like you’re using arenaqq.us you like this app for Mac or Windows?Looks like you’re using arenaqq.us the Google Play button to get antivirus for arenaqq.us download it for arenaqq.us like you’re using arenaqq.us antivirus file won't work on your arenaqq.usad it for arenaqq.us like you’re using arenaqq.us you like this app for Windows or Mac?Looks like you’re using arenaqq.us the Google Play button to get antivirus for arenaqq.us download it for arenaqq.us like you’re using arenaqq.us the App Store button to get antivirus for arenaqq.us download it for arenaqq.us file is for Android and won't work on your arenaqq.us file is for Android and won't work on your arenaqq.us file is for Android and won't work on your arenaqq.us file is for iOS and won't work on your arenaqq.us file is for iOS and won't work on your arenaqq.us file is for iOS and won't work on your arenaqq.us antivirus file is for PC and won’t work on your arenaqq.us antivirus file is for Mac and won’t work on your arenaqq.us antivirus file is for Android and won’t work on your arenaqq.us antivirus file is for iOS and won’t work on your arenaqq.us like you’re using arenaqq.us you like this app for Mac or Windows?Looks like you’re using arenaqq.us the Google Play button to get antivirus for arenaqq.us download it for arenaqq.us like you’re using arenaqq.us antivirus file won't work on your arenaqq.usad it for arenaqq.us like you’re using arenaqq.us you like this app for Windows or Mac?Looks like you’re using arenaqq.us the Google Play button to get antivirus for arenaqq.us download it for arenaqq.us like you’re using arenaqq.us the App Store button to get antivirus for arenaqq.us download it for arenaqq.us VPN file is for Android and won't work on your arenaqq.us VPN file is for Android and won't work on your arenaqq.us VPN file is for Android and won't work on your arenaqq.us VPN file is for iOS and won't work on your arenaqq.us VPN file is for iOS and won't work on your arenaqq.us VPN file is for iOS and won't work on your arenaqq.us antivirus file is for PC and won’t work on your arenaqq.us antivirus file is for Mac and won’t work on your arenaqq.us antivirus file is for Android and won’t work on your arenaqq.us antivirus file is for iOS and won’t work on your machine.

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Windows

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

WindowsMacAndroidiOS(from Google Play)(from Google Play)(from Google Play)Back
AVG Logo
Источник: [arenaqq.us]

Download Crack CareUEyes Pro Crack + Activation Key Free Download CareUEyes Pro Crack is a utility designed to protect your eyes when using your computer for extended […]

Read more

AVG Internet Security Crack + Activation Code [] AVG Internet Security Crack protects your computer from viruses and malware, secures your emails, protects your files, passwords, and webcam from […]

Read more

Avast Pro Antivirus Crack + License Key Download Avast Pro Antivirus Crack is a security suite that includes antivirus, antispyware, and heuristic detection systems. The software supports […]

Read more

Macrium Reflect Crack + License Key Download Macrium Reflect Crack is one of the admired image-based backup and disaster recovery solutions. It is a speedy, simple, and […]

Read more

AVG Internet Security Crack With Activation Key [] AVG Internet Security Crack is the most advanced and essential security tool which is built upon the powerful AVG Antivirus. […]

Read more

Snagit Crack + License Key Download (bit) Snagit Crack is the gift tool for screen capture and screen recording on Windows and Mac operating systems. Visuals improve the refinement […]

Read more

Blackmagic Fusion Crack + Product Key Full Download Blackmagic Fusion Crack is the most advanced compositing software available on the market today for visual effects artists, broadcast […]

Read more

AVG Antivirus Crack + Serial Key Full Version Download AVG Antivirus Crack is a program that ensures security and online safety. It has a robust, rich-featured, newly designed, […]

Read more

Reason Crack + Keygen Torrent Reason Crack is the best music tool for music management. Reason allows you to mix, blend, add the songs to make the music […]

Read more

DVDFab Crack + Keygen Full Torrent Download [] DVDFab Crack is a marvelous software with great features. It comes with DVD burning, CD and DVD lock breaking, backup […]

Read more
Источник: [arenaqq.us]

Introduction to Parallel Computing Tutorial

Table of Contents

  1. Abstract
    1. Parallel Computing Overview
      1. What is Parallel Computing?
      2. Why Use Parallel Computing?
      3. Who is Using Parallel Computing?
    2. Concepts and Terminology
      1. von Neumann Computer Architecture
      2. Flynn's Taxonomy
      3. Parallel Computing Terminology
      4. Potential Benefits, Limits and Costs of Parallel Programming
    3. Parallel Computer Memory Architectures
      1. Shared Memory
      2. Distributed Memory
      3. Hybrid Distributed-Shared Memory
    4. Parallel Programming Models
      1. Parallel Programming Models Overview
      2. Shared Memory Model
      3. Threads Model
      4. Distributed Memory / Message Passing Model
      5. Data Parallel Model
      6. Hybrid Model
      7. SPMD and MPMP
    5. Designing Parallel Programs
      1. Automatic vs. Manual Parallelization
      2. Understand the Problem and the Program
      3. Partitioning
      4. Communications
      5. Synchronization
      6. Data Dependencies
      7. Load Balancing
      8. Granularity
      9. I/O
      10. Debugging
      11. Performance Analysis and Tuning
    6. Parallel Examples
      1. Array Processing
      2. PI Calculation
      3. Simple Heat Equation
      4. 1-D Wave Equation
    7. References and More Information

Abstract

This is the first tutorial in the "Livermore Computing Getting Started" workshop. It is intended to provide only a brief overview of the extensive and broad topic of Parallel Computing, as a lead-in for the tutorials that follow it. As such, it covers just the very basics of parallel computing, and is intended for someone who is just becoming acquainted with the subject and who is planning to attend one or more of the other tutorials in this workshop. It is not intended to cover Parallel Programming in depth, as this would require significantly more time. The tutorial begins with a discussion on parallel computing - what it is and how it's used, followed by a discussion on concepts and terminology associated with parallel computing. The topics of parallel memory architectures and programming models are then explored. These topics are followed by a series of practical discussions on a number of the complex issues related to designing and running parallel programs. The tutorial concludes with several examples of how to parallelize several simple problems. References are included for further self-study. 

Overview

What is Parallel Computing?

Serial Computing

Traditionally, software has been written for serial computation:

  • A problem is broken into a discrete series of instructions
  • Instructions are executed sequentially one after another
  • Executed on a single processor
  • Only one instruction may execute at any moment in time

For example:

Parallel Computing

In the simplest sense, parallel computing is the simultaneous use of multiple compute resources to solve a computational problem:

  • A problem is broken into discrete parts that can be solved concurrently
  • Each part is further broken down to a series of instructions
  • Instructions from each part execute simultaneously on different processors
  • An overall control/coordination mechanism is employed

For example:

  • The computational problem should be able to:
    • Be broken apart into discrete pieces of work that can be solved simultaneously;
    • Execute multiple program instructions at any moment in time;
    • Be solved in less time with multiple compute resources than with a single compute resource.
  • The compute resources are typically:
    • A single computer with multiple processors/cores
    • An arbitrary number of such computers connected by a network

Parallel Computers

  • Virtually all stand-alone computers today are parallel from a hardware perspective:
    • Multiple functional units (L1 cache, L2 cache, branch, prefetch, decode, floating-point, graphics processing (GPU), integer, etc.)
    • Multiple execution units/cores
    • Multiple hardware threads
IBM BG/Q Compute Chip with 18 cores (PU) and 16 L2 Cache units (L2)
  • Networks connect multiple stand-alone computers (nodes) to make larger parallel computer clusters.
  • For example, the schematic below shows a typical LLNL parallel computer cluster:
    • Each compute node is a multi-processor parallel computer in itself
    • Multiple compute nodes are networked together with an Infiniband network
    • Special purpose nodes, also multi-processor, are used for other purposes
  • The majority of the world's large parallel computers (supercomputers) are clusters of hardware produced by a handful of (mostly) well known vendors.

Source: Toporg

Why Use Parallel Computing?

The Real World is Massively Complex

  • In the natural world, many complex, interrelated events are happening at the same time, yet within a temporal sequence.
  • Compared to serial computing, parallel computing is much better suited for modeling, simulating and understanding complex, real world phenomena.
  • For example, imagine modeling these serially:

Main Reasons for Using Parallel Programming

SAVE TIME AND/OR MONEY
  • In theory, throwing more resources at a task will shorten its time to completion, with potential cost savings.
  • Parallel computers can be built from cheap, commodity components.
SOLVE LARGER / MORE COMPLEX PROBLEMS
  • Many problems are so large and/or complex that it is impractical or impossible to solve them using a serial program, especially given limited computer memory.
  • Example: "Grand Challenge Problems" (arenaqq.us) requiring petaflops and petabytes of computing resources.
  • Example: Web search engines/databases processing millions of transactions every second
PROVIDE CONCURRENCY
  • A single compute resource can only do one thing at a time. Multiple compute resources can do many things simultaneously.
  • Example: Collaborative Networks provide a global venue where people from around the world can meet and conduct work "virtually".
TAKE ADVANTAGE OF NON-LOCAL RESOURCES
  • Using compute resources on a wide area network, or even the Internet when local compute resources are scarce or insufficient.
  • Example: SETI@home (arenaqq.us) has over million users in nearly every country in the world. (May, ).
MAKE BETTER USE OF UNDERLYING PARALLEL HARDWARE
  • Modern computers, even laptops, are parallel in architecture with multiple processors/cores.
  • Parallel software is specifically intended for parallel hardware with multiple cores, threads, etc.
  • In most cases, serial programs run on modern computers "waste" potential computing power.

The Future

  • During the past 20+ years, the trends indicated by ever faster networks, distributed systems, and multi-processor computer architectures (even at the desktop level) clearly show that parallelism is the future of computing.
  • In this same time period, there has been a greater than ,x increase in supercomputer performance, with no end currently in sight.
  • The race is already on for Exascale Computing - we are entering Exascale era
Source: Toporg

Who is Using Parallel Computing?

Science and Engineering

  • Historically, parallel computing has been considered to be "the high end of computing", and has been used to model difficult problems in many areas of science and engineering:
  • Atmosphere, Earth, Environment
  • Physics - applied, nuclear, particle, condensed matter, high pressure, fusion, photonics
  • Bioscience, Biotechnology, Genetics
  • Chemistry, Molecular Sciences
  • Geology, Seismology
  • Mechanical Engineering - from prosthetics to spacecraft
  • Electrical Engineering, Circuit Design, Microelectronics
  • Computer Science, Mathematics
  • Defense, Weapons

Industrial and Commercial

  • Today, commercial applications provide an equal or greater driving force in the development of faster computers. These applications require the processing of large amounts of data in sophisticated ways. For example:
  • "Big Data", databases, data mining
  • Artificial Intelligence (AI)
  • Oil exploration
  • Web search engines, web based business services
  • Medical imaging and diagnosis
  • Pharmaceutical design
  • Financial and economic modeling
  • Management of national and multi-national corporations
  • Advanced graphics and virtual reality, particularly in the entertainment industry
  • Networked video and multi-media technologies
  • Collaborative work environments

Global Applications

  • Parallel computing is now being used extensively around the world, in a wide variety of applications.

Source: Toporg


Source: Toporg

Concepts and Terminology

von Neumann Architecture

John von Neumann circa s
(Source: LANL archives)
  • Named after the Hungarian mathematician John von Neumann who first authored the general requirements for an electronic computer in his papers.
  • Also known as "stored-program computer" - both program instructions and data are kept in electronic memory. Differs from earlier computers which were programmed through "hard wiring".
  • Since then, virtually all computers have followed this basic design:
  • Comprised of four main components:
  1. Memory
  2. Control Unit
  3. Arithmetic Logic Unit
  4. Input/Output
  • Read/write, random access memory is used to store both program instructions and data
  • Program instructions are coded data which tell the computer to do something
  • Data is simply information to be used by the program
  • Control unit fetches instructions/data from memory, decodes the instructions and then sequentially coordinates operations to accomplish the programmed task.
  • Arithmetic Unit performs basic arithmetic operations
  • Input/Output is the interface to the human operator
  • More info on his other remarkable accomplishments: arenaqq.us
  • Parallel computers still follow this basic design, just multiplied in units. The basic, fundamental architecture remains the same.

Flynn's Classical Taxonomy

  • There are a number of different ways to classify parallel computers. Examples are available in the references.
  • One of the more widely used classifications, in use since , is called Flynn's Taxonomy.
  • Flynn's taxonomy distinguishes multi-processor computer architectures according to how they can be classified along the two independent dimensions of Instruction Stream and Data Stream. Each of these dimensions can have only one of two possible states: Single or Multiple.
  • The matrix below defines the 4 possible classifications according to Flynn:

Single Instruction, Single Data (SISD)

  • A serial (non-parallel) computer
  • Single Instruction: Only one instruction stream is being acted on by the CPU during any one clock cycle
  • Single Data: Only one data stream is being used as input during any one clock cycle
  • Deterministic execution
  • This is the oldest type of computer
  • Examples: older generation mainframes, minicomputers, workstations and single processor/core PCs.

Single Instruction, Multiple Data (SIMD)

  • A type of parallel computer
  • Single Instruction: All processing units execute the same instruction at any given clock cycle
  • Multiple Data: Each processing unit can operate on a different data element
  • Best suited for specialized problems characterized by a high degree of regularity, such as graphics/image processing.
  • Synchronous (lockstep) and deterministic execution
  • Two varieties: Processor Arrays and Vector Pipelines
  • Examples:
    • Processor Arrays: Thinking Machines CM-2, MasPar MP-1 & MP-2, ILLIAC IV
    • Vector Pipelines: IBM , Cray X-MP, Y-MP & C90, Fujitsu VP, NEC SX-2, Hitachi S, ETA10
  • Most modern computers, particularly those with graphics processor units (GPUs) employ SIMD instructions and execution units.

Multiple Instruction, Single Data (MISD)

  • A type of parallel computer
  • Multiple Instruction: Each processing unit operates on the data independently via separate instruction streams.
  • Single Data: A single data stream is fed into multiple processing units.
  • Few (if any) actual examples of this class of parallel computer have ever existed.
  • Some conceivable uses might be:
    • multiple frequency filters operating on a single signal stream
    • multiple cryptography algorithms attempting to crack a single coded message.

Multiple Instruction, Multiple Data (MIMD)

  • A type of parallel computer
  • Multiple Instruction: Every processor may be executing a different instruction stream
  • Multiple Data: Every processor may be working with a different data stream
  • Execution can be synchronous or asynchronous, deterministic or non-deterministic
  • Currently, the most common type of parallel computer - most modern supercomputers fall into this category.
  • Examples: most current supercomputers, networked parallel computer clusters and "grids", multi-processor SMP computers, multi-core PCs.
  • Note many MIMD architectures also include SIMD execution sub-components

General Parallel Computing Terminology

  • Like everything else, parallel computing has its own jargon. Some of the more commonly used terms associated with parallel computing are listed below. Most of these will be discussed in more detail later.
CPU

Contemporary CPUs consist of one or more cores - a distinct execution unit with its own instruction stream. Cores with a CPU may be organized into one or more sockets - each socket

with its own distinct memory . When a CPU consists of two or more sockets, usually hardware infrastructure supports memory sharing across sockets.

Node

A standalone "computer in a box". Usually comprised of multiple CPUs/processors/cores, memory, network interfaces, etc. Nodes are networked together to comprise a supercomputer.

Task

A logically discrete section of computational work. A task is typically a program or program-like set of instructions that is executed by a processor. A parallel program consists of multiple tasks running on multiple processors.

Pipelining

Breaking a task into steps performed by different processor units, with inputs streaming through, much like an assembly line; a type of parallel computing.

Shared Memory

Describes a computer architecture where all processors have direct access to common physical memory. In a programming sense, it describes a model where parallel tasks all have the same "picture" of memory and can directly address and access the same logical memory locations regardless of where the physical memory actually exists.

Symmetric Multi-Processor (SMP)

Shared memory hardware architecture where multiple processors share a single address space and have equal access to all resources - memory, disk, etc.

Distributed Memory

In hardware, refers to network based memory access for physical memory that is not common. As a programming model, tasks can only logically "see" local machine memory and must use communications to access memory on other machines where other tasks are executing.

Communications

Parallel tasks typically need to exchange data. There are several ways this can be accomplished, such as through a shared memory bus or over a network.

Synchronization

The coordination of parallel tasks in real time, very often associated with communications.

Synchronization usually involves waiting by at least one task, and can therefore cause a parallel application's wall clock execution time to increase.

Computational Granularity

In parallel computing, granularity is a quantitative or qualitative measure of the ratio of computation to communication.

  • Coarse: relatively large amounts of computational work are done between communication events
  • Fine: relatively small amounts of computational work are done between communication events
Observed Speedup

Observed speedup of a code which has been parallelized, defined as:

wall-clock time of serial execution wall-clock time of parallel execution

One of the simplest and most widely used indicators for a parallel program's performance.

Parallel Overhead

Required execution time that is unique to parallel tasks, as opposed to that for doing useful work. Parallel overhead can include factors such as:

  • Task start-up time
  • Synchronizations
  • Data communications
  • Software overhead imposed by parallel languages, libraries, operating system, etc.
  • Task termination time
Massively Parallel

Refers to the hardware that comprises a given parallel system - having many processing elements. The meaning of "many" keeps increasing, but currently, the largest parallel computers are comprised of processing elements numbering in the hundreds of thousands to millions.

