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Execute Simultaneously on Multiple Data Sets


The single program multiple data (spmd) language construct allows seamless interleaving of serial and parallel programming. The spmd statement lets you define a block of code to run simultaneously on multiple workers. Variables assigned inside the spmd statement on the workers allow direct access to their values from the client by reference via Composite objects.

This chapter explains some of the characteristics of spmd statements and Composite objects.

When to Use spmd

The "single program" aspect of spmd means that the identical code runs on multiple workers. You run one program in the MATLAB® client, and those parts of it labeled as spmd blocks run on the workers. When the spmd block is complete, your program continues running in the client.

The "multiple data" aspect means that even though the spmd statement runs identical code on all workers, each worker can have different, unique data for that code. So multiple data sets can be accommodated by multiple workers.

Typical applications appropriate for spmd are those that require running simultaneous execution of a program on multiple data sets, when communication or synchronization is required between the workers. Some common cases are:

  • Programs that take a long time to execute — spmd lets several workers compute solutions simultaneously.

  • Programs operating on large data sets — spmd lets the data be distributed to multiple workers.

Define an spmd Statement

The general form of an spmd statement is:


    Note   If a parallel pool is not running, spmd creates a pool using your default cluster profile, if your parallel preferences are set accordingly.

The block of code represented by <statements> executes in parallel simultaneously on all workers in the parallel pool. If you want to limit the execution to only a portion of these workers, specify exactly how many workers to run on:

spmd (n)

This statement requires that n workers run the spmd code. n must be less than or equal to the number of workers in the open parallel pool. If the pool is large enough, but n workers are not available, the statement waits until enough workers are available. If n is 0, the spmd statement uses no workers, and runs locally on the client, the same as if there were not a pool currently running.

You can specify a range for the number of workers:

spmd (m,n)

In this case, the spmd statement requires a minimum of m workers, and it uses a maximum of n workers.

If it is important to control the number of workers that execute your spmd statement, set the exact number in the cluster profile or with the spmd statement, rather than using a range.

For example, create a random matrix on three workers:

spmd (3)
    R = rand(4,4);

    Note   All subsequent examples in this chapter assume that a parallel pool is open and remains open between sequences of spmd statements.

Unlike a parfor-loop, the workers used for an spmd statement each have a unique value for labindex. This lets you specify code to be run on only certain workers, or to customize execution, usually for the purpose of accessing unique data.

For example, create different sized arrays depending on labindex:

spmd (3)
    if labindex==1 
        R = rand(9,9);
        R = rand(4,4);

Load unique data on each worker according to labindex, and use the same function on each worker to compute a result from the data:

spmd (3)
    labdata = load(['datafile_' num2str(labindex) '.ascii'])
    result = MyFunction(labdata)

The workers executing an spmd statement operate simultaneously and are aware of each other. As with a communicating job, you are allowed to directly control communications between the workers, transfer data between them, and use codistributed arrays among them.

For example, use a codistributed array in an spmd statement:

spmd (3)
    RR = rand(30, codistributor());

Each worker has a 30-by-10 segment of the codistributed array RR. For more information about codistributed arrays, see Working with Codistributed Arrays.

Display Output

When running an spmd statement on a parallel pool, all command-line output from the workers displays in the client Command Window. Because the workers are MATLAB sessions without displays, any graphical output (for example, figure windows) from the pool does not display at all.

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