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Parallel Matlab. Vikas Argod Research Computing and Cyberinfrastructure [email protected] Matlab in Parallel. Beautifully parallel. Message Passing. e.g., Multi, paralize, Plab, ParMatlab. e.g., MultiMatlab, CMTM, DPToolbox,MatlabMPI, pMatlab.

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Parallel Matlab

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Parallel matlab l.jpg

Parallel Matlab

Vikas Argod

Research Computing and Cyberinfrastructure

[email protected]


Matlab in parallel l.jpg

Matlab in Parallel

Beautifully parallel

Message Passing

e.g., Multi, paralize, Plab, ParMatlab

e.g., MultiMatlab, CMTM, DPToolbox,MatlabMPI, pMatlab

www-math.mit.edu/~edelman/homepage/papers/pmatlab.pdf


Parallel computing toolbox pct l.jpg

Parallel Computing Toolbox (PCT)

  • Data and task parallelism using

    • Parallel-for loops

    • Distributed arrays

    • Parallel numerical algorithms

    • Message Passing functions

  • Easy transition between serial and parallel

http://www.mathworks.com/products/parallel-computing/description1.html


Distributed computing server dcs l.jpg

Distributed Computing Server (DCS)

  • Parallel Computing Toolbox

    • Only four local workers on a multicore or multiprocessor computer

  • PCT + DCS -> Cluster-based applications

  • Coordinate and execute independent MATLAB operations simultaneously on a cluster of computers


Architecture of pct l.jpg

Architecture of PCT

Coordinates the execution of jobs and the evaluation of their tasks, distributes the tasks for evaluation to the individual Matlab sessions called workers

Normal Matlab session in which the job and its tasks are defined by using the functions provided by PCT. Often, it is on the machine where user programs Matlab.

Matlab sessions which evaluates the task distributed by scheduler


Example problem description l.jpg

Example -Problem Description

  • System of 2^6 (=64) square matrices

    • Each matrix  Sparse, square,2^17 (=131072) dimension

    • Matrix is generated by ‘spdiags’ using a ‘random’ array

  • To extract first 100 eigenvectors

    • ‘eigs’ function is used

  • See handout for the code

  • Each matrix calculation is distributed


Example serial matlab l.jpg

Example - Serial Matlab

  • ‘eigen’ is a function

    • Input : (vector of random numbers, dimension of the matrix)

    • Output : eigenvectors

n = 2^16;

p = 2^7;

e = rand (n,1,p);

for i = 1 : p

a = e(:,:,i);

ans(i) = eigen(a,n);

end

Independent calculations


Example parallel matlab l.jpg

Example-Parallel Matlab

  • Find available distributed computing resources (findResource function)nprocs = [ getenv('DMATLAB_NPROCS') ]

    np = sscanf( nprocs, '%d' )

    mgr_name = [ getenv('JOBMANAGER') ]

    mgr_host = [ getenv('JOBMANAGERHOST') ]jm =findResource('jobmanager','Name',mgr_name,'LookupURL',mgr_host);

    2. Create distributed job

    j = createJob(jm,'FileDependencies',{<path>});

Path to additional files


Example parallel matlab9 l.jpg

Example – Parallel Matlab

  • Create Tasks for each worker

    n = 2^16;

    p = 2^7;

    e = rand (n,1,p);

    for i = 1 : p

    a = e(:,:,i);

    createTask(j, @eigen, 1, {a,n});

    end

Same as Serial Code

Creating tasks

Name of the job

Input arguments to each task

Number of output arguments

Parallel Task function


Example parallel matlab10 l.jpg

Example – Parallel Matlab

4. Submit the job and wait for the results

5. Remove the individual task or parent job objectdestroy(j);

If there are M tasks created and each task has N output arguments, then ‘results’ is a MxN cell array

submit(j);

get(jm);

waitForState(j);

results = getAllOutputArguments(j)


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