Embarrassingly (IDEALY) Parallel

Solving many similar, but independent tasks simultaneously; little to no need for coordination between the tasks.

Scalability

Refers to a parallel system's (hardware and/or software) ability to demonstrate a proportionate increase in parallel speedup with the addition of more resources. Factors that contribute to scalability include:

  • Hardware - particularly memory-cpu bandwidths and network communication properties
  • Application algorithm
  • Parallel overhead related
  • Characteristics of your specific application

Potential Benefits, Limits and Costs of Parallel Programming

Amdahl's Law

  • Amdahl's Law states that potential program speedup is defined by the fraction of code (P) that can be parallelized:
1 speedup = 1 - P
  • If none of the code can be parallelized, P = 0 and the speedup = 1 (no speedup).
  • If all of the code is parallelized, P = 1 and the speedup is infinite (in theory).
  • If 50% of the code can be parallelized, maximum speedup = 2, meaning the code will run twice as fast.
  • Introducing the number of processors performing the parallel fraction of work, the relationship can be modeled by:
1 speedup = P + S N
  • where P = parallel fraction, N = number of processors and S = serial fraction.
  • It soon becomes obvious that there are limits to the scalability of parallelism. For example:
speedup N P = P = P = P = 10 1, 10, ,
  • "Famous" quote:You can spend a lifetime getting 95% of your code to be parallel, and never achieve better than 20x speedup no matter how many processors you throw at it!
  • However, certain problems demonstrate increased performance by increasing the problem size. For example:
2D Grid Calculations Parallel fraction 85 seconds 85% Serial fraction 15 seconds 15%
  • We can increase the problem size by doubling the grid dimensions and halving the time step. This results in four times the number of grid points and twice the number of time steps. The timings then look like:
2D Grid Calculations Parallel fraction seconds % Serial fraction 15 seconds %
  • Problems that increase the percentage of parallel time with their size are more scalable than problems with a fixed percentage of parallel time.

Complexity

  • In general, parallel applications are  more complex than corresponding serial applications. Not only do you have multiple instruction streams executing at the same time, but you also have data flowing between them.
  • The costs of complexity are measured in programmer time in virtually every aspect of the software development cycle:
    • Design
    • Coding
    • Debugging
    • Tuning
    • Maintenance
  • Adhering to "good" software development practices is essential when developing  parallel applications.

Portability

  • Thanks to standardization in several APIs, such as MPI, OpenMP and POSIX threads, portability issues with parallel programs are not as serious as in years past. However
  • All of the usual portability issues associated with serial programs apply to parallel programs. For example, if you use vendor "enhancements" to Fortran, C or C++, portability will be a problem.
  • Even though standards exist for several APIs, implementations will differ in a number of details, sometimes to the point of requiring code modifications in order to effect portability.
  • Operating systems can play a key role in code portability issues.
  • Hardware architectures are characteristically highly variable and can affect portability.

Resource Requirements

  • The primary intent of parallel programming is to decrease execution wall clock time, however in order to accomplish this, more CPU time is required. For example, a parallel code that runs in 1 hour on 8 processors actually uses 8 hours of CPU time.
  • The amount of memory required can be greater for parallel codes than serial codes, due to the need to replicate data and for overheads associated with parallel support libraries and subsystems.
  • For short running parallel programs, there can actually be a decrease in performance compared to a similar serial implementation. The overhead costs associated with setting up the parallel environment, task creation, communications and task termination can comprise a significant portion of the total execution time for short runs.

Scalability

  • Two types of scaling based on time to solution: strong scaling and weak scaling.
  • Strong scaling (Amdahl):
    • The total problem size stays fixed as more processors are added.
    • Goal is to run the same problem size faster
    • Perfect scaling means problem is solved in 1/P time (compared to serial)
  • Weak scaling (Gustafson):
    • The problem size per processor stays fixed as more processors are added. The total problem size is proportional to the number of processors used.
    • Goal is to run larger problem in same amount of time
    • Perfect scaling means problem Px runs in same time as single processor run
  • The ability of a parallel program's performance to scale is a result of a number of interrelated factors. Simply adding more processors is rarely the answer.
  • The algorithm may have inherent limits to scalability. At some point, adding more resources causes performance to decrease. This is a common situation with many parallel applications.
  • Hardware factors play a significant role in scalability. Examples:
    • Memory-cpu bus bandwidth on an SMP machine
    • Communications network bandwidth
    • Amount of memory available on any given machine or set of machines
    • Processor clock speed
  • Parallel support libraries and subsystems software can limit scalability independent of your application.

Parallel Computer Memory Architectures

Shared Memory

General Characteristics

  • Shared memory parallel computers vary widely, but generally have in common the ability for all processors to access all memory as global address space.
  • Multiple processors can operate independently but share the same memory resources.
  • Changes in a memory location effected by one processor are visible to all other processors.
  • Historically, shared memory machines have been classified as UMA and NUMA, based upon memory access times.

Uniform Memory Access (UMA)

  • Most commonly represented today by Symmetric Multiprocessor (SMP) machines
  • Identical processors
  • Equal access and access times to memory
  • Sometimes called CC-UMA - Cache Coherent UMA. Cache coherent means if one processor updates a location in shared memory, all the other processors know about the update. Cache coherency is accomplished at the hardware level.

Non-Uniform Memory Access (NUMA)

  • Often made by physically linking two or more SMPs
  • One SMP can directly access memory of another SMP
  • Not all processors have equal access time to all memories
  • Memory access across link is slower
  • If cache coherency is maintained, then may also be called CC-NUMA - Cache Coherent NUMA

Advantages

  • Global address space provides a user-friendly programming perspective to memory
  • Data sharing between tasks is both fast and uniform due to the proximity of memory to CPUs

Disadvantages

  • Primary disadvantage is the lack of scalability between memory and CPUs. Adding more CPUs can geometrically increases traffic on the shared memory-CPU path, and for cache coherent systems, geometrically increase traffic associated with cache/memory management.
  • Programmer responsibility for synchronization constructs that ensure "correct" access of global memory.

Distributed Memory

General Characteristics

  • Like shared memory systems, distributed memory systems vary widely but share a common characteristic. Distributed memory systems require a communication network to connect inter-processor memory.
  • Processors have their own local memory. Memory addresses in one processor do not map to another processor, so there is no concept of global address space across all processors.
  • Because each processor has its own local memory, it operates independently. Changes it makes to its local memory have no effect on the memory of other processors. Hence, the concept of cache coherency does not apply.
  • When a processor needs access to data in another processor, it is usually the task of the programmer to explicitly define how and when data is communicated. Synchronization between tasks is likewise the programmer's responsibility.
  • The network "fabric" used for data transfer varies widely, though it can be as simple as Ethernet.

Advantages

  • Memory is scalable with the number of processors. Increase the number of processors and the size of memory increases proportionately.
  • Each processor can rapidly access its own memory without interference and without the overhead incurred with trying to maintain global cache coherency.
  • Cost effectiveness: can use commodity, off-the-shelf processors and networking.

Disadvantages

  • The programmer is responsible for many of the details associated with data communication between processors.
  • It may be difficult to map existing data structures, based on global memory, to this memory organization.
  • Non-uniform memory access times - data residing on a remote node takes longer to access than node local data.

Hybrid Distributed-Shared Memory

General Characteristics

  • The largest and fastest computers in the world today employ both shared and distributed memory architectures.
  • The shared memory component can be a shared memory machine and/or graphics processing units (GPU).
  • The distributed memory component is the networking of multiple shared memory/GPU machines, which know only about their own memory - not the memory on another machine. Therefore, network communications are required to move data from one machine to another.
  • Current trends seem to indicate that this type of memory architecture will continue to prevail and increase at the high end of computing for the foreseeable future.

Advantages and Disadvantages

  • Whatever is common to both shared and distributed memory architectures.
  • Increased scalability is an important advantage
  • Increased programmer complexity is an important disadvantage

Parallel Programming Models

Overview

  • There are several parallel programming models in common use:
    • Shared Memory (without threads)
    • Threads
    • Distributed Memory / Message Passing
    • Data Parallel
    • Hybrid
    • Single Program Multiple Data (SPMD)
    • Multiple Program Multiple Data (MPMD)
  • Parallel programming models exist as an abstraction above hardware and memory architectures.
  • Although it might not seem apparent, these models are NOT specific to a particular type of machine or memory architecture. In fact, any of these models can (theoretically) be implemented on any underlying hardware. Two examples from the past are discussed below.
SHARED memory model on a DISTRIBUTED memory machine

Kendall Square Research (KSR) ALLCACHE approach. Machine memory was physically distributed across networked machines, but appeared to the user as a single shared memory global address space. Generically, this approach is referred to as "virtual shared memory".

DISTRIBUTED memory model on a SHARED memory machine

Message Passing Interface (MPI) on SGI Origin The SGI Origin employed the CC-NUMA type of shared memory architecture, where every task has direct access to global address space spread across all machines. However, the ability to send and receive messages using MPI, as is commonly done over a network of distributed memory machines, was implemented and commonly used.

  • Which model to use? This is often a combination of what is available and personal choice. There is no "best" model, although there certainly are better implementations of some models over others.
  • The following sections describe each of the models mentioned above, and also discuss some of their actual implementations.

Shared Memory Model (without threads)

  • In this programming model, processes/tasks share a common address space, which they read and write to asynchronously.
  • Various mechanisms such as locks / semaphores are used to control access to the shared memory, resolve contentions and to prevent race conditions and deadlocks.
  • This is perhaps the simplest parallel programming model.
  • An advantage of this model from the programmer's point of view is that the notion of data "ownership" is lacking, so there is no need to specify explicitly the communication of data between tasks. All processes see and have equal access to shared memory. Program development can often be simplified.
  • An important disadvantage in terms of performance is that it becomes more difficult to understand and manage data locality:
    • Keeping data local to the process that works on it conserves memory accesses, cache refreshes and bus traffic that occurs when multiple processes use the same data.
    • Unfortunately, controlling data locality is hard to understand and may be beyond the control of the average user.

Implementations:

  • On stand-alone shared memory machines, native operating systems, compilers and/or hardware provide support for shared memory programming. For example, the POSIX standard provides an API for using shared memory, and UNIX provides shared memory segments (shmget, shmat, shmctl, etc).
  • On distributed memory machines, memory is physically distributed across a network of machines, but made global through specialized hardware and software. A variety of SHMEM implementations are available: arenaqq.us

Threads Model

  • This programming model is a type of shared memory programming.
  • In the threads model of parallel programming, a single "heavy weight" process can have multiple "light weight", concurrent execution paths.
  • For example:
    • The main program arenaqq.us is scheduled to run by the native operating system. arenaqq.us loads and acquires all of the necessary system and user resources to run. This is the "heavy weight" process.
    • arenaqq.us performs some serial work, and then creates a number of tasks (threads) that can be scheduled and run by the operating system concurrently.
    • Each thread has local data, but also, shares the entire resources of arenaqq.us. This saves the overhead associated with replicating a program's resources for each thread ("light weight"). Each thread also benefits from a global memory view because it shares the memory space of arenaqq.us.
    • A thread's work may best be described as a subroutine within the main program. Any thread can execute any subroutine at the same time as other threads.
    • Threads communicate with each other through global memory (updating address locations). This requires synchronization constructs to ensure that more than one thread is not updating the same global address at any time.
    • Threads can come and go, but arenaqq.us remains present to provide the necessary shared resources until the application has completed.

Implementations:

  • From a programming perspective, threads implementations commonly comprise:
    • A library of subroutines that are called from within parallel source code
    • A set of compiler directives imbedded in either serial or parallel source code

In both cases, the programmer is responsible for determining the parallelism (although compilers can sometimes help).

  • Threaded implementations are not new in computing. Historically, hardware vendors have implemented their own proprietary versions of threads. These implementations differed substantially from each other making it difficult for programmers to develop portable threaded applications.
  • Unrelated standardization efforts have resulted in two very different implementations of threads: POSIX Threads and OpenMP.
POSIX Threads
  • Specified by the IEEE POSIX c standard (). C Language only.
  • Part of Unix/Linux operating systems
  • Library based
  • Commonly referred to as Pthreads.
  • Very explicit parallelism; requires significant programmer attention to detail.
OpenMP
  •  Industry standard, jointly defined and endorsed by a group of major computer hardware and software vendors, organizations and individuals.
  • Compiler directive based
  • Portable / multi-platform, including Unix and Windows platforms
  • Available in C/C++ and Fortran implementations
  • Can be very easy and simple to use - provides for "incremental parallelism". Can begin with serial code.
  • Other threaded implementations are common, but not discussed here:
    • Microsoft threads
    • Java, Python threads
    • CUDA threads for GPUs

More Information

Distributed Memory / Message Passing Model

  • This model demonstrates the following characteristics:
    • A set of tasks that use their own local memory during computation. Multiple tasks can reside on the same physical machine and/or across an arbitrary number of machines.
    • Tasks exchange data through communications by sending and receiving messages.
    • Data transfer usually requires cooperative operations to be performed by each process. For example, a send operation must have a matching receive operation.

Implementations:

  • From a programming perspective, message passing implementations usually comprise a library of subroutines. Calls to these subroutines are imbedded in source code. The programmer is responsible for determining all parallelism.
  • Historically, a variety of message passing libraries have been available since the s. These implementations differed substantially from each other making it difficult for programmers to develop portable applications.
  • In , the MPI Forum was formed with the primary goal of establishing a standard interface for message passing implementations.
  • Part 1 of the Message Passing Interface (MPI) was released in Part 2 (MPI-2) was released in and MPI-3 in All MPI specifications are available on the web at arenaqq.us
  • MPI is the "de facto" industry standard for message passing, replacing virtually all other message passing implementations used for production work. MPI implementations exist for virtually all popular parallel computing platforms. Not all implementations include everything in MPI-1, MPI-2 or MPI

More Information

Data Parallel Model

  • May also be referred to as the Partitioned Global Address Space (PGAS) model.
  • The data parallel model demonstrates the following characteristics:
    • Address space is treated globally
    • Most of the parallel work focuses on performing operations on a data set. The data set is typically organized into a common structure, such as an array or cube.
    • A set of tasks work collectively on the same data structure, however, each task works on a different partition of the same data structure.
    • Tasks perform the same operation on their partition of work, for example, "add 4 to every array element".
  • On shared memory architectures, all tasks may have access to the data structure through global memory.
  • On distributed memory architectures, the global data structure can be split up logically and/or physically across tasks.

Implementations:

  • Currently, there are several parallel programming implementations in various stages of developments, based on the Data Parallel / PGAS model.
  • Coarray Fortran: a small set of extensions to Fortran 95 for SPMD parallel programming. Compiler dependent. More information: arenaqq.us
  • Unified Parallel C (UPC): an extension to the C programming language for SPMD parallel programming. Compiler dependent. More information: arenaqq.us
  • Global Arrays: provides a shared memory style programming environment in the context of distributed array data structures. Public domain library with C and Fortran77 bindings. More information: arenaqq.us
  • X a PGAS based parallel programming language being developed by IBM at the Thomas J. Watson Research Center. More information: arenaqq.us
  • Chapel: an open source parallel programming language project being led by Cray. More information: arenaqq.us

Hybrid Model

  • A hybrid model combines more than one of the previously described programming models.
  • Currently, a common example of a hybrid model is the combination of the message passing model (MPI) with the threads model (OpenMP).
    • Threads perform computationally intensive kernels using local, on-node data
    • Communications between processes on different nodes occurs over the network using MPI
  • This hybrid model lends itself well to the most popular (currently) hardware environment of clustered multi/many-core machines.
  • Another similar and increasingly popular example of a hybrid model is using MPI with CPU-GPU (Graphics Processing Unit) programming.
    • MPI tasks run on CPUs using local memory and communicating with each other over a network.
    • Computationally intensive kernels are off-loaded to GPUs on-node.
    • Data exchange between node-local memory and GPUs uses CUDA (or something equivalent).
  • Other hybrid models are common:
    • MPI with Pthreads
    • MPI with non-GPU accelerators

SPMD and MPMD

Single Program Multiple Data (SPMD)

  • SPMD is actually a "high level" programming model that can be built upon any combination of the previously mentioned parallel programming models.
  • SINGLE PROGRAM: All tasks execute their copy of the same program simultaneously. This program can be threads, message passing, data parallel or hybrid.
  • MULTIPLE DATA: All tasks may use different data
  • SPMD programs usually have the necessary logic programmed into them to allow different tasks to branch or conditionally execute only those parts of the program they are designed to execute. That is, tasks do not necessarily have to execute the entire program - perhaps only a portion of it.
  • The SPMD model, using message passing or hybrid programming, is probably the most commonly used parallel programming model for multi-node clusters.

Multiple Program Multiple Data (MPMD)

  • Like SPMD, MPMD is actually a "high level" programming model that can be built upon any combination of the previously mentioned parallel programming models.
  • MULTIPLE PROGRAM: Tasks may execute different programs simultaneously. The programs can be threads, message passing, data parallel or hybrid.
  • MULTIPLE DATA: All tasks may use different data
  • MPMD applications are not as common as SPMD applications, but may be better suited for certain types of problems, particularly those that lend themselves better to functional decomposition than domain decomposition (discussed later under Partitioning).

