1 Friday, September 29, 2006 If all you have is a hammer, then everything looks like a nail....

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Friday, September 29, 2006

If all you have is a hammer, then everything looks like

a nail.

- Anonymous

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Domain Decomposition1. Divide data in approx. equal parts

2. Partition the computation

Functional Decomposition1. Divide the computation into disjoint tasks

2. Determine data requirements of these tasks

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Domain Decomposition1. Divide data in approx. equal parts

2. Partition the computation

Functional Decomposition1. Divide the computation into disjoint tasks

2. Determine data requirements of these tasks If data is disjoint then partition is complete If there is significant overlap consider

domain decomposition.

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Task dependency graph?

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Typically maximum degree of concurrency is less than the total number of tasks.

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Typically maximum degree of concurrency is less than the total number of tasks.

Degree of concurrency depends on shape of task dependency graph.

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Degree of concurrency depends on shape of task dependency graph.

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Mapping

Maximize use of concurrency.Task dependencies and interactions are

important in selection of good mapping.Minimize completion time by making

sure that the processes on critical path execute as soon as they are ready.

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Mapping

Maximize concurrency and minimize interaction among processors Place tasks that are able to execute

independently on different processors to increase concurrency.

Place tasks that communicate frequently on same processor to increase locality.

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Mapping

Cannot use more than 4 processors. Why?

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Mapping

Prevent inter-task interaction from becoming inter-process interaction

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Agglomeration

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Data partitioning: Block distribution

Higher dimensional distributions may help reduce the amount of shared data that needs to be accessed

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Data partitioning: Block distribution

Higher dimensional distributions may help reduce the amount of shared data that needs to be accessed.

n2/p +n2 vs.

2n2/√p

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Sum N numbers

N numbers are distributed among N tasks

Centralized algorithm

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Sum N numbers

N numbers are distributed among N tasks

Distributing computation

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Recursive Decomposition

Divide and conquerSet of independent sub-problems

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Recursive Decomposition

Sum N numbers.

How many steps required?

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Hybrid decomposition

Possible to combine different techniquesFinding minimum of a large set of

numbers by purely recursive decomposition is not efficient.

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Hybrid decomposition

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Exploratory Decomposition

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Unfinished tasks can be terminated once solution is found

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Read: Speculative Decomposition

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Communications

Most parallel problems need to communicate data between different tasks.

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Embarrassingly Parallel Applications

No communication between tasks.One end of spectrum of parallelization

Examples?

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Factors involving communication

Machine cycles and resources that could be used for computation are instead used to package and transmit data.

Sending many small messages can cause latency to dominate communication overheads.

Package small messages into a larger message results in increased bandwidth.

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Factors involving communicationSynchronous communications.

Asynchronous communications.

Interleaving computation with communication.

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npoints = 10000

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 = 4.0*circle_count/npoints

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