SlideServe
Télécharger Obtenir Pro
Menu
Accueil Pro
Préférences
ApparenceLight Aide et commentaires
© SlideServe · v2026

Parallel Decomposition Methods

eve avatar
eve
44 diapositives · 670 Views · Jul 18, 2014

Parallel Decomposition Methods. Introduction to Parallel Programming – Part 2. Review & Objectives. Previously: Defined parallel computing Explained why parallel computing is becoming mainstream Explained why explicit parallel programming is necessary

Parallel Decomposition Methods - slide 1
01 / 44Parallel Decomposition Methods
1 / 17

Télécharger la présentation

Parallel Decomposition Methods
Une image / un lien ci-dessous est fourni (tel quel) pour télécharger la présentation

Politique de téléchargement : le contenu du site vous est fourni TEL QUEL pour votre information et votre usage personnel ; il ne peut être vendu, concédé sous licence ni partagé sur d’autres sites sans l’accord de son auteur.Le contenu vous est fourni TEL QUEL, pour votre information et votre usage personnel uniquement. Téléchargez la présentation en cliquant sur ce lien. Lors du téléchargement, si vous ne parvenez pas à télécharger une présentation, l’éditeur a peut-être supprimé le fichier de son serveur.Pendant le téléchargement, si vous ne parvenez pas à obtenir une présentation, le fichier a peut-être été supprimé par l’éditeur.

F I N

Plus de contenu associé

Transcription de la présentation

  1. Parallel Decomposition Methods Introduction to Parallel Programming – Part 2

  2. Review & Objectives • Previously: • Defined parallel computing • Explained why parallel computing is becoming mainstream • Explained why explicit parallel programming is necessary • At the end of this part you should be able to: • Identify opportunities for parallelism in code segments and applications • Describe three methods of dividing independent work

  3. Methodology • Study problem, sequential program, or code segment • Look for opportunities for parallelism • Try to keep all cores busy doing useful work

  4. Ways of Exploiting Parallelism • Domain decomposition • Task decomposition • Pipelining

  5. Domain Decomposition • First, decide how data elements should be divided among cores • Second, decide which tasks each core should be doing • Example: Vector addition

  6. Domain Decomposition Find the largest element of an array

  7. Domain Decomposition Find the largest element of an array Core 0 Core 1 Core 2 Core 3

  8. Domain Decomposition Find the largest element of an array Core 0 Core 1 Core 2 Core 3

  9. Domain Decomposition Find the largest element of an array Core 0 Core 1 Core 2 Core 3

  10. Domain Decomposition Find the largest element of an array Core 0 Core 1 Core 2 Core 3

  11. Domain Decomposition Find the largest element of an array Core 0 Core 1 Core 2 Core 3

  12. Domain Decomposition Find the largest element of an array Core 0 Core 1 Core 2 Core3

  13. Domain Decomposition Find the largest element of an array Core 0 Core 1 Core 2 Core 3

  14. Domain Decomposition Find the largest element of an array Core 0 Core 1 Core 2 Core 3

  15. Domain Decomposition Find the largest element of an array Core 0 Core 1 Core 2 Core 3

  16. Domain Decomposition Find the largest element of an array Core 0 Core 1 Core 2 Core 3

  17. Domain Decomposition Find the largest element of an array Core 0 Core 1 Core2 Core3

  18. Task (Functional) Decomposition • First, divide problem into independent tasks • Second, decide which data elements are going to be accessed (read and/or written) by which tasks • Example: Event-handler for GUI

  19. Task Decomposition f() g() r() q() h() s()

  20. Task Decomposition Core1 f() Core 0 g() Core 2 r() q() h() s()

  21. Task Decomposition Core 1 f() Core 0 g() Core 2 r() q() h() s()

  22. Task Decomposition Core 1 f() Core 0 g() Core 2 r() q() h() s()

  23. Task Decomposition Core 1 f() Core 0 g() Core 2 r() q() h() s()

  24. Task Decomposition Core 1 f() Core 0 g() Core 2 r() q() h() s()

  25. Pipelining • Special kind of task decomposition • “Assembly line” parallelism • Example: 3D rendering in computer graphics Model Project Clip Rasterize Output Input

  26. Processing One Data Set (Step 1) Model Project Clip Rasterize

  27. Processing One Data Set (Step 2) Model Project Clip Rasterize

  28. Processing One Data Set (Step 3) Model Project Clip Rasterize

  29. Processing One Data Set (Step 4) Model Project Clip Rasterize The pipeline processes 1 data set in 4 steps

  30. Processing Two Data Sets (Step 1) Model Project Clip Rasterize

  31. Processing Two Data Sets (Time 2) Model Project Clip Rasterize

  32. Processing Two Data Sets (Step 3) Model Project Clip Rasterize

  33. Processing Two Data Sets (Step 4) Model Project Clip Rasterize

  34. Processing Two Data Sets (Step 5) Model Project Clip Rasterize The pipeline processes 2 data sets in 5 steps

  35. Pipelining Five Data Sets (Step 1) Core 0 Core 1 Core 2 Core 3 Data set 0 Data set 1 Data set 2 Data set 3 Data set 4

  36. Pipelining Five Data Sets (Step 2) Core 0 Core 1 Core 2 Core 3 Data set 0 Data set 1 Data set 2 Data set 3 Data set 4

  37. Pipelining Five Data Sets (Step 3) Core 0 Core 1 Core 2 Core 3 Data set 0 Data set 1 Data set 2 Data set 3 Data set 4

  38. Pipelining Five Data Sets (Step 4) Core 0 Core 1 Core 2 Core 3 Data set 0 Data set 1 Data set 2 Data set 3 Data set 4

  39. Pipelining Five Data Sets (Step 5) Core 0 Core 1 Core 2 Core 3 Data set 0 Data set 1 Data set 2 Data set 3 Data set 4

  40. Pipelining Five Data Sets (Step 6) Core 0 Core 1 Core 2 Core 3 Data set 0 Data set 1 Data set 2 Data set 3 Data set 4

  41. Pipelining Five Data Sets (Step 7) Core 0 Core 1 Core 2 Core 3 Data set 0 Data set 1 Data set 2 Data set 3 Data set 4

  42. Pipelining Five Data Sets (Step 8) Core 0 Core 1 Core 2 Core 3 Data set 0 Data set 1 Data set 2 Data set 3 Data set 4

  43. References • Richard H. Carver and Kuo-Chung Tai, Modern Multithreading: Implementing, Testing, and Debugging Java and C++/Pthreads/ Win32 Programs, Wiley-Interscience (2006). • Robert L. Mitchell, “Decline of the Desktop,” Computerworld (September 26, 2005). • Michael J. Quinn, Parallel Programming in C with MPI and OpenMP, McGraw-Hill (2004). • Herb Sutter, “The Free Lunch is Over: A Fundamental Turn Toward Concurrency in Software,” Dr. Dobb’s Journal 30, 3 (March 2005).