Designing Parallel Programs

Automatic vs. Manual Parallelization

  • Designing and developing parallel programs has characteristically been a very manual process. The programmer is typically responsible for both identifying and actually implementing parallelism.
  • Very often, manually developing parallel codes is a time consuming, complex, error-prone and iterative process.
  • For a number of years now, various tools have been available to assist the programmer with converting serial programs into parallel programs. The most common type of tool used to automatically parallelize a serial program is a parallelizing compiler or pre-processor.
  • A parallelizing compiler generally works in two different ways:
Fully Automatic
  • The compiler analyzes the source code and identifies opportunities for parallelism.
  • The analysis includes identifying inhibitors to parallelism and possibly a cost weighting on whether or not the parallelism would actually improve performance.
  • Loops (do, for) are the most frequent target for automatic parallelization.
Programmer Directed
  • Using "compiler directives" or possibly compiler flags, the programmer explicitly tells the compiler how to parallelize the code.
  • May be able to be used in conjunction with some degree of automatic parallelization also.
  • The most common compiler generated parallelization is done using on-node shared memory and threads (such as OpenMP).
  • If you are beginning with an existing serial code and have time or budget constraints, then automatic parallelization may be the answer. However, there are several important caveats that apply to automatic parallelization:
    • Wrong results may be produced
    • Performance may actually degrade
    • Much less flexible than manual parallelization
    • Limited to a subset (mostly loops) of code
    • May actually not parallelize code if the compiler analysis suggests there are inhibitors or the code is too complex
  • The remainder of this section applies to the manual method of developing parallel codes.

Understand the Problem and the Program

Programs = algorithms + data + (hardware)

  • Undoubtedly, the first step in developing parallel software is to first understand the problem that you wish to solve in parallel. If you are starting with a serial program, this means understanding the existing code also.
  • Before spending time in an attempt to develop a parallel solution for a problem, determine whether or not the problem is one that can actually be parallelized.
    • Example of an easy-to-parallelize problem:

Calculate the potential energy for each of several thousand independent conformations of a molecule. When done, find the minimum energy conformation.

This problem is able to be solved in parallel. Each of the molecular conformations is independently determinable. The calculation of the minimum energy conformation is also a parallelizable problem.

  • Example of a problem and algorithm with little-to-no parallelism:

Calculation of the first 10, members of the Fibonacci series (0,1,1,2,3,5,8,13,21,) by use of the formula:
F(n) = F(n-1) + F(n-2)

The calculation of the F(n) value uses those of both F(n-1) and F(n-2), which must be computed first.

An example of a parallel algorithm for solving this problem (using Binet's formula):

where

  • Identify the program's hotspots:
    • Know where most of the real work is being done. The majority of scientific and technical programs usually accomplish most of their work in a few places.
    • Profilers and performance analysis tools can help here
    • Focus on parallelizing the hotspots and ignore those sections of the program that account for little CPU usage.
  • Identify bottlenecks in the program:
    • Are there areas that are disproportionately slow, or cause parallelizable work to halt or be deferred? For example, I/O is usually something that slows a program down.
    • May be possible to restructure the program or use a different algorithm to reduce or eliminate unnecessary slow areas
  • Identify inhibitors to parallelism. One common class of inhibitor is data dependence, as demonstrated by the Fibonacci sequence above.
  • Investigate other algorithms if possible. This may be the single most important consideration when designing a parallel application.
  • Take advantage of optimized third party parallel software and highly optimized math libraries available from leading vendors (IBM's ESSL, Intel's MKL, AMD's AMCL, etc.).

Partitioning

  • One of the first steps in designing a parallel program is to break the problem into discrete "chunks" of work that can be distributed to multiple tasks. This is known as decomposition or partitioning.
  • There are two basic ways to partition computational work among parallel tasks: domain decomposition and functional decomposition.

Domain Decomposition

  • In this type of partitioning, the data associated with a problem is decomposed. Each parallel task then works on a portion of the data.
  • There are different ways to partition data:

Functional Decomposition

  • In this approach, the focus is on the computation that is to be performed rather than on the data manipulated by the computation. The problem is decomposed according to the work that must be done. Each task then performs a portion of the overall work.
  • Functional decomposition lends itself well to problems that can be split into different tasks. For example:
Ecosystem Modeling

Each program calculates the population of a given group, where each group's growth depends on that of its neighbors. As time progresses, each process calculates its current state, then exchanges information with the neighbor populations. All tasks then progress to calculate the state at the next time step.

Signal Processing

An audio signal data set is passed through four distinct computational filters. Each filter is a separate process. The first segment of data must pass through the first filter before progressing to the second. When it does, the second segment of data passes through the first filter. By the time the fourth segment of data is in the first filter, all four tasks are busy.

Climate Modeling

Each model component can be thought of as a separate task. Arrows represent exchanges of data between components during computation: the atmosphere model generates wind velocity data that are used by the ocean model, the ocean model generates sea surface temperature data that are used by the atmosphere model, and so on.

  • Combining these two types of problem decomposition is common and natural.

Communications

Who Needs Communications?

  • The need for communications between tasks depends upon your problem:
You DON'T need communications
  • Some types of problems can be decomposed and executed in parallel with virtually no need for tasks to share data. These types of problems are often called embarrassingly parallel - little or no communications are required.
  • For example, imagine an image processing operation where every pixel in a black and white image needs to have its color reversed. The image data can easily be distributed to multiple tasks that then act independently of each other to do their portion of the work.
You DO need communications
  • Most parallel applications are not quite so simple, and do require tasks to share data with each other.
  • For example, a 2-D heat diffusion problem requires a task to know the temperatures calculated by the tasks that have neighboring data. Changes to neighboring data has a direct effect on that task's data.

Factors to Consider

There are a number of important factors to consider when designing your program's inter-task communications:

Communication overhead
  • Inter-task communication virtually always implies overhead.
  • Machine cycles and resources that could be used for computation are instead used to package and transmit data.
  • Communications frequently require some type of synchronization between tasks, which can result in tasks spending time "waiting" instead of doing work.
  • Competing communication traffic can saturate the available network bandwidth, further aggravating performance problems.
Latency vs. Bandwidth
  • Latency is the time it takes to send a minimal (0 byte) message from point A to point B. Commonly expressed as microseconds.
  • Bandwidth is the amount of data that can be communicated per unit of time. Commonly expressed as megabytes/sec or gigabytes/sec.
  • Sending many small messages can cause latency to dominate communication overheads. Often it is more efficient to package small messages into a larger message, thus increasing the effective communications bandwidth.
Visibility of communications
  • With the Message Passing Model, communications are explicit and generally quite visible and under the control of the programmer.
  • With the Data Parallel Model, communications often occur transparently to the programmer, particularly on distributed memory architectures. The programmer may not even be able to know exactly how inter-task communications are being accomplished.
Synchronous vs. asynchronous communications
  • Synchronous communications require some type of "handshaking" between tasks that are sharing data. This can be explicitly structured in code by the programmer, or it may happen at a lower level unknown to the programmer.
  • Synchronous communications are often referred to as blocking communications since other work must wait until the communications have completed.
  • Asynchronous communications allow tasks to transfer data independently from one another. For example, task 1 can prepare and send a message to task 2, and then immediately begin doing other work. When task 2 actually receives the data doesn't matter.
  • Asynchronous communications are often referred to as non-blocking communications since other work can be done while the communications are taking place.
  • Interleaving computation with communication is the single greatest benefit for using asynchronous communications.
Scope of communications
  • Knowing which tasks must communicate with each other is critical during the design stage of a parallel code. Both of the two scopings described below can be implemented synchronously or asynchronously.
  • Point-to-point - involves two tasks with one task acting as the sender/producer of data, and the other acting as the receiver/consumer.
  • Collective - involves data sharing between more than two tasks, which are often specified as being members in a common group, or collective. Some common variations (there are more):
Efficiency of communications
  • Oftentimes, the programmer has choices that can affect communications performance. Only a few are mentioned here.
  • Which implementation for a given model should be used? Using the Message Passing Model as an example, one MPI implementation may be faster on a given hardware platform than another.
  • What type of communication operations should be used? As mentioned previously, asynchronous communication operations can improve overall program performance.
  • Network fabric—different platforms use different networks. Some networks perform better than others. Choosing a platform with a faster network may be an option.
Overhead and Complexity
  • Finally, realize that this is only a partial list of things to consider!

Synchronization

  • Managing the sequence of work and the tasks performing it is a critical design consideration for most parallel programs.
  • Can be a significant factor in program performance (or lack of it)
  • Often requires "serialization" of segments of the program.

Types of Synchronization

Barrier
  • Usually implies that all tasks are involved
  • Each task performs its work until it reaches the barrier. It then stops, or "blocks".
  • When the last task reaches the barrier, all tasks are synchronized.
  • What happens from here varies. Often, a serial section of work must be done. In other cases, the tasks are automatically released to continue their work.
Lock / semaphore
  • Can involve any number of tasks
  • Typically used to serialize (protect) access to global data or a section of code. Only one task at a time may use (own) the lock / semaphore / flag.
  • The first task to acquire the lock "sets" it. This task can then safely (serially) access the protected data or code.
  • Other tasks can attempt to acquire the lock but must wait until the task that owns the lock releases it.
  • Can be blocking or non-blocking.
Synchronous communication operations
  • Involves only those tasks executing a communication operation.
  • When a task performs a communication operation, some form of coordination is required with the other task(s) participating in the communication. For example, before a task can perform a send operation, it must first receive an acknowledgment from the receiving task that it is OK to send.
  • Discussed previously in the Communications section.

Data Dependencies

Definition

  • A dependence exists between program statements when the order of statement execution affects the results of the program.
  • A data dependence results from multiple use of the same location(s) in storage by different tasks.
  • Dependencies are important to parallel programming because they are one of the primary inhibitors to parallelism.

Examples

Loop carried data dependence
DO J = MYSTART,MYEND A(J) = A(J-1) * END DO
  • The value of A(J-1) must be computed before the value of A(J), therefore A(J) exhibits a data dependency on A(J-1). Parallelism is inhibited.
  • If Task 2 has A(J) and task 1 has A(J-1), computing the correct value of A(J) necessitates:
    • Distributed memory architecture - task 2 must obtain the value of A(J-1) from task 1 after task 1 finishes its computation
    • Shared memory architecture - task 2 must read A(J-1) after task 1 updates it
Loop independent data dependence
task 1 task 2 X = 2 X = 4 . . . . Y = X**2 Y = X**3
  • As with the previous example, parallelism is inhibited. The value of Y is dependent on:
    • Distributed memory architecture - if or when the value of X is communicated between the tasks.
    • Shared memory architecture - which task last stores the value of X.
  • Although all data dependencies are important to identify when designing parallel programs, loop carried dependencies are particularly important since loops are possibly the most common target of parallelization efforts.

How to Handle Data Dependencies

  • Distributed memory architectures - communicate required data at synchronization points.
  • Shared memory architectures -synchronize read/write operations between tasks.

Load Balancing

  • Load balancing refers to the practice of distributing approximately equal amounts of work among tasks so that all tasks are kept busy all of the time. It can be considered a minimization of task idle time.
  • Load balancing is important to parallel programs for performance reasons. For example, if all tasks are subject to a barrier synchronization point, the slowest task will determine the overall performance.

How to Achieve Load Balance

Equally partition the work each task receives
  • For array/matrix operations where each task performs similar work, evenly distribute the data set among the tasks.
  • For loop iterations where the work done in each iteration is similar, evenly distribute the iterations across the tasks.
  • If a heterogeneous mix of machines with varying performance characteristics are being used, be sure to use some type of performance analysis tool to detect any load imbalances. Adjust work accordingly.
Use dynamic work assignment
  • Certain classes of problems result in load imbalances even if data is evenly distributed among tasks:
Sparse arrays - some tasks will have actual data to work on while others have mostly "zeros".Adaptive grid methods - some tasks may need to refine their mesh while others don't.N-body simulations - particles may migrate across task domains requiring more work for some tasks.
  • When the amount of work each task will perform is intentionally variable, or is unable to be predicted, it may be helpful to use a scheduler-task pool approach. As each task finishes its work, it receives a new piece from the work queue.
  • Ultimately, it may become necessary to design an algorithm which detects and handles load imbalances as they occur dynamically within the code.

Granularity

Computation / Communication Ratio

  • In parallel computing, granularity is a qualitative measure of the ratio of computation to communication.
  • Periods of computation are typically separated from periods of communication by synchronization events.

Fine-grain Parallelism

  • Relatively small amounts of computational work are done between communication events.
  • Low computation to communication ratio.
  • Facilitates load balancing.
  • Implies high communication overhead and less opportunity for performance enhancement.
  • If granularity is too fine it is possible that the overhead required for communications and synchronization between tasks takes longer than the computation.

Coarse-grain Parallelism

  • Relatively large amounts of computational work are done between communication/synchronization events
  • High computation to communication ratio
  • Implies more opportunity for performance increase
  • Harder to load balance efficiently

Which is Best?

  • The most efficient granularity is dependent on the algorithm and the hardware environment in which it runs.
  • In most cases the overhead associated with communications and synchronization is high relative to execution speed so it is advantageous to have coarse granularity.
  • Fine-grain parallelism can help reduce overheads due to load imbalance.

I/O

The Bad News

  • I/O operations are generally regarded as inhibitors to parallelism.
  • I/O operations require orders of magnitude more time than memory operations.
  • Parallel I/O systems may be immature or not available for all platforms.
  • In an environment where all tasks see the same file space, write operations can result in file overwriting.
  • Read operations can be affected by the file server's ability to handle multiple read requests at the same time.
  • I/O that must be conducted over the network (NFS, non-local) can cause severe bottlenecks and even crash file servers.

The Good News

  • The parallel I/O programming interface specification for MPI has been available since as part of MPI Vendor and "free" implementations are now commonly available.
  • A few pointers:
  • Rule #1: Reduce overall I/O as much as possible.
  • If you have access to a parallel file system, use it.
  • Writing large chunks of data rather than small chunks is usually significantly more efficient.
  • Fewer, larger files performs better than many small files.
  • Confine I/O to specific serial portions of the job, and then use parallel communications to distribute data to parallel tasks. For example, Task 1 could read an input file and then communicate required data to other tasks. Likewise, Task 1 could perform write operation after receiving required data from all other tasks.
  • Aggregate I/O operations across tasks - rather than having many tasks perform I/O, have a subset of tasks perform it.

Debugging

  • Debugging parallel codes can be incredibly difficult, particularly as codes scale upwards.
  • The good news is that there are some excellent debuggers available to assist:
    • Threaded - pthreads and OpenMP
    • MPI
    • GPU / accelerator
    • Hybrid
  • Livermore Computing users have access to several parallel debugging tools installed on LC's clusters:
    • TotalView from RogueWave Software
    • DDT from Allinea
    • Inspector from Intel
    • Stack Trace Analysis Tool (STAT) - locally developed at LLNL
  • All of these tools have a learning curve associated with them.
  • For details and getting started information, see:

Performance Analysis and Tuning

Parallel Examples

Array Processing

  • This example demonstrates calculations on 2-dimensional array elements; a function is evaluated on each array element.
  • The computation on each array element is independent from other array elements.
  • The problem is computationally intensive.
  • The serial program calculates one element at a time in sequential order.
  • Serial code could be of the form:
do j = 1,n do i = 1,n a(i,j) = fcn(i,j) end do end do
  • Questions to ask:
    • Is this problem able to be parallelized?
    • How would the problem be partitioned?
    • Are communications needed?
    • Are there any data dependencies?
    • Are there synchronization needs?
    • Will load balancing be a concern?

Parallel Solution 1

  • The calculation of elements is independent of one another - leads to an embarrassingly parallel solution.
  • Arrays elements are evenly distributed so that each process owns a portion of the array (subarray).
    • Distribution scheme is chosen for efficient memory access; e.g. unit stride (stride of 1) through the subarrays. Unit stride maximizes cache/memory usage.
    • Since it is desirable to have unit stride through the subarrays, the choice of a distribution scheme depends on the programming language. See the Block - Cyclic Distributions Diagram for the options.
  • Independent calculation of array elements ensures there is no need for communication or synchronization between tasks.
  • Since the amount of work is evenly distributed across processes, there should not be load balance concerns.
  • After the array is distributed, each task executes the portion of the loop corresponding to the data it owns.
  • For example, both Fortran (column-major) and C (row-major) block distributions are shown:

Column-major:

do j = mystart, myend do i = 1, n a(i,j) = fcn(i,j) end do end do

Row-major:

for i (i = mystart; i < myend; i++) {   for j (j = 0; j < n; j++) {   a(i,j) = fcn(i,j);   } }
  • Notice that only the outer loop variables are different from the serial solution.
One Possible Solution:
  • Implement as a Single Program Multiple Data (SPMD) model - every task executes the same program.
  • Master process initializes array, sends info to worker processes and receives results.
  • Worker process receives info, performs its share of computation and sends results to master.
  • Using the Fortran storage scheme, perform block distribution of the array.
  • Pseudo code solution: red highlights changes for parallelism.
find out if I am MASTER or WORKER if I am MASTER initialize the array send each WORKER info on part of array it owns send each WORKER its portion of initial array receive from each WORKER results else if I am WORKER receive from MASTER info on part of array I own receive from MASTER my portion of initial array # calculate my portion of array do j = my first column,my last column do i = 1,n a(i,j) = fcn(i,j) end do end do send MASTER resultsendif
Example Programs

Parallel Solution 2: Pool of Tasks

  • The previous array solution demonstrated static load balancing:
    • Each task has a fixed amount of work to do
    • May be significant idle time for faster or more lightly loaded processors - slowest tasks determines overall performance.
  • Static load balancing is not usually a major concern if all tasks are performing the same amount of work on identical machines.
  • If you have a load balance problem (some tasks work faster than others), you may benefit by using a "pool of tasks" scheme.
Pool of Tasks Scheme
  • Two processes are employed

Master Process:

  • Holds pool of tasks for worker processes to do
  • Sends worker a task when requested
  • Collects results from workers

Worker Process: repeatedly does the following

  • Gets task from master process
  • Performs computation
  • Sends results to master
  • Worker processes do not know before runtime which portion of array they will handle or how many tasks they will perform.
  • Dynamic load balancing occurs at run time: the faster tasks will get more work to do.
  • Pseudo code solution: red highlights changes for parallelism.
find out if I am MASTER or WORKER if I am MASTER do until no more jobs if request send to WORKER next job else receive results from WORKER end do else if I am WORKER do until no more jobs request job from MASTER receive from MASTER next job calculate array element: a(i,j) = fcn(i,j) send results to MASTER end do endif
Discussion
  • In the above pool of tasks example, each task calculated an individual array element as a job. The computation to communication ratio is finely granular.
  • Finely granular solutions incur more communication overhead in order to reduce task idle time.
  • A more optimal solution might be to distribute more work with each job. The "right" amount of work is problem dependent.

