parallel decomposition methods

INTEL CONFIDENTIAL

Parallel Decomposition MethodsIntroduction to Parallel Programming – Part 2

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2

Review & Objectives

Previously:Defined parallel computingExplained why parallel computing is becoming mainstreamExplained 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 applicationsDescribe three methods of dividing independent

work


3

Methodology

Study problem, sequential program, or code segmentLook for opportunities for parallelismTry to keep all cores busy doing useful work


4

Ways of Exploiting Parallelism

Domain decompositionTask decompositionPipelining


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Domain Decomposition

First, decide how data elements should be divided among cores

Second, decide which tasks each core should be doing

Example: Vector addition


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Find the largest element of an array


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Core 0 Core 1 Core 2 Core 3


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Task (Functional) Decomposition

First, divide problem into independent tasksSecond, decide which data elements are going to be

accessed (read and/or written) by which tasks

Example: Event-handler for GUI


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Task Decomposition

s()

r()q()h()

g()

f()


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Task Decomposition

s()

r()q()h()

g()

Core 0

Core 2

Core 1 f()


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Task Decomposition

s()

r()q()h()

g()Core 2

Core 1 f() Core 0


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Task Decomposition

s()

r()q()h()

g()Core 2

Core 1 Core 0f()


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Task Decomposition

s()

r()q()h()

g()Core 2

Core 1 Core 0f()


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Task Decomposition

s()

r()q()h()

g()Core 2

Core 1 Core 0f()


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Pipelining

Special kind of task decomposition“Assembly line” parallelismExample: 3D rendering in computer graphics

RasterizeClipProjectModel

Input Output


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Processing One Data Set (Step 1)



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The pipeline processes 1 data set in 4 steps


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Processing Two Data Sets (Step 1)



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Processing Two Data Sets (Time 2)



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The pipeline processes 2 data sets in 5 steps


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Pipelining Five Data Sets (Step 1)

Data set 0

Data set 1

Data set 2

Data set 3

Data set 4



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Data set 0

Data set 1

Data set 2

Data set 3

Data set 4



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Data set 0

Data set 1

Data set 2

Data set 3

Data set 4



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Data set 0

Data set 1

Data set 2

Data set 3

Data set 4



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Data set 0

Data set 1

Data set 2

Data set 3

Data set 4



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Data set 0

Data set 1

Data set 2

Data set 3

Data set 4



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Data set 0

Data set 1

Data set 2

Data set 3

Data set 4



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Data set 0

Data set 1

Data set 2

Data set 3

Data set 4



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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).

parallel decomposition methods

Documents

intel logo

parallel computingexplained

respective owners

united states

explicit parallel programming

processors busy means

good parallel programmer

available processors