PI Calculation

  • The value of PI can be calculated in various ways. Consider the Monte Carlo method of approximating PI:
    • Inscribe a circle with radius r in a square with side length of 2r
    • The area of the circle is Πr2 and the area of the square is 4r2
    • The ratio of the area of the circle to the area of the square is:
      Πr2 / 4r2 = Π / 4
    • If you randomly generate N points inside the square, approximately
      N * Π / 4 of those points (M) should fall inside the circle.
    • Π is then approximated as:
      N * Π / 4 = M
      Π / 4 = M / N
      Π = 4 * M / N
    • Note that increasing the number of points generated improves the approximation.
  • Serial pseudo code for this procedure:
npoints = circle_count = 0 do j = 1,npoints generate 2 random numbers between 0 and 1 xcoordinate = random1 ycoordinate = random2 if (xcoordinate, ycoordinate) inside circle then circle_count = circle_count + 1 end do PI = *circle_count/npoints
  • The problem is computationally intensive—most of the time is spent executing the loop
  • Questions to ask:
    • Is this problem able to be parallelized?
    • How would the problem be partitioned?
    • Are communications needed?
    • Are there any data dependencies?
    • Are there synchronization needs?
    • Will load balancing be a concern?

Parallel Solution

  • Another problem that's easy to parallelize:
    • All point calculations are independent; no data dependencies
    • Work can be evenly divided; no load balance concerns
    • No need for communication or synchronization between tasks
  • Parallel strategy:
    • Divide the loop into equal portions that can be executed by the pool of tasks
    • Each task independently performs its work
    • A SPMD model is used
    • One task acts as the master to collect results and compute the value of PI
  • Pseudo code solution: red highlights changes for parallelism.
npoints = circle_count = 0 p = number of tasks num = npoints/p find out if I am MASTER or WORKER do j = 1,num generate 2 random numbers between 0 and 1 xcoordinate = random1 ycoordinate = random2 if (xcoordinate, ycoordinate) inside circle then circle_count = circle_count + 1 end do if I am MASTER receive from WORKERS their circle_counts compute PI (use MASTER and WORKER calculations) else if I am WORKER send to MASTER circle_count endif

Example Programs

Simple Heat Equation

  • Most problems in parallel computing require communication among the tasks. A number of common problems require communication with "neighbor" tasks.
  • The 2-D heat equation describes the temperature change over time, given initial temperature distribution and boundary conditions.
  • A finite differencing scheme is employed to solve the heat equation numerically on a square region.
    • The elements of a 2-dimensional array represent the temperature at points on the square.
    • The initial temperature is zero on the boundaries and high in the middle.
    • The boundary temperature is held at zero.
    • A time stepping algorithm is used.
  • The calculation of an element is dependent upon neighbor element values:
  • A serial program would contain code like:
do iy = 2, ny - 1 do ix = 2, nx - 1 u2(ix, iy) = u1(ix, iy) + cx * (u1(ix+1,iy) + u1(ix-1,iy) - 2.*u1(ix,iy)) + cy * (u1(ix,iy+1) + u1(ix,iy-1) - 2.*u1(ix,iy)) end do end do
  • Questions to ask:
    • Is this problem able to be parallelized?
    • How would the problem be partitioned?
    • Are communications needed?
    • Are there any data dependencies?
    • Are there synchronization needs?
    • Will load balancing be a concern?

Parallel Solution

  • This problem is more challenging, since there are data dependencies, which require communications and synchronization.
  • The entire array is partitioned and distributed as subarrays to all tasks. Each task owns an equal portion of the total array.
  • Because the amount of work is equal, load balancing should not be a concern
  • Determine data dependencies:
  • Implement as an SPMD model:
    • Master process sends initial info to workers, and then waits to collect results from all workers
    • Worker processes calculate solution within specified number of time steps, communicating as necessary with neighbor processes
  • Pseudo code solution: red highlights changes for parallelism.
find out if I am MASTER or WORKER if I am MASTER initialize array send each WORKER starting info and subarray receive results from each WORKER else if I am WORKER receive from MASTER starting info and subarray # Perform time steps do t = 1, nsteps update time send neighbors my border info receive from neighbors their border info update my portion of solution array end do send MASTER results endif

Example Programs

1-D Wave Equation

  • In this example, the amplitude along a uniform, vibrating string is calculated after a specified amount of time has elapsed.
  • The calculation involves:
    • the amplitude on the y axis
    • i as the position index along the x axis
    • node points imposed along the string
    • update of the amplitude at discrete time steps.
  • The equation to be solved is the one-dimensional wave equation:
A(i,t+1) = ( * A(i,t)) - A(i,t-1) + (c * (A(i-1,t) - ( * A(i,t)) + A(i+1,t)))

where c is a constant

  • Note that amplitude will depend on previous timesteps (t, t-1) and neighboring points (i-1, i+1).
  • Questions to ask:
    • Is this problem able to be parallelized?
    • How would the problem be partitioned?
    • Are communications needed?
    • Are there any data dependencies?
    • Are there synchronization needs?
    • Will load balancing be a concern?

1-D Wave Equation Parallel Solution

  • This is another example of a problem involving data dependencies. A parallel solution will involve communications and synchronization.
  • The entire amplitude array is partitioned and distributed as subarrays to all tasks. Each task owns an equal portion of the total array.
  • Load balancing: all points require equal work, so the points should be divided equally
  • A block decomposition would have the work partitioned into the number of tasks as chunks, allowing each task to own mostly contiguous data points.
  • Communication need only occur on data borders. The larger the block size the less the communication.
  • Implement as an SPMD model:
    • Master process sends initial info to workers, and then waits to collect results from all workers
    • Worker processes calculate solution within specified number of time steps, communicating as necessary with neighbor processes
  • Pseudo code solution: red highlights changes for parallelism.
find out number of tasks and task identities #Identify left and right neighbors left_neighbor = mytaskid - 1 right_neighbor = mytaskid +1 if mytaskid = first then left_neigbor = last if mytaskid = last then right_neighbor = first find out if I am MASTER or WORKER if I am MASTER initialize array send each WORKER starting info and subarray else if I am WORKER` receive starting info and subarray from MASTER endif #Perform time steps #In this example the master participates in calculations do t = 1, nsteps send left endpoint to left neighbor receive left endpoint from right neighbor send right endpoint to right neighbor receive right endpoint from left neighbor #Update points along line do i = 1, npoints newval(i) = ( * values(i)) - oldval(i) + (sqtau * (values(i-1) - ( * values(i)) + values(i+1))) end do end do #Collect results and write to file if I am MASTER receive results from each WORKER write results to file else if I am WORKER send results to MASTER endif

Example Programs

This completes the tutorial.

Please complete the online evaluation form.

References and More Information

  • Author: Blaise Barney, Livermore Computing (retired), Donald Frederick, LLNL
  • Contact: hpc-tutorials@arenaqq.us
  • A search on the Web for "parallel programming" or "parallel computing" will yield a wide variety of information.
  • Recommended reading - Parallel Programming:
    • "Designing and Building Parallel Programs", Ian Foster - from the early days of parallel computing, but still illuminating.
      arenaqq.us~itf/dbpp/
    • "Introduction to Parallel Computing", Ananth Grama, Anshul Gupta, George Karypis, Vipin Kumar.
      arenaqq.us~karypis/parbook/
    • University of Oregon - Intel Parallel Computing Curriculum
      arenaqq.us
    • UC Berkeley CS, Applications of Paralele Computing, Prof. Jim Demmel, UCB -- arenaqq.us
    • Udacity CS Intro to Parallel Programming - arenaqq.us
    • "Programming on Parallel Machines", Norm Matloff, UC Davis: arenaqq.us~matloff//PLN/ParProcBookSpdf
    • Cornell Virtual Workshop: Parallel Programming Concepts and High-Performance Computing - arenaqq.us
    • CS, Applications of Parallel Computers, Spring , Prof. Jim Demmel, UCB - arenaqq.us
    • Introduction to High Performance Scientific Computing", Victor Eijkhout, TACC - arenaqq.us~eijkhout/istc/arenaqq.us
    • COMP Advanced Parallel Computing (Fall, ), SDSU, Prof. Mary Thomas - arenaqq.us~mthomas/f

    • Georg Hager's SC '20 Tutorial on Node-Level Performance Tuning - arenaqq.us
  • Recommended reading - Linux
  • Photos/Graphics have been created by the authors, created by other LLNL employees, obtained from non-copyrighted, government or public domain (such as arenaqq.us) sources, or used with the permission of authors from other presentations and web pages.
  • History: These materials evolved from the following sources:
    • Tutorials developed by the Cornell University Center for Advanced Computing (CAC) available at arenaqq.us
    • Tutorials developed by the Maui High Performance Computing Center’s “SP Parallel Programming Workshop” (no longer available).
Источник: [arenaqq.us]

Can: Speed Up My PC 2010 crack serial keygen

Speed Up My PC 2010 crack serial keygen
OPENSHOT VIDEO EDITOR 2021 CRACK WITH SERIAL KEY LATEST FREE DOWNLOAD
NCH PIXILLION IMAGE CONVERTER PUS V2.44 CRACK SERIAL KEYGEN
Corel Draw X8 Crack With Keygen Full Version Torrent Download
GRIDINSOFT ANTI-MALWARE 4.2.0 WITH CRACK FREE DOWNLOAD [2021]

You can watch a thematic video

speed up my pc key

Speed Up My PC 2010 crack serial keygen - are

Take the frustration out of updating drivers!

Fixes Driver Problems - FAST!

Hardware Driver Stop Working? Get Fast Fixes For Your Driver Problems. DriverDoc will get your computer up and running in no time. Click here to start your driver scan. Download Now »

Driver Update Wizard!

Use the Driver Update Wizard with simple One Click Updates for your old drivers. DriverDoc is the right prescription to get your driver problems fixed fast, so your computer runs like new. Fix it now! Download Now »


Smart Scan - 1 Click Repair

Do You Get Cryptic Error Messages? DriverDoc Smart Scan your system and fix the error messages that are driving you crazy. Even custom computer systems are no problem with DriverDoc. Click to start your driver scan. Download Now »

Live Driver Tech Support

Get support for over 16,, drivers inside DriverDoc. Call us for Live Driver Tech Support included in your driver update software. Click here for your driver scan and get help today!
Download Now »


Источник: [arenaqq.us]

Introduction to Parallel Computing Tutorial

Table of Contents

  1. Abstract
    1. Parallel Computing Overview
      1. What is Parallel Computing?
      2. Why Use Parallel Computing?
      3. Who is Using Parallel Computing?
    2. Concepts and Terminology
      1. von Neumann Computer Architecture
      2. Flynn's Taxonomy
      3. Parallel Computing Terminology
      4. Potential Benefits, Limits and Costs of Parallel Programming
    3. Parallel Computer Memory Architectures
      1. Shared Memory
      2. Distributed Memory
      3. Hybrid Distributed-Shared Memory
    4. Parallel Programming Models
      1. Parallel Programming Models Overview
      2. Shared Memory Model
      3. Threads Model
      4. Distributed Memory / Message Passing Model
      5. Data Parallel Model
      6. Hybrid Model
      7. SPMD and MPMP
    5. Designing Parallel Programs
      1. Automatic vs. Manual Parallelization
      2. Understand the Problem and the Program
      3. Partitioning
      4. Communications
      5. Synchronization
      6. Data Dependencies
      7. Load Balancing
      8. Granularity
      9. I/O
      10. Debugging
      11. Performance Analysis and Tuning
    6. Parallel Examples
      1. Array Processing
      2. PI Calculation
      3. Simple Heat Equation
      4. 1-D Wave Equation
    7. References and More Information

Abstract

This is the first tutorial in the "Livermore Computing Getting Started" workshop. It is intended to provide only a brief overview of the extensive and broad topic of Parallel Computing, as a lead-in for the tutorials that follow it. As such, it covers just the very basics of parallel computing, and is intended for someone who is just becoming acquainted with the subject and who is planning to attend one or more of the other tutorials in this workshop. It is not intended to cover Parallel Programming in depth, as this would require significantly more time. The tutorial begins with a discussion on parallel computing - what it is and how it's used, followed by a discussion on concepts and terminology associated with parallel computing. The topics of parallel memory architectures and programming models are then explored. These topics are followed by a series of practical discussions on a number of the complex issues related to designing and running parallel programs. The tutorial concludes with several examples of how to parallelize several simple problems. References are included for further self-study. 

Overview

What is Parallel Computing?

Serial Computing

Traditionally, software has been written for serial computation:

  • A problem is broken into a discrete series of instructions
  • Instructions are executed sequentially one after another
  • Executed on a single processor
  • Only one instruction may execute at any moment in time

For example:

Parallel Computing

In the simplest sense, parallel computing is the simultaneous use of multiple compute resources to solve a computational problem:

  • A problem is broken into discrete parts that can be solved concurrently
  • Each part is further broken down to a series of instructions
  • Instructions from each part execute simultaneously on different processors
  • An overall control/coordination mechanism is employed

For example:

  • The computational problem should be able to:
    • Be broken apart into discrete pieces of work that can be solved simultaneously;
    • Execute multiple program instructions at any moment in time;
    • Be solved in less time with multiple compute resources than with a single compute resource.
  • The compute resources are typically:
    • A single computer with multiple processors/cores
    • An arbitrary number of such computers connected by a network

Parallel Computers

  • Virtually all stand-alone computers today are parallel from a hardware perspective:
    • Multiple functional units (L1 cache, L2 cache, branch, prefetch, decode, floating-point, graphics processing (GPU), integer, etc.)
    • Multiple execution units/cores
    • Multiple hardware threads
IBM BG/Q Compute Chip with 18 cores (PU) and 16 L2 Cache units (L2)
  • Networks connect multiple stand-alone computers (nodes) to make larger parallel computer clusters.
  • For example, the schematic below shows a typical LLNL parallel computer cluster:
    • Each compute node is a multi-processor parallel computer in itself
    • Multiple compute nodes are networked together with an Infiniband network
    • Special purpose nodes, also multi-processor, are used for other purposes
  • The majority of the world's large parallel computers (supercomputers) are clusters of hardware produced by a handful of (mostly) well known vendors.

Source: Toporg

Why Use Parallel Computing?

The Real World is Massively Complex

  • In the natural world, many complex, interrelated events are happening at the same time, yet within a temporal sequence.
  • Compared to serial computing, parallel computing is much better suited for modeling, simulating and understanding complex, real world phenomena.
  • For example, imagine modeling these serially:

Main Reasons for Using Parallel Programming

SAVE TIME AND/OR MONEY
  • In theory, throwing more resources at a task will shorten its time to completion, with potential cost savings.
  • Parallel computers can be built from cheap, commodity components.
SOLVE LARGER / MORE COMPLEX PROBLEMS
  • Many problems are so large and/or complex that it is impractical or impossible to solve them using a serial program, especially given limited computer memory.
  • Example: "Grand Challenge Problems" (arenaqq.us) requiring petaflops and petabytes of computing resources.
  • Example: Web search engines/databases processing millions of transactions every second
PROVIDE CONCURRENCY
  • A single compute resource can only do one thing at a time. Multiple compute resources can do many things simultaneously.
  • Example: Collaborative Networks provide a global venue where people from around the world can meet and conduct work "virtually".
TAKE ADVANTAGE OF NON-LOCAL RESOURCES
  • Using compute resources on a wide area network, or even the Internet when local compute resources are scarce or insufficient.
  • Example: SETI@home (arenaqq.us) has over million users in nearly every country in the world. (May, ).
MAKE BETTER USE OF UNDERLYING PARALLEL HARDWARE
  • Modern computers, even laptops, are parallel in architecture with multiple processors/cores.
  • Parallel software is specifically intended for parallel hardware with multiple cores, threads, etc.
  • In most cases, serial programs run on modern computers "waste" potential computing power.

The Future

  • During the past 20+ years, the trends indicated by ever faster networks, distributed systems, and multi-processor computer architectures (even at the desktop level) clearly show that parallelism is the future of computing.
  • In this same time period, there has been a greater than ,x increase in supercomputer performance, with no end currently in sight.
  • The race is already on for Exascale Computing - we are entering Exascale era
Source: Toporg

Who is Using Parallel Computing?

Science and Engineering

  • Historically, parallel computing has been considered to be "the high end of computing", and has been used to model difficult problems in many areas of science and engineering:
  • Atmosphere, Earth, Environment
  • Physics - applied, nuclear, particle, condensed matter, high pressure, fusion, photonics
  • Bioscience, Biotechnology, Genetics
  • Chemistry, Molecular Sciences
  • Geology, Seismology
  • Mechanical Engineering - from prosthetics to spacecraft
  • Electrical Engineering, Circuit Design, Microelectronics
  • Computer Science, Mathematics
  • Defense, Weapons

Industrial and Commercial

  • Today, commercial applications provide an equal or greater driving force in the development of faster computers. These applications require the processing of large amounts of data in sophisticated ways. For example:
  • "Big Data", databases, data mining
  • Artificial Intelligence (AI)
  • Oil exploration
  • Web search engines, web based business services
  • Medical imaging and diagnosis
  • Pharmaceutical design
  • Financial and economic modeling
  • Management of national and multi-national corporations
  • Advanced graphics and virtual reality, particularly in the entertainment industry
  • Networked video and multi-media technologies
  • Collaborative work environments

Global Applications

  • Parallel computing is now being used extensively around the world, in a wide variety of applications.

Source: Toporg


Source: Toporg

Concepts and Terminology

von Neumann Architecture

John von Neumann circa s
(Source: LANL archives)
  • Named after the Hungarian mathematician John von Neumann who first authored the general requirements for an electronic computer in his papers.
  • Also known as "stored-program computer" - both program instructions and data are kept in electronic memory. Differs from earlier computers which were programmed through "hard wiring".
  • Since then, virtually all computers have followed this basic design:
  • Comprised of four main components:
  1. Memory
  2. Control Unit
  3. Arithmetic Logic Unit
  4. Input/Output
  • Read/write, random access memory is used to store both program instructions and data
  • Program instructions are coded data which tell the computer to do something
  • Data is simply information to be used by the program
  • Control unit fetches instructions/data from memory, decodes the instructions and then sequentially coordinates operations to accomplish the programmed task.
  • Arithmetic Unit performs basic arithmetic operations
  • Input/Output is the interface to the human operator
  • More info on his other remarkable accomplishments: arenaqq.us
  • Parallel computers still follow this basic design, just multiplied in units. The basic, fundamental architecture remains the same.

Flynn's Classical Taxonomy

  • There are a number of different ways to classify parallel computers. Examples are available in the references.
  • One of the more widely used classifications, in use since , is called Flynn's Taxonomy.
  • Flynn's taxonomy distinguishes multi-processor computer architectures according to how they can be classified along the two independent dimensions of Instruction Stream and Data Stream. Each of these dimensions can have only one of two possible states: Single or Multiple.
  • The matrix below defines the 4 possible classifications according to Flynn:

Single Instruction, Single Data (SISD)

  • A serial (non-parallel) computer
  • Single Instruction: Only one instruction stream is being acted on by the CPU during any one clock cycle
  • Single Data: Only one data stream is being used as input during any one clock cycle
  • Deterministic execution
  • This is the oldest type of computer
  • Examples: older generation mainframes, minicomputers, workstations and single processor/core PCs.

Single Instruction, Multiple Data (SIMD)

  • A type of parallel computer
  • Single Instruction: All processing units execute the same instruction at any given clock cycle
  • Multiple Data: Each processing unit can operate on a different data element
  • Best suited for specialized problems characterized by a high degree of regularity, such as graphics/image processing.
  • Synchronous (lockstep) and deterministic execution
  • Two varieties: Processor Arrays and Vector Pipelines
  • Examples:
    • Processor Arrays: Thinking Machines CM-2, MasPar MP-1 & MP-2, ILLIAC IV
    • Vector Pipelines: IBM , Cray X-MP, Y-MP & C90, Fujitsu VP, NEC SX-2, Hitachi S, ETA10
  • Most modern computers, particularly those with graphics processor units (GPUs) employ SIMD instructions and execution units.

Multiple Instruction, Single Data (MISD)

  • A type of parallel computer
  • Multiple Instruction: Each processing unit operates on the data independently via separate instruction streams.
  • Single Data: A single data stream is fed into multiple processing units.
  • Few (if any) actual examples of this class of parallel computer have ever existed.
  • Some conceivable uses might be:
    • multiple frequency filters operating on a single signal stream
    • multiple cryptography algorithms attempting to crack a single coded message.

Multiple Instruction, Multiple Data (MIMD)

  • A type of parallel computer
  • Multiple Instruction: Every processor may be executing a different instruction stream
  • Multiple Data: Every processor may be working with a different data stream
  • Execution can be synchronous or asynchronous, deterministic or non-deterministic
  • Currently, the most common type of parallel computer - most modern supercomputers fall into this category.
  • Examples: most current supercomputers, networked parallel computer clusters and "grids", multi-processor SMP computers, multi-core PCs.
  • Note many MIMD architectures also include SIMD execution sub-components

General Parallel Computing Terminology

  • Like everything else, parallel computing has its own jargon. Some of the more commonly used terms associated with parallel computing are listed below. Most of these will be discussed in more detail later.
CPU

Contemporary CPUs consist of one or more cores - a distinct execution unit with its own instruction stream. Cores with a CPU may be organized into one or more sockets - each socket

with its own distinct memory . When a CPU consists of two or more sockets, usually hardware infrastructure supports memory sharing across sockets.

Node

A standalone "computer in a box". Usually comprised of multiple CPUs/processors/cores, memory, network interfaces, etc. Nodes are networked together to comprise a supercomputer.

Task

A logically discrete section of computational work. A task is typically a program or program-like set of instructions that is executed by a processor. A parallel program consists of multiple tasks running on multiple processors.

Pipelining

Breaking a task into steps performed by different processor units, with inputs streaming through, much like an assembly line; a type of parallel computing.

Shared Memory

Describes a computer architecture where all processors have direct access to common physical memory. In a programming sense, it describes a model where parallel tasks all have the same "picture" of memory and can directly address and access the same logical memory locations regardless of where the physical memory actually exists.

Symmetric Multi-Processor (SMP)

Shared memory hardware architecture where multiple processors share a single address space and have equal access to all resources - memory, disk, etc.

Distributed Memory

In hardware, refers to network based memory access for physical memory that is not common. As a programming model, tasks can only logically "see" local machine memory and must use communications to access memory on other machines where other tasks are executing.

Communications

Parallel tasks typically need to exchange data. There are several ways this can be accomplished, such as through a shared memory bus or over a network.

Synchronization

The coordination of parallel tasks in real time, very often associated with communications.

Synchronization usually involves waiting by at least one task, and can therefore cause a parallel application's wall clock execution time to increase.

Computational Granularity

In parallel computing, granularity is a quantitative or qualitative measure of the ratio of computation to communication.

  • Coarse: relatively large amounts of computational work are done between communication events
  • Fine: relatively small amounts of computational work are done between communication events
Observed Speedup

Observed speedup of a code which has been parallelized, defined as:

wall-clock time of serial execution wall-clock time of parallel execution

One of the simplest and most widely used indicators for a parallel program's performance.

Parallel Overhead

Required execution time that is unique to parallel tasks, as opposed to that for doing useful work. Parallel overhead can include factors such as:

  • Task start-up time
  • Synchronizations
  • Data communications
  • Software overhead imposed by parallel languages, libraries, operating system, etc.
  • Task termination time
Massively Parallel

Refers to the hardware that comprises a given parallel system - having many processing elements. The meaning of "many" keeps increasing, but currently, the largest parallel computers are comprised of processing elements numbering in the hundreds of thousands to millions.

Embarrassingly (IDEALY) Parallel

Solving many similar, but independent tasks simultaneously; little to no need for coordination between the tasks.

Scalability

Refers to a parallel system's (hardware and/or software) ability to demonstrate a proportionate increase in parallel speedup with the addition of more resources. Factors that contribute to scalability include:

  • Hardware - particularly memory-cpu bandwidths and network communication properties
  • Application algorithm
  • Parallel overhead related
  • Characteristics of your specific application

Potential Benefits, Limits and Costs of Parallel Programming

Amdahl's Law

  • Amdahl's Law states that potential program speedup is defined by the fraction of code (P) that can be parallelized:
1 speedup = 1 - P
  • If none of the code can be parallelized, P = 0 and the speedup = 1 (no speedup).
  • If all of the code is parallelized, P = 1 and the speedup is infinite (in theory).
  • If 50% of the code can be parallelized, maximum speedup = 2, meaning the code will run twice as fast.
  • Introducing the number of processors performing the parallel fraction of work, the relationship can be modeled by:
1 speedup = P + S N
  • where P = parallel fraction, N = number of processors and S = serial fraction.
  • It soon becomes obvious that there are limits to the scalability of parallelism. For example:
speedup N P = P = P = P = 10 1, 10, ,
  • "Famous" quote:You can spend a lifetime getting 95% of your code to be parallel, and never achieve better than 20x speedup no matter how many processors you throw at it!
  • However, certain problems demonstrate increased performance by increasing the problem size. For example:
2D Grid Calculations Parallel fraction 85 seconds 85% Serial fraction 15 seconds 15%
  • We can increase the problem size by doubling the grid dimensions and halving the time step. This results in four times the number of grid points and twice the number of time steps. The timings then look like:
2D Grid Calculations Parallel fraction seconds % Serial fraction 15 seconds %
  • Problems that increase the percentage of parallel time with their size are more scalable than problems with a fixed percentage of parallel time.

Complexity

  • In general, parallel applications are  more complex than corresponding serial applications. Not only do you have multiple instruction streams executing at the same time, but you also have data flowing between them.
  • The costs of complexity are measured in programmer time in virtually every aspect of the software development cycle:
    • Design
    • Coding
    • Debugging
    • Tuning
    • Maintenance
  • Adhering to "good" software development practices is essential when developing  parallel applications.

Portability

  • Thanks to standardization in several APIs, such as MPI, OpenMP and POSIX threads, portability issues with parallel programs are not as serious as in years past. However
  • All of the usual portability issues associated with serial programs apply to parallel programs. For example, if you use vendor "enhancements" to Fortran, C or C++, portability will be a problem.
  • Even though standards exist for several APIs, implementations will differ in a number of details, sometimes to the point of requiring code modifications in order to effect portability.
  • Operating systems can play a key role in code portability issues.
  • Hardware architectures are characteristically highly variable and can affect portability.

Resource Requirements

  • The primary intent of parallel programming is to decrease execution wall clock time, however in order to accomplish this, more CPU time is required. For example, a parallel code that runs in 1 hour on 8 processors actually uses 8 hours of CPU time.
  • The amount of memory required can be greater for parallel codes than serial codes, due to the need to replicate data and for overheads associated with parallel support libraries and subsystems.
  • For short running parallel programs, there can actually be a decrease in performance compared to a similar serial implementation. The overhead costs associated with setting up the parallel environment, task creation, communications and task termination can comprise a significant portion of the total execution time for short runs.

Scalability

  • Two types of scaling based on time to solution: strong scaling and weak scaling.
  • Strong scaling (Amdahl):
    • The total problem size stays fixed as more processors are added.
    • Goal is to run the same problem size faster
    • Perfect scaling means problem is solved in 1/P time (compared to serial)
  • Weak scaling (Gustafson):
    • The problem size per processor stays fixed as more processors are added. The total problem size is proportional to the number of processors used.
    • Goal is to run larger problem in same amount of time
    • Perfect scaling means problem Px runs in same time as single processor run
  • The ability of a parallel program's performance to scale is a result of a number of interrelated factors. Simply adding more processors is rarely the answer.
  • The algorithm may have inherent limits to scalability. At some point, adding more resources causes performance to decrease. This is a common situation with many parallel applications.
  • Hardware factors play a significant role in scalability. Examples:
    • Memory-cpu bus bandwidth on an SMP machine
    • Communications network bandwidth
    • Amount of memory available on any given machine or set of machines
    • Processor clock speed
  • Parallel support libraries and subsystems software can limit scalability independent of your application.

Parallel Computer Memory Architectures

Shared Memory

General Characteristics

  • Shared memory parallel computers vary widely, but generally have in common the ability for all processors to access all memory as global address space.
  • Multiple processors can operate independently but share the same memory resources.
  • Changes in a memory location effected by one processor are visible to all other processors.
  • Historically, shared memory machines have been classified as UMA and NUMA, based upon memory access times.

Uniform Memory Access (UMA)

  • Most commonly represented today by Symmetric Multiprocessor (SMP) machines
  • Identical processors
  • Equal access and access times to memory
  • Sometimes called CC-UMA - Cache Coherent UMA. Cache coherent means if one processor updates a location in shared memory, all the other processors know about the update. Cache coherency is accomplished at the hardware level.

Non-Uniform Memory Access (NUMA)

  • Often made by physically linking two or more SMPs
  • One SMP can directly access memory of another SMP
  • Not all processors have equal access time to all memories
  • Memory access across link is slower
  • If cache coherency is maintained, then may also be called CC-NUMA - Cache Coherent NUMA

Advantages

  • Global address space provides a user-friendly programming perspective to memory
  • Data sharing between tasks is both fast and uniform due to the proximity of memory to CPUs

Disadvantages

  • Primary disadvantage is the lack of scalability between memory and CPUs. Adding more CPUs can geometrically increases traffic on the shared memory-CPU path, and for cache coherent systems, geometrically increase traffic associated with cache/memory management.
  • Programmer responsibility for synchronization constructs that ensure "correct" access of global memory.

Distributed Memory

General Characteristics

  • Like shared memory systems, distributed memory systems vary widely but share a common characteristic. Distributed memory systems require a communication network to connect inter-processor memory.
  • Processors have their own local memory. Memory addresses in one processor do not map to another processor, so there is no concept of global address space across all processors.
  • Because each processor has its own local memory, it operates independently. Changes it makes to its local memory have no effect on the memory of other processors. Hence, the concept of cache coherency does not apply.
  • When a processor needs access to data in another processor, it is usually the task of the programmer to explicitly define how and when data is communicated. Synchronization between tasks is likewise the programmer's responsibility.
  • The network "fabric" used for data transfer varies widely, though it can be as simple as Ethernet.

Advantages

  • Memory is scalable with the number of processors. Increase the number of processors and the size of memory increases proportionately.
  • Each processor can rapidly access its own memory without interference and without the overhead incurred with trying to maintain global cache coherency.
  • Cost effectiveness: can use commodity, off-the-shelf processors and networking.

Disadvantages

  • The programmer is responsible for many of the details associated with data communication between processors.
  • It may be difficult to map existing data structures, based on global memory, to this memory organization.
  • Non-uniform memory access times - data residing on a remote node takes longer to access than node local data.

Hybrid Distributed-Shared Memory

General Characteristics

  • The largest and fastest computers in the world today employ both shared and distributed memory architectures.
  • The shared memory component can be a shared memory machine and/or graphics processing units (GPU).
  • The distributed memory component is the networking of multiple shared memory/GPU machines, which know only about their own memory - not the memory on another machine. Therefore, network communications are required to move data from one machine to another.
  • Current trends seem to indicate that this type of memory architecture will continue to prevail and increase at the high end of computing for the foreseeable future.

Advantages and Disadvantages

  • Whatever is common to both shared and distributed memory architectures.
  • Increased scalability is an important advantage
  • Increased programmer complexity is an important disadvantage

Parallel Programming Models

Overview

  • There are several parallel programming models in common use:
    • Shared Memory (without threads)
    • Threads
    • Distributed Memory / Message Passing
    • Data Parallel
    • Hybrid
    • Single Program Multiple Data (SPMD)
    • Multiple Program Multiple Data (MPMD)
  • Parallel programming models exist as an abstraction above hardware and memory architectures.
  • Although it might not seem apparent, these models are NOT specific to a particular type of machine or memory architecture. In fact, any of these models can (theoretically) be implemented on any underlying hardware. Two examples from the past are discussed below.
SHARED memory model on a DISTRIBUTED memory machine

Kendall Square Research (KSR) ALLCACHE approach. Machine memory was physically distributed across networked machines, but appeared to the user as a single shared memory global address space. Generically, this approach is referred to as "virtual shared memory".

DISTRIBUTED memory model on a SHARED memory machine

Message Passing Interface (MPI) on SGI Origin The SGI Origin employed the CC-NUMA type of shared memory architecture, where every task has direct access to global address space spread across all machines. However, the ability to send and receive messages using MPI, as is commonly done over a network of distributed memory machines, was implemented and commonly used.

  • Which model to use? This is often a combination of what is available and personal choice. There is no "best" model, although there certainly are better implementations of some models over others.
  • The following sections describe each of the models mentioned above, and also discuss some of their actual implementations.

Shared Memory Model (without threads)

  • In this programming model, processes/tasks share a common address space, which they read and write to asynchronously.
  • Various mechanisms such as locks / semaphores are used to control access to the shared memory, resolve contentions and to prevent race conditions and deadlocks.
  • This is perhaps the simplest parallel programming model.
  • An advantage of this model from the programmer's point of view is that the notion of data "ownership" is lacking, so there is no need to specify explicitly the communication of data between tasks. All processes see and have equal access to shared memory. Program development can often be simplified.
  • An important disadvantage in terms of performance is that it becomes more difficult to understand and manage data locality:
    • Keeping data local to the process that works on it conserves memory accesses, cache refreshes and bus traffic that occurs when multiple processes use the same data.
    • Unfortunately, controlling data locality is hard to understand and may be beyond the control of the average user.

Implementations:

  • On stand-alone shared memory machines, native operating systems, compilers and/or hardware provide support for shared memory programming. For example, the POSIX standard provides an API for using shared memory, and UNIX provides shared memory segments (shmget, shmat, shmctl, etc).
  • On distributed memory machines, memory is physically distributed across a network of machines, but made global through specialized hardware and software. A variety of SHMEM implementations are available: arenaqq.us

Threads Model

  • This programming model is a type of shared memory programming.
  • In the threads model of parallel programming, a single "heavy weight" process can have multiple "light weight", concurrent execution paths.
  • For example:
    • The main program arenaqq.us is scheduled to run by the native operating system. arenaqq.us loads and acquires all of the necessary system and user resources to run. This is the "heavy weight" process.
    • arenaqq.us performs some serial work, and then creates a number of tasks (threads) that can be scheduled and run by the operating system concurrently.
    • Each thread has local data, but also, shares the entire resources of arenaqq.us. This saves the overhead associated with replicating a program's resources for each thread ("light weight"). Each thread also benefits from a global memory view because it shares the memory space of arenaqq.us.
    • A thread's work may best be described as a subroutine within the main program. Any thread can execute any subroutine at the same time as other threads.
    • Threads communicate with each other through global memory (updating address locations). This requires synchronization constructs to ensure that more than one thread is not updating the same global address at any time.
    • Threads can come and go, but arenaqq.us remains present to provide the necessary shared resources until the application has completed.

Implementations:

  • From a programming perspective, threads implementations commonly comprise:
    • A library of subroutines that are called from within parallel source code
    • A set of compiler directives imbedded in either serial or parallel source code

In both cases, the programmer is responsible for determining the parallelism (although compilers can sometimes help).

  • Threaded implementations are not new in computing. Historically, hardware vendors have implemented their own proprietary versions of threads. These implementations differed substantially from each other making it difficult for programmers to develop portable threaded applications.
  • Unrelated standardization efforts have resulted in two very different implementations of threads: POSIX Threads and OpenMP.
POSIX Threads
  • Specified by the IEEE POSIX c standard (). C Language only.
  • Part of Unix/Linux operating systems
  • Library based
  • Commonly referred to as Pthreads.
  • Very explicit parallelism; requires significant programmer attention to detail.
OpenMP
  •  Industry standard, jointly defined and endorsed by a group of major computer hardware and software vendors, organizations and individuals.
  • Compiler directive based
  • Portable / multi-platform, including Unix and Windows platforms
  • Available in C/C++ and Fortran implementations
  • Can be very easy and simple to use - provides for "incremental parallelism". Can begin with serial code.
  • Other threaded implementations are common, but not discussed here:
    • Microsoft threads
    • Java, Python threads
    • CUDA threads for GPUs

More Information

Distributed Memory / Message Passing Model

  • This model demonstrates the following characteristics:
    • A set of tasks that use their own local memory during computation. Multiple tasks can reside on the same physical machine and/or across an arbitrary number of machines.
    • Tasks exchange data through communications by sending and receiving messages.
    • Data transfer usually requires cooperative operations to be performed by each process. For example, a send operation must have a matching receive operation.

Implementations:

  • From a programming perspective, message passing implementations usually comprise a library of subroutines. Calls to these subroutines are imbedded in source code. The programmer is responsible for determining all parallelism.
  • Historically, a variety of message passing libraries have been available since the s. These implementations differed substantially from each other making it difficult for programmers to develop portable applications.
  • In , the MPI Forum was formed with the primary goal of establishing a standard interface for message passing implementations.
  • Part 1 of the Message Passing Interface (MPI) was released in Part 2 (MPI-2) was released in and MPI-3 in All MPI specifications are available on the web at arenaqq.us
  • MPI is the "de facto" industry standard for message passing, replacing virtually all other message passing implementations used for production work. MPI implementations exist for virtually all popular parallel computing platforms. Not all implementations include everything in MPI-1, MPI-2 or MPI

More Information

Data Parallel Model

  • May also be referred to as the Partitioned Global Address Space (PGAS) model.
  • The data parallel model demonstrates the following characteristics:
    • Address space is treated globally
    • Most of the parallel work focuses on performing operations on a data set. The data set is typically organized into a common structure, such as an array or cube.
    • A set of tasks work collectively on the same data structure, however, each task works on a different partition of the same data structure.
    • Tasks perform the same operation on their partition of work, for example, "add 4 to every array element".
  • On shared memory architectures, all tasks may have access to the data structure through global memory.
  • On distributed memory architectures, the global data structure can be split up logically and/or physically across tasks.

Implementations:

  • Currently, there are several parallel programming implementations in various stages of developments, based on the Data Parallel / PGAS model.
  • Coarray Fortran: a small set of extensions to Fortran 95 for SPMD parallel programming. Compiler dependent. More information: arenaqq.us
  • Unified Parallel C (UPC): an extension to the C programming language for SPMD parallel programming. Compiler dependent. More information: arenaqq.us
  • Global Arrays: provides a shared memory style programming environment in the context of distributed array data structures. Public domain library with C and Fortran77 bindings. More information: arenaqq.us
  • X a PGAS based parallel programming language being developed by IBM at the Thomas J. Watson Research Center. More information: arenaqq.us
  • Chapel: an open source parallel programming language project being led by Cray. More information: arenaqq.us

Hybrid Model

  • A hybrid model combines more than one of the previously described programming models.
  • Currently, a common example of a hybrid model is the combination of the message passing model (MPI) with the threads model (OpenMP).
    • Threads perform computationally intensive kernels using local, on-node data
    • Communications between processes on different nodes occurs over the network using MPI
  • This hybrid model lends itself well to the most popular (currently) hardware environment of clustered multi/many-core machines.
  • Another similar and increasingly popular example of a hybrid model is using MPI with CPU-GPU (Graphics Processing Unit) programming.
    • MPI tasks run on CPUs using local memory and communicating with each other over a network.
    • Computationally intensive kernels are off-loaded to GPUs on-node.
    • Data exchange between node-local memory and GPUs uses CUDA (or something equivalent).
  • Other hybrid models are common:
    • MPI with Pthreads
    • MPI with non-GPU accelerators

SPMD and MPMD

Single Program Multiple Data (SPMD)

  • SPMD is actually a "high level" programming model that can be built upon any combination of the previously mentioned parallel programming models.
  • SINGLE PROGRAM: All tasks execute their copy of the same program simultaneously. This program can be threads, message passing, data parallel or hybrid.
  • MULTIPLE DATA: All tasks may use different data
  • SPMD programs usually have the necessary logic programmed into them to allow different tasks to branch or conditionally execute only those parts of the program they are designed to execute. That is, tasks do not necessarily have to execute the entire program - perhaps only a portion of it.
  • The SPMD model, using message passing or hybrid programming, is probably the most commonly used parallel programming model for multi-node clusters.

Multiple Program Multiple Data (MPMD)

  • Like SPMD, MPMD is actually a "high level" programming model that can be built upon any combination of the previously mentioned parallel programming models.
  • MULTIPLE PROGRAM: Tasks may execute different programs simultaneously. The programs can be threads, message passing, data parallel or hybrid.
  • MULTIPLE DATA: All tasks may use different data
  • MPMD applications are not as common as SPMD applications, but may be better suited for certain types of problems, particularly those that lend themselves better to functional decomposition than domain decomposition (discussed later under Partitioning).

Designing Parallel Programs

Automatic vs. Manual Parallelization

  • Designing and developing parallel programs has characteristically been a very manual process. The programmer is typically responsible for both identifying and actually implementing parallelism.
  • Very often, manually developing parallel codes is a time consuming, complex, error-prone and iterative process.
  • For a number of years now, various tools have been available to assist the programmer with converting serial programs into parallel programs. The most common type of tool used to automatically parallelize a serial program is a parallelizing compiler or pre-processor.
  • A parallelizing compiler generally works in two different ways:
Fully Automatic
  • The compiler analyzes the source code and identifies opportunities for parallelism.
  • The analysis includes identifying inhibitors to parallelism and possibly a cost weighting on whether or not the parallelism would actually improve performance.
  • Loops (do, for) are the most frequent target for automatic parallelization.
Programmer Directed
  • Using "compiler directives" or possibly compiler flags, the programmer explicitly tells the compiler how to parallelize the code.
  • May be able to be used in conjunction with some degree of automatic parallelization also.
  • The most common compiler generated parallelization is done using on-node shared memory and threads (such as OpenMP).
  • If you are beginning with an existing serial code and have time or budget constraints, then automatic parallelization may be the answer. However, there are several important caveats that apply to automatic parallelization:
    • Wrong results may be produced
    • Performance may actually degrade
    • Much less flexible than manual parallelization
    • Limited to a subset (mostly loops) of code
    • May actually not parallelize code if the compiler analysis suggests there are inhibitors or the code is too complex
  • The remainder of this section applies to the manual method of developing parallel codes.

Understand the Problem and the Program

Programs = algorithms + data + (hardware)

  • Undoubtedly, the first step in developing parallel software is to first understand the problem that you wish to solve in parallel. If you are starting with a serial program, this means understanding the existing code also.
  • Before spending time in an attempt to develop a parallel solution for a problem, determine whether or not the problem is one that can actually be parallelized.
    • Example of an easy-to-parallelize problem:

Calculate the potential energy for each of several thousand independent conformations of a molecule. When done, find the minimum energy conformation.

This problem is able to be solved in parallel. Each of the molecular conformations is independently determinable. The calculation of the minimum energy conformation is also a parallelizable problem.

  • Example of a problem and algorithm with little-to-no parallelism:

Calculation of the first 10, members of the Fibonacci series (0,1,1,2,3,5,8,13,21,) by use of the formula:
F(n) = F(n-1) + F(n-2)

The calculation of the F(n) value uses those of both F(n-1) and F(n-2), which must be computed first.

An example of a parallel algorithm for solving this problem (using Binet's formula):

where

  • Identify the program's hotspots:
    • Know where most of the real work is being done. The majority of scientific and technical programs usually accomplish most of their work in a few places.
    • Profilers and performance analysis tools can help here
    • Focus on parallelizing the hotspots and ignore those sections of the program that account for little CPU usage.
  • Identify bottlenecks in the program:
    • Are there areas that are disproportionately slow, or cause parallelizable work to halt or be deferred? For example, I/O is usually something that slows a program down.
    • May be possible to restructure the program or use a different algorithm to reduce or eliminate unnecessary slow areas
  • Identify inhibitors to parallelism. One common class of inhibitor is data dependence, as demonstrated by the Fibonacci sequence above.
  • Investigate other algorithms if possible. This may be the single most important consideration when designing a parallel application.
  • Take advantage of optimized third party parallel software and highly optimized math libraries available from leading vendors (IBM's ESSL, Intel's MKL, AMD's AMCL, etc.).

Partitioning

  • One of the first steps in designing a parallel program is to break the problem into discrete "chunks" of work that can be distributed to multiple tasks. This is known as decomposition or partitioning.
  • There are two basic ways to partition computational work among parallel tasks: domain decomposition and functional decomposition.

Domain Decomposition

  • In this type of partitioning, the data associated with a problem is decomposed. Each parallel task then works on a portion of the data.
  • There are different ways to partition data:

Functional Decomposition

  • In this approach, the focus is on the computation that is to be performed rather than on the data manipulated by the computation. The problem is decomposed according to the work that must be done. Each task then performs a portion of the overall work.
  • Functional decomposition lends itself well to problems that can be split into different tasks. For example:
Ecosystem Modeling

Each program calculates the population of a given group, where each group's growth depends on that of its neighbors. As time progresses, each process calculates its current state, then exchanges information with the neighbor populations. All tasks then progress to calculate the state at the next time step.

Signal Processing

An audio signal data set is passed through four distinct computational filters. Each filter is a separate process. The first segment of data must pass through the first filter before progressing to the second. When it does, the second segment of data passes through the first filter. By the time the fourth segment of data is in the first filter, all four tasks are busy.

Climate Modeling

Each model component can be thought of as a separate task. Arrows represent exchanges of data between components during computation: the atmosphere model generates wind velocity data that are used by the ocean model, the ocean model generates sea surface temperature data that are used by the atmosphere model, and so on.

  • Combining these two types of problem decomposition is common and natural.

Communications

Who Needs Communications?

  • The need for communications between tasks depends upon your problem:
You DON'T need communications
  • Some types of problems can be decomposed and executed in parallel with virtually no need for tasks to share data. These types of problems are often called embarrassingly parallel - little or no communications are required.
  • For example, imagine an image processing operation where every pixel in a black and white image needs to have its color reversed. The image data can easily be distributed to multiple tasks that then act independently of each other to do their portion of the work.
You DO need communications
  • Most parallel applications are not quite so simple, and do require tasks to share data with each other.
  • For example, a 2-D heat diffusion problem requires a task to know the temperatures calculated by the tasks that have neighboring data. Changes to neighboring data has a direct effect on that task's data.

Factors to Consider

There are a number of important factors to consider when designing your program's inter-task communications:

Communication overhead
  • Inter-task communication virtually always implies overhead.
  • Machine cycles and resources that could be used for computation are instead used to package and transmit data.
  • Communications frequently require some type of synchronization between tasks, which can result in tasks spending time "waiting" instead of doing work.
  • Competing communication traffic can saturate the available network bandwidth, further aggravating performance problems.
Latency vs. Bandwidth
  • Latency is the time it takes to send a minimal (0 byte) message from point A to point B. Commonly expressed as microseconds.
  • Bandwidth is the amount of data that can be communicated per unit of time. Commonly expressed as megabytes/sec or gigabytes/sec.
  • Sending many small messages can cause latency to dominate communication overheads. Often it is more efficient to package small messages into a larger message, thus increasing the effective communications bandwidth.
Visibility of communications
  • With the Message Passing Model, communications are explicit and generally quite visible and under the control of the programmer.
  • With the Data Parallel Model, communications often occur transparently to the programmer, particularly on distributed memory architectures. The programmer may not even be able to know exactly how inter-task communications are being accomplished.
Synchronous vs. asynchronous communications
  • Synchronous communications require some type of "handshaking" between tasks that are sharing data. This can be explicitly structured in code by the programmer, or it may happen at a lower level unknown to the programmer.
  • Synchronous communications are often referred to as blocking communications since other work must wait until the communications have completed.
  • Asynchronous communications allow tasks to transfer data independently from one another. For example, task 1 can prepare and send a message to task 2, and then immediately begin doing other work. When task 2 actually receives the data doesn't matter.
  • Asynchronous communications are often referred to as non-blocking communications since other work can be done while the communications are taking place.
  • Interleaving computation with communication is the single greatest benefit for using asynchronous communications.
Scope of communications
  • Knowing which tasks must communicate with each other is critical during the design stage of a parallel code. Both of the two scopings described below can be implemented synchronously or asynchronously.
  • Point-to-point - involves two tasks with one task acting as the sender/producer of data, and the other acting as the receiver/consumer.
  • Collective - involves data sharing between more than two tasks, which are often specified as being members in a common group, or collective. Some common variations (there are more):
Efficiency of communications
  • Oftentimes, the programmer has choices that can affect communications performance. Only a few are mentioned here.
  • Which implementation for a given model should be used? Using the Message Passing Model as an example, one MPI implementation may be faster on a given hardware platform than another.
  • What type of communication operations should be used? As mentioned previously, asynchronous communication operations can improve overall program performance.
  • Network fabric—different platforms use different networks. Some networks perform better than others. Choosing a platform with a faster network may be an option.
Overhead and Complexity
  • Finally, realize that this is only a partial list of things to consider!

Synchronization

  • Managing the sequence of work and the tasks performing it is a critical design consideration for most parallel programs.
  • Can be a significant factor in program performance (or lack of it)
  • Often requires "serialization" of segments of the program.

Types of Synchronization

Barrier
  • Usually implies that all tasks are involved
  • Each task performs its work until it reaches the barrier. It then stops, or "blocks".
  • When the last task reaches the barrier, all tasks are synchronized.
  • What happens from here varies. Often, a serial section of work must be done. In other cases, the tasks are automatically released to continue their work.
Lock / semaphore
  • Can involve any number of tasks
  • Typically used to serialize (protect) access to global data or a section of code. Only one task at a time may use (own) the lock / semaphore / flag.
  • The first task to acquire the lock "sets" it. This task can then safely (serially) access the protected data or code.
  • Other tasks can attempt to acquire the lock but must wait until the task that owns the lock releases it.
  • Can be blocking or non-blocking.
Synchronous communication operations
  • Involves only those tasks executing a communication operation.
  • When a task performs a communication operation, some form of coordination is required with the other task(s) participating in the communication. For example, before a task can perform a send operation, it must first receive an acknowledgment from the receiving task that it is OK to send.
  • Discussed previously in the Communications section.

Data Dependencies

Definition

  • A dependence exists between program statements when the order of statement execution affects the results of the program.
  • A data dependence results from multiple use of the same location(s) in storage by different tasks.
  • Dependencies are important to parallel programming because they are one of the primary inhibitors to parallelism.

Examples

Loop carried data dependence
DO J = MYSTART,MYEND A(J) = A(J-1) * END DO
  • The value of A(J-1) must be computed before the value of A(J), therefore A(J) exhibits a data dependency on A(J-1). Parallelism is inhibited.
  • If Task 2 has A(J) and task 1 has A(J-1), computing the correct value of A(J) necessitates:
    • Distributed memory architecture - task 2 must obtain the value of A(J-1) from task 1 after task 1 finishes its computation
    • Shared memory architecture - task 2 must read A(J-1) after task 1 updates it
Loop independent data dependence
task 1 task 2 X = 2 X = 4 . . . . Y = X**2 Y = X**3
  • As with the previous example, parallelism is inhibited. The value of Y is dependent on:
    • Distributed memory architecture - if or when the value of X is communicated between the tasks.
    • Shared memory architecture - which task last stores the value of X.
  • Although all data dependencies are important to identify when designing parallel programs, loop carried dependencies are particularly important since loops are possibly the most common target of parallelization efforts.

How to Handle Data Dependencies

  • Distributed memory architectures - communicate required data at synchronization points.
  • Shared memory architectures -synchronize read/write operations between tasks.

Load Balancing

  • Load balancing refers to the practice of distributing approximately equal amounts of work among tasks so that all tasks are kept busy all of the time. It can be considered a minimization of task idle time.
  • Load balancing is important to parallel programs for performance reasons. For example, if all tasks are subject to a barrier synchronization point, the slowest task will determine the overall performance.

How to Achieve Load Balance

Equally partition the work each task receives
  • For array/matrix operations where each task performs similar work, evenly distribute the data set among the tasks.
  • For loop iterations where the work done in each iteration is similar, evenly distribute the iterations across the tasks.
  • If a heterogeneous mix of machines with varying performance characteristics are being used, be sure to use some type of performance analysis tool to detect any load imbalances. Adjust work accordingly.
Use dynamic work assignment
  • Certain classes of problems result in load imbalances even if data is evenly distributed among tasks:
Sparse arrays - some tasks will have actual data to work on while others have mostly "zeros".Adaptive grid methods - some tasks may need to refine their mesh while others don't.N-body simulations - particles may migrate across task domains requiring more work for some tasks.
  • When the amount of work each task will perform is intentionally variable, or is unable to be predicted, it may be helpful to use a scheduler-task pool approach. As each task finishes its work, it receives a new piece from the work queue.
  • Ultimately, it may become necessary to design an algorithm which detects and handles load imbalances as they occur dynamically within the code.

Granularity

Computation / Communication Ratio

  • In parallel computing, granularity is a qualitative measure of the ratio of computation to communication.
  • Periods of computation are typically separated from periods of communication by synchronization events.

Fine-grain Parallelism

  • Relatively small amounts of computational work are done between communication events.
  • Low computation to communication ratio.
  • Facilitates load balancing.
  • Implies high communication overhead and less opportunity for performance enhancement.
  • If granularity is too fine it is possible that the overhead required for communications and synchronization between tasks takes longer than the computation.

Coarse-grain Parallelism

  • Relatively large amounts of computational work are done between communication/synchronization events
  • High computation to communication ratio
  • Implies more opportunity for performance increase
  • Harder to load balance efficiently

Which is Best?

  • The most efficient granularity is dependent on the algorithm and the hardware environment in which it runs.
  • In most cases the overhead associated with communications and synchronization is high relative to execution speed so it is advantageous to have coarse granularity.
  • Fine-grain parallelism can help reduce overheads due to load imbalance.

I/O

The Bad News

  • I/O operations are generally regarded as inhibitors to parallelism.
  • I/O operations require orders of magnitude more time than memory operations.
  • Parallel I/O systems may be immature or not available for all platforms.
  • In an environment where all tasks see the same file space, write operations can result in file overwriting.
  • Read operations can be affected by the file server's ability to handle multiple read requests at the same time.
  • I/O that must be conducted over the network (NFS, non-local) can cause severe bottlenecks and even crash file servers.

The Good News

  • The parallel I/O programming interface specification for MPI has been available since as part of MPI Vendor and "free" implementations are now commonly available.
  • A few pointers:
  • Rule #1: Reduce overall I/O as much as possible.
  • If you have access to a parallel file system, use it.
  • Writing large chunks of data rather than small chunks is usually significantly more efficient.
  • Fewer, larger files performs better than many small files.
  • Confine I/O to specific serial portions of the job, and then use parallel communications to distribute data to parallel tasks. For example, Task 1 could read an input file and then communicate required data to other tasks. Likewise, Task 1 could perform write operation after receiving required data from all other tasks.
  • Aggregate I/O operations across tasks - rather than having many tasks perform I/O, have a subset of tasks perform it.

Debugging

  • Debugging parallel codes can be incredibly difficult, particularly as codes scale upwards.
  • The good news is that there are some excellent debuggers available to assist:
    • Threaded - pthreads and OpenMP
    • MPI
    • GPU / accelerator
    • Hybrid
  • Livermore Computing users have access to several parallel debugging tools installed on LC's clusters:
    • TotalView from RogueWave Software
    • DDT from Allinea
    • Inspector from Intel
    • Stack Trace Analysis Tool (STAT) - locally developed at LLNL
  • All of these tools have a learning curve associated with them.
  • For details and getting started information, see:

Performance Analysis and Tuning

Parallel Examples

Array Processing

  • This example demonstrates calculations on 2-dimensional array elements; a function is evaluated on each array element.
  • The computation on each array element is independent from other array elements.
  • The problem is computationally intensive.
  • The serial program calculates one element at a time in sequential order.
  • Serial code could be of the form:
do j = 1,n do i = 1,n a(i,j) = fcn(i,j) end do end do
  • Questions to ask:
    • Is this problem able to be parallelized?
    • How would the problem be partitioned?
    • Are communications needed?
    • Are there any data dependencies?
    • Are there synchronization needs?
    • Will load balancing be a concern?

Parallel Solution 1

  • The calculation of elements is independent of one another - leads to an embarrassingly parallel solution.
  • Arrays elements are evenly distributed so that each process owns a portion of the array (subarray).
    • Distribution scheme is chosen for efficient memory access; e.g. unit stride (stride of 1) through the subarrays. Unit stride maximizes cache/memory usage.
    • Since it is desirable to have unit stride through the subarrays, the choice of a distribution scheme depends on the programming language. See the Block - Cyclic Distributions Diagram for the options.
  • Independent calculation of array elements ensures there is no need for communication or synchronization between tasks.
  • Since the amount of work is evenly distributed across processes, there should not be load balance concerns.
  • After the array is distributed, each task executes the portion of the loop corresponding to the data it owns.
  • For example, both Fortran (column-major) and C (row-major) block distributions are shown:

Column-major:

do j = mystart, myend do i = 1, n a(i,j) = fcn(i,j) end do end do

Row-major:

for i (i = mystart; i < myend; i++) {   for j (j = 0; j < n; j++) {   a(i,j) = fcn(i,j);   } }
  • Notice that only the outer loop variables are different from the serial solution.
One Possible Solution:
  • Implement as a Single Program Multiple Data (SPMD) model - every task executes the same program.
  • Master process initializes array, sends info to worker processes and receives results.
  • Worker process receives info, performs its share of computation and sends results to master.
  • Using the Fortran storage scheme, perform block distribution of the array.
  • Pseudo code solution: red highlights changes for parallelism.
find out if I am MASTER or WORKER if I am MASTER initialize the array send each WORKER info on part of array it owns send each WORKER its portion of initial array receive from each WORKER results else if I am WORKER receive from MASTER info on part of array I own receive from MASTER my portion of initial array # calculate my portion of array do j = my first column,my last column do i = 1,n a(i,j) = fcn(i,j) end do end do send MASTER resultsendif
Example Programs

Parallel Solution 2: Pool of Tasks

  • The previous array solution demonstrated static load balancing:
    • Each task has a fixed amount of work to do
    • May be significant idle time for faster or more lightly loaded processors - slowest tasks determines overall performance.
  • Static load balancing is not usually a major concern if all tasks are performing the same amount of work on identical machines.
  • If you have a load balance problem (some tasks work faster than others), you may benefit by using a "pool of tasks" scheme.
Pool of Tasks Scheme
  • Two processes are employed

Master Process:

  • Holds pool of tasks for worker processes to do
  • Sends worker a task when requested
  • Collects results from workers

Worker Process: repeatedly does the following

  • Gets task from master process
  • Performs computation
  • Sends results to master
  • Worker processes do not know before runtime which portion of array they will handle or how many tasks they will perform.
  • Dynamic load balancing occurs at run time: the faster tasks will get more work to do.
  • Pseudo code solution: red highlights changes for parallelism.
find out if I am MASTER or WORKER if I am MASTER do until no more jobs if request send to WORKER next job else receive results from WORKER end do else if I am WORKER do until no more jobs request job from MASTER receive from MASTER next job calculate array element: a(i,j) = fcn(i,j) send results to MASTER end do endif
Discussion
  • In the above pool of tasks example, each task calculated an individual array element as a job. The computation to communication ratio is finely granular.
  • Finely granular solutions incur more communication overhead in order to reduce task idle time.
  • A more optimal solution might be to distribute more work with each job. The "right" amount of work is problem dependent.

PI Calculation

  • The value of PI can be calculated in various ways. Consider the Monte Carlo method of approximating PI:
    • Inscribe a circle with radius r in a square with side length of 2r
    • The area of the circle is Πr2 and the area of the square is 4r2
    • The ratio of the area of the circle to the area of the square is:
      Πr2 / 4r2 = Π / 4
    • If you randomly generate N points inside the square, approximately
      N * Π / 4 of those points (M) should fall inside the circle.
    • Π is then approximated as:
      N * Π / 4 = M
      Π / 4 = M / N
      Π = 4 * M / N
    • Note that increasing the number of points generated improves the approximation.
  • Serial pseudo code for this procedure:
npoints = circle_count = 0 do j = 1,npoints generate 2 random numbers between 0 and 1 xcoordinate = random1 ycoordinate = random2 if (xcoordinate, ycoordinate) inside circle then circle_count = circle_count + 1 end do PI = *circle_count/npoints
  • The problem is computationally intensive—most of the time is spent executing the loop
  • Questions to ask:
    • Is this problem able to be parallelized?
    • How would the problem be partitioned?
    • Are communications needed?
    • Are there any data dependencies?
    • Are there synchronization needs?
    • Will load balancing be a concern?

Parallel Solution

  • Another problem that's easy to parallelize:
    • All point calculations are independent; no data dependencies
    • Work can be evenly divided; no load balance concerns
    • No need for communication or synchronization between tasks
  • Parallel strategy:
    • Divide the loop into equal portions that can be executed by the pool of tasks
    • Each task independently performs its work
    • A SPMD model is used
    • One task acts as the master to collect results and compute the value of PI
  • Pseudo code solution: red highlights changes for parallelism.
npoints = circle_count = 0 p = number of tasks num = npoints/p find out if I am MASTER or WORKER do j = 1,num generate 2 random numbers between 0 and 1 xcoordinate = random1 ycoordinate = random2 if (xcoordinate, ycoordinate) inside circle then circle_count = circle_count + 1 end do if I am MASTER receive from WORKERS their circle_counts compute PI (use MASTER and WORKER calculations) else if I am WORKER send to MASTER circle_count endif

Example Programs

Simple Heat Equation

  • Most problems in parallel computing require communication among the tasks. A number of common problems require communication with "neighbor" tasks.
  • The 2-D heat equation describes the temperature change over time, given initial temperature distribution and boundary conditions.
  • A finite differencing scheme is employed to solve the heat equation numerically on a square region.
    • The elements of a 2-dimensional array represent the temperature at points on the square.
    • The initial temperature is zero on the boundaries and high in the middle.
    • The boundary temperature is held at zero.
    • A time stepping algorithm is used.
  • The calculation of an element is dependent upon neighbor element values:
  • A serial program would contain code like:
do iy = 2, ny - 1 do ix = 2, nx - 1 u2(ix, iy) = u1(ix, iy) + cx * (u1(ix+1,iy) + u1(ix-1,iy) - 2.*u1(ix,iy)) + cy * (u1(ix,iy+1) + u1(ix,iy-1) - 2.*u1(ix,iy)) end do end do
  • Questions to ask:
    • Is this problem able to be parallelized?
    • How would the problem be partitioned?
    • Are communications needed?
    • Are there any data dependencies?
    • Are there synchronization needs?
    • Will load balancing be a concern?

Parallel Solution

  • This problem is more challenging, since there are data dependencies, which require communications and synchronization.
  • The entire array is partitioned and distributed as subarrays to all tasks. Each task owns an equal portion of the total array.
  • Because the amount of work is equal, load balancing should not be a concern
  • Determine data dependencies:
  • Implement as an SPMD model:
    • Master process sends initial info to workers, and then waits to collect results from all workers
    • Worker processes calculate solution within specified number of time steps, communicating as necessary with neighbor processes
  • Pseudo code solution: red highlights changes for parallelism.
find out if I am MASTER or WORKER if I am MASTER initialize array send each WORKER starting info and subarray receive results from each WORKER else if I am WORKER receive from MASTER starting info and subarray # Perform time steps do t = 1, nsteps update time send neighbors my border info receive from neighbors their border info update my portion of solution array end do send MASTER results endif

Example Programs

1-D Wave Equation

  • In this example, the amplitude along a uniform, vibrating string is calculated after a specified amount of time has elapsed.
  • The calculation involves:
    • the amplitude on the y axis
    • i as the position index along the x axis
    • node points imposed along the string
    • update of the amplitude at discrete time steps.
  • The equation to be solved is the one-dimensional wave equation:
A(i,t+1) = ( * A(i,t)) - A(i,t-1) + (c * (A(i-1,t) - ( * A(i,t)) + A(i+1,t)))

where c is a constant

  • Note that amplitude will depend on previous timesteps (t, t-1) and neighboring points (i-1, i+1).
  • Questions to ask:
    • Is this problem able to be parallelized?
    • How would the problem be partitioned?
    • Are communications needed?
    • Are there any data dependencies?
    • Are there synchronization needs?
    • Will load balancing be a concern?

1-D Wave Equation Parallel Solution

  • This is another example of a problem involving data dependencies. A parallel solution will involve communications and synchronization.
  • The entire amplitude array is partitioned and distributed as subarrays to all tasks. Each task owns an equal portion of the total array.
  • Load balancing: all points require equal work, so the points should be divided equally
  • A block decomposition would have the work partitioned into the number of tasks as chunks, allowing each task to own mostly contiguous data points.
  • Communication need only occur on data borders. The larger the block size the less the communication.
  • Implement as an SPMD model:
    • Master process sends initial info to workers, and then waits to collect results from all workers
    • Worker processes calculate solution within specified number of time steps, communicating as necessary with neighbor processes
  • Pseudo code solution: red highlights changes for parallelism.
find out number of tasks and task identities #Identify left and right neighbors left_neighbor = mytaskid - 1 right_neighbor = mytaskid +1 if mytaskid = first then left_neigbor = last if mytaskid = last then right_neighbor = first find out if I am MASTER or WORKER if I am MASTER initialize array send each WORKER starting info and subarray else if I am WORKER` receive starting info and subarray from MASTER endif #Perform time steps #In this example the master participates in calculations do t = 1, nsteps send left endpoint to left neighbor receive left endpoint from right neighbor send right endpoint to right neighbor receive right endpoint from left neighbor #Update points along line do i = 1, npoints newval(i) = ( * values(i)) - oldval(i) + (sqtau * (values(i-1) - ( * values(i)) + values(i+1))) end do end do #Collect results and write to file if I am MASTER receive results from each WORKER write results to file else if I am WORKER send results to MASTER endif

Example Programs

This completes the tutorial.

Please complete the online evaluation form.

References and More Information

  • Author: Blaise Barney, Livermore Computing (retired), Donald Frederick, LLNL
  • Contact: hpc-tutorials@arenaqq.us
  • A search on the Web for "parallel programming" or "parallel computing" will yield a wide variety of information.
  • Recommended reading - Parallel Programming:
    • "Designing and Building Parallel Programs", Ian Foster - from the early days of parallel computing, but still illuminating.
      arenaqq.us~itf/dbpp/
    • "Introduction to Parallel Computing", Ananth Grama, Anshul Gupta, George Karypis, Vipin Kumar.
      arenaqq.us~karypis/parbook/
    • University of Oregon - Intel Parallel Computing Curriculum
      arenaqq.us
    • UC Berkeley CS, Applications of Paralele Computing, Prof. Jim Demmel, UCB -- arenaqq.us
    • Udacity CS Intro to Parallel Programming - arenaqq.us
    • "Programming on Parallel Machines", Norm Matloff, UC Davis: arenaqq.us~matloff//PLN/ParProcBookSpdf
    • Cornell Virtual Workshop: Parallel Programming Concepts and High-Performance Computing - arenaqq.us
    • CS, Applications of Parallel Computers, Spring , Prof. Jim Demmel, UCB - arenaqq.us
    • Introduction to High Performance Scientific Computing", Victor Eijkhout, TACC - arenaqq.us~eijkhout/istc/arenaqq.us
    • COMP Advanced Parallel Computing (Fall, ), SDSU, Prof. Mary Thomas - arenaqq.us~mthomas/f

    • Georg Hager's SC '20 Tutorial on Node-Level Performance Tuning - arenaqq.us
  • Recommended reading - Linux
  • Photos/Graphics have been created by the authors, created by other LLNL employees, obtained from non-copyrighted, government or public domain (such as arenaqq.us) sources, or used with the permission of authors from other presentations and web pages.
  • History: These materials evolved from the following sources:
    • Tutorials developed by the Cornell University Center for Advanced Computing (CAC) available at arenaqq.us
    • Tutorials developed by the Maui High Performance Computing Center’s “SP Parallel Programming Workshop” (no longer available).
Источник: [arenaqq.us]

CCleaner®

New: Driver UpdaterBoost the performance of PC hardware and devices

NoYesYes

Faster ComputerControl which apps use your computer's resources

YesYesYes

Privacy ProtectionRemoves tracking files and browsing data

YesYesYes

PC Health CheckAutomatically analyzes, fixes and tunes your PC's performance

YesYesYes

Software UpdaterQuickly updates apps to reduce security vulnerabilities

NoYesYes

Cleans EverywhereEven places other cleaners can't reach

NoYesYes

Guards Against Junk FilesMonitors junk in real-time

NoYesYes

Automatically Clears HistoryCleans your browser when you close it

NoYesYes

Faster, Longer-lasting Hard DrivesIncludes Defraggler, to keep hard disks healthy and running efficiently

  Yes

File RecoveryIncludes Recuva, so you never have to worry about losing a file again

  Yes

See Inside your PCIncludes Speccy, so you can spot issues or find compatible upgrades

  Yes
Источник: [arenaqq.us]

AVG TuneUp for PC

Speed up, clean up, and fix your PC
with our advanced PC performance optimizer.

Get your PC running like new:

Clean out junk for more storage space.
Enjoy faster performance and startup speeds.
Update your programs automatically and avoid security risks.

See all features

Automatically fix and maintain your PC

Tired of bugs, crashes, and freezes? AVG’s Improved Automatic Maintenance tunes your PC every week for you, so you can enjoy better performance every time you turn it on.

Speed up and tune up your PC

Get your programs running faster, your PC starting quicker, and your games running smoother with AVG TuneUp and our patented Sleep Mode technology. Here’s how it works:

Remove bloatware and junk programs

Unnecessary programs, old toolbars and trial versions, and software that came preinstalled in your PC can take up space and cause trouble down the road. Which is why we make it easy to get rid of them.

Get more room for the stuff that matters

Your PC starts accumulating junk from the very first day: leftover Windows files, junk from the web, and more. You don’t need any of it, so AVG TuneUp cleans it out so your PC has the space for the things you need.

79%quicker startup

(in seconds)

30%faster work performance

(in points)

71 GBcleaned up

(in GB of free space)

Usage

For personal and family use only. Not for business or commercial use.


System Requirements

  • WindowsWindows 10, 8, and Windows 7 (Windows XP can be found here)
  • Apple MacOS (Mavericks) or above
  • Android Android (Lollipop, API 21) or above

Languages

For Windows: Chinese (simplified), Chinese (traditional), Czech, Danish, Dutch, English, French, German, Hungarian, Indonesian, Italian, Japanese, Korean, Malay, Polish, Portuguese (Brazil), Portuguese (Portugal), Russian, Serbian, Slovak, Spanish, and Turkish.

For Mac: English only.

For Android: Arabic, Chinese (simplified), Chinese (traditional), Czech, Danish, Dutch, English, Finnish, French, German, Greek, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Malay, Norwegian, Polish, Portuguese (Brazil), Portuguese (Portugal), Russian, Serbian, Slovak, Spanish, Swedish, Thai, Turkish, Ukrainian, and Vietnamese.

Frequently Asked Questions

How will AVG TuneUp improve the performance of my PC?

AVG TuneUp speeds up and cleans your PC by detecting and safely removing bloatware (unnecessary software) from your system. AVG TuneUp also addresses the issues that can cause system crashes and unpredictable behavior, including junk files, unnecessary programs installed on your PC, outdated software, and unusual system settings.

Over time, the reliability and performance of your PC will decline. You may notice issues such as:

  • Programs that run very slowly, crash, or freeze.
  • A lack of disk space.
  • Pop-ups from “bloatware”

AVG TuneUp optimizes your PC to restore its performance and keep it in top shape. To see AVG TuneUp in action, take a look at the results of our PC speed and cleaning tests.

How long will it take for AVG TuneUp to scan and clean my PC?

It typically takes only a few minutes for AVG TuneUp to scan and clean your PC, depending on the scan type and the amount of data being analyzed. But you can continue to use your PC normally while AVG TuneUp scans and cleans. The latest version of AVG TuneUp includes Automatic Maintenance, which runs silently in the background when needed to remove unnecessary items from your PC.

When you configure Automatic Maintenance for the first time, you can specify which item types you want AVG TuneUp to remove. AVG TuneUp is designed to run automatically, so you don’t need to worry about manually tuning your PC.

Is AVG TuneUp free to download?

Download a free trial of AVG TuneUp to enjoy our premium product completely free for 30 days. You’ll be able to scan your machine to identify bloatware and other unnecessary files that may be slowing you down, then remove them with just a single click. Optimize your PC absolutely free with our free trial today.

How can I test my PC performance?

A computer performance test works by first setting a benchmark and then running another test one to two months later to see how performance has changed. You can also use this test to measure your PC’s battery life.

Benchmarking your PC’s performance allows you to measure how fast your PC handles various operations by simulating normal processes, including Microsoft Office and Adobe products, playing games, browsing the web, and editing multimedia files.

Is AVG TuneUp an antivirus?

No. AVG TuneUp is a PC optimization tool that speeds up and cleans up your PC while fixing issues that may cause system crashes and other unexpected behavior. There is no “AVG TuneUp antivirus.”

Although AVG TuneUp does not actively protect your PC against viruses, you can use the application alongside trusted antivirus software to identify and remove unwanted programs that may have been installed on your PC as a result of malware. If you're interested in antivirus software in addition to cleanup software, we recommend getting an AVG Ultimate subscription, which includes both AVG Internet Security and AVG TuneUp.

Is AVG TuneUp the same as AVG TuneUp Utilities?

AVG TuneUp Utilities is an older version of AVG TuneUp that is no longer supported. If AVG TuneUp Utilities is installed on your PC, you can continue to use the product, but it no longer receives updates or bug fixes.

We strongly recommend downloading or upgrading to the latest version of AVG TuneUp, which includes extra features, improvements to existing features, and an updated user interface. After you download or upgrade, you’ll be able to install AVG TuneUp on an unlimited number of devices to optimize every PC in your household.

AVG TuneUp for PC AVG TuneUp for PC

The powerful, easy way to make your PC faster, cleaner, and better.

Get your PC running like new:

Clean out junk for more storage space.
Enjoy faster performance and startup speeds.
Update your programs automatically and avoid security risks.

See all features

Looks like you’re using arenaqq.us you like this app for Mac or Windows?

Looks like you’re using arenaqq.us the Google Play button to get antivirus for arenaqq.us download it for arenaqq.us like you’re using arenaqq.us antivirus file won't work on your arenaqq.usad it for arenaqq.us like you’re using arenaqq.us you like this app for Windows or Mac?Looks like you’re using arenaqq.us the Google Play button to get antivirus for arenaqq.us download it for arenaqq.us like you’re using arenaqq.us the App Store button to get antivirus for arenaqq.us download it for arenaqq.us antivirus file is for Android and won't work on your arenaqq.us antivirus file is for Android and won't work on your arenaqq.us antivirus file is for Android and won't work on your arenaqq.us antivirus file is for iOS and won't work on your arenaqq.us antivirus file is for iOS and won't work on your arenaqq.us antivirus file is for iOS and won't work on your arenaqq.us antivirus file is for PC and won’t work on your arenaqq.us antivirus file is for Mac and won’t work on your arenaqq.us antivirus file is for Android and won’t work on your arenaqq.us antivirus file is for iOS and won’t work on your arenaqq.us like you’re using arenaqq.us you like this app for Mac or Windows?Looks like you’re using arenaqq.us the Google Play button to get antivirus for arenaqq.us download it for arenaqq.us like you’re using arenaqq.us antivirus file won't work on your arenaqq.usad it for arenaqq.us like you’re using arenaqq.us you like this app for Windows or Mac?Looks like you’re using arenaqq.us the Google Play button to get antivirus for arenaqq.us download it for arenaqq.us like you’re using arenaqq.us the App Store button to get antivirus for arenaqq.us download it for arenaqq.us file is for Android and won't work on your arenaqq.us file is for Android and won't work on your arenaqq.us file is for Android and won't work on your arenaqq.us file is for iOS and won't work on your arenaqq.us file is for iOS and won't work on your arenaqq.us file is for iOS and won't work on your arenaqq.us antivirus file is for PC and won’t work on your arenaqq.us antivirus file is for Mac and won’t work on your arenaqq.us antivirus file is for Android and won’t work on your arenaqq.us antivirus file is for iOS and won’t work on your arenaqq.us like you’re using arenaqq.us you like this app for Mac or Windows?Looks like you’re using arenaqq.us the Google Play button to get antivirus for arenaqq.us download it for arenaqq.us like you’re using arenaqq.us antivirus file won't work on your arenaqq.usad it for arenaqq.us like you’re using arenaqq.us you like this app for Windows or Mac?Looks like you’re using arenaqq.us the Google Play button to get antivirus for arenaqq.us download it for arenaqq.us like you’re using arenaqq.us the App Store button to get antivirus for arenaqq.us download it for arenaqq.us VPN file is for Android and won't work on your arenaqq.us VPN file is for Android and won't work on your arenaqq.us VPN file is for Android and won't work on your arenaqq.us VPN file is for iOS and won't work on your arenaqq.us VPN file is for iOS and won't work on your arenaqq.us VPN file is for iOS and won't work on your arenaqq.us antivirus file is for PC and won’t work on your arenaqq.us antivirus file is for Mac and won’t work on your arenaqq.us antivirus file is for Android and won’t work on your arenaqq.us antivirus file is for iOS and won’t work on your machine.

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Windows

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

Download it here

WindowsMacAndroidiOS(from Google Play)(from Google Play)(from Google Play)Back
AVG Logo
Источник: [arenaqq.us]

This is the ultimate guide to Windows 8 Pro product key and activation.

 

WindowsProfessional-Product-Key


In this post, you will find free updated product keys you can use to activate the Windows 8 Professional. 

 

Why Use Windows 8 Professional?

Here are the incredible features of Windows 8:

  • Easy to use interface 
  • Comes with SkyDrive 
  • Split-screen app
  • Metro-style
  • Improved Windows store 
  • Upgraded app search function
  • Fast boot
  • Comes with Xbox games
  • Live synching system
  • Upgraded Internet Explorer and Antivirus program
  • Improved security

Windows 8 was received with much anticipation from Windows users.

It became the preferred Operating System among many who found it user-friendly and appealing. It came with interactive tiles and was pleasing to the eye.

Microsoft changed the look of Windows with the launch of Windows 8.

Eventually, the new metro style tiled interface became the accepted style for desktop. 

Even Windows 10, which is the latest and last version of Windows OS, uses the same style that was introduced in Windows 8 for the very first time. 

 

What Is Windows 8 Pro Product Key?

Windows 8 professional product key is a digit character code used to activate a copy of Windows 8 professional. Without a working pro activation key, you will not be able to activate the Operating System.

The product key code looks like this: XXXXX-XXXXX-XXXXX-XXXXX-XXXXX

If you have obtained a copy of Windows 8 professional, you should see the Windows 8 professional product key inside the product box. 

Given you have purchased Windows 8 pro online, you will receive your Windows 8 professional product key via email. 

In case you have lost your product key, you can retrieve it using a key finder. 

However, if you have purchased the Operating System, you can download and activate it using free product keys on this post. 

We have a wide range of keys you can use.

 

Free List of Windows 8 Professional Product Activation Keys 

Here is a free list of % free working Windows 8 pro product serial keys you can use to activate your copy of Windows 8 professional.

Simply copy one of these product keys and use them.

 

84NRV-6CJR6-DBDXH-FYTBF-4X49V

QGR4NPMD-KCRQBXT-YG

ND8P2-BD2PB-DD8HMR-CRYQH

T3NJK-3PT7BJ-2X27F-8B2KV

YMMV-FVDXB-QP6XF-9FTRT-P7F9V

 

BTNJ7-FFMBR-FF9BH-7QMJ9-H49T7

HB39N-V9K6F-PV-KWBTC-Q3R9V

XWCHQ-CDMYC-9WN2C-BWWTV-YY2KV

RRYGR-8JNBY-V2RJ9-TJP4PT7

4Y8N3-H7MMW-C76VJ-YD3XV-MBDKV

 

28VNV-HF42G-K2WM9-JXRJQ-2WBQW

BDDNV-BQ27P-9P9JJ-BQJKTJXV

CR8NGKCR-X2MPD-G7M7P-GQ4DH

6PNR4BBH-XX8K2-DCKVMFDH

9XNM-YYYR9HM-YFPTX-T8XT7

 

NTTX3-RV7VB-T7X7F-WQYYY-9Y92F

MBFBV-W3DPMVKN-PJCQD-KKTF7

DNJXJ-7XBWT-X22TX-BKG7J

6RH4V-HNTWC-JQKG8-RFR3R

Y8N3-H7MMW-C76VJ-YD3XV-MBDKV

 

6RH4V-HNTWC-JQKG8-RFR3R

XKY4K-2NRWR-8F6PRF-CRYQH

TK8TP-9JN6P-7X7WW-RFFTV-B7QPF

NF32V-Q9P3W-7DR7Y-JGWRW-JFCK8

DNJXJ-7XBWT-X22TX-BKG7J


Each Windows 8 professional product key on this list is genuine and should work for most users. However, if you don’t find a working serial key, then someone else has probably used it. 

Note that you can only use a product key Windows on one computer. If none of the keys work for you, feel free to bookmark this page and come back tomorrow for updated keys. 

We update these keys regularly; therefore, you can be sure you find a working Windows 8 professional product key at the end of the day.

 

FAQ

How Do I Get Windows 8 Pro?

If your PC is running a genuine copy of Windows 7, you can easily upgrade to Windows 8 without paying the additional license fee.

Otherwise, you will be required to buy a genuine copy of Windows 8 and get a pro product key along with it. 

The method involves these simple steps:

  1. Launch the Windows 8 Upgrade Assistant on your computer 
  2. The upgrade wizard will determine the hardware of your computer 
  3. If your Windows PC meets the system requirements, you will be able to download updated files
  4. Wait for Windows update files to download and install 

If your PC doesn’t qualify for the upgrade, you have the following options:

  • Purchase and install Windows from Microsoft using the provided pro key 
  • Purchase a computer with pre-installed Windows
  • Download and install Windows 8 professional using a free pro product key on this page

 

What Do You Need in Windows 8 Professional Product Key?

When installing Windows 8, you will be required to provide an activation key. You must provide a working Windows 8 professional product key to proceed with the installation. 

If you don’t have a product key, it might be impossible to install and activate the Operating System. Luckily, you can find free product keys online. 

You can also install the software with a generic Windows 8 professional product key and continue using it without activating it. However, you will not be able to access its premium features.

 

How Can I Activate My Windows 8 Pro?

You can activate Windows 8 from the PC settings app. To do this, press the Windows key + C to access the charms bar. Then click Settings and then Change PC settings. 

If your copy of Windows is not activated, you will see the option Activate Windows. Alternatively, you can go to PC and devices, then PC info to see if you have an activated Windows. 

Click the activate button to activate your Windows over the internet. If you see an error preventing you from activating, perform a search form the error to find more information. 

 

Where Do I Find My Windows 8 Pro Product Key on My HP Laptop?

When Microsoft unveiled Windows 8, they changed from including a sticker with a serial key to BIOS embedded product keys. 

According to the tech giant, by eliminating the sticker, they are able to eliminate one of the easiest ways for reprehensible people to get a genuine activation key. 

The move also enables them to eliminate the worry the sticker could be damaged and the irritating process of having to type all the numbers and letters when installing Windows 8 professional. 

With the product key in the BIOS, whenever you want to reinstall the operating system on the same computer it can with, the installation wizard will grab the product key automatically from the BIOS. That’s why you cannot find the activation key on a sticker on your HP laptop.

 

Can I Download Windows 8 Pro for Free?

Here are a few ways to get Windows 8 for free:

  • Obtain a preview version of the Windows 8 OS. Go to this page and download an ISO file. Burn the ISO file to a CD/DVD. 
  • Get a copy of Windows 8 for students. Visit this page to get a copy of Windows 8 if you’re a student. You may have to pay a small price. 
  • Simply upgrade to Windows 8 using a product key. Use this page if you have previously purchased Windows 8. 
petr kudlacek arenaqq.us

Petr Kudlacek

Petr is a serial tech entrepreneur and the CEO of Apro Software, a machine learning company. Whenever he’s not blogging about technology for arenaqq.us or arenaqq.us, Petr enjoys playing sports and going to the movies. He’s also deeply interested about mediation, Buddhism and biohacking.

Categories WindowsИсточник: [arenaqq.us]
Speed Up My PC 2010 crack serial keygen

Notice: Undefined variable: z_bot in /sites/arenaqq.us/antivirus/speed-up-my-pc-2010-crack-serial-keygen.php on line 111

Notice: Undefined variable: z_empty in /sites/arenaqq.us/antivirus/speed-up-my-pc-2010-crack-serial-keygen.php on line 111

Comments

Leave a Reply

Your email address will not be published. Required fields are marked *