Examples

The Algorithm

The objective in the pctdemo_aux_proftaskpar example is to calculate the eig of a random matrix of increasing size, and pick the maximum value from the resulting vector. The crucial issue is the increasing matrix size based on the loop counter ii. Here is the basic iteration:

v(ii) = max( abs( eig( rand(ii) ) ) );

The actual for loop can be seen in the example code.

The code for this example can be found in pctdemo_aux_proftaskpar.

Enabling the Parallel Profiler's Data Collection

A good practice is to reset the parallel profiler on the cluster before turning on mpiprofile in pmode. It makes sure the data is cleared and the profiler is off and in default -messagedetail setting.

P>> mpiprofile reset;
P>> mpiprofile on;

Inside a for drange there cannot be any communication between labs so the -messagedetail can be set to simplified (see help mpiprofile). If you do not specify the -messagedetail option and you run a program with no communication, you get 0s in the communication fields.

P>> v = zeros( 1, 300, codistributor() );
P>> tic;pctdemo_aux_proftaskpar('drange');toc;

1    Start of for-drange loop.
     The computational complexity increases with the loop index.
     Done
     Elapsed time is 0.287845 seconds.

2    Start of for-drange loop.
     The computational complexity increases with the loop index.
     Done
     Elapsed time is 0.351070 seconds.

3    Start of for-drange loop.
     The computational complexity increases with the loop index.
     Done
     Elapsed time is 0.335363 seconds.

4    Start of for-drange loop.
     The computational complexity increases with the loop index.
     Done
     Elapsed time is 0.412805 seconds.

5    Start of for-drange loop.
     The computational complexity increases with the loop index.
     Done
     Elapsed time is 0.482021 seconds.

6    Start of for-drange loop.
     The computational complexity increases with the loop index.
     Done
     Elapsed time is 0.683651 seconds.

7    Start of for-drange loop.
     The computational complexity increases with the loop index.
     Done
     Elapsed time is 0.838188 seconds.

8    Start of for-drange loop.
     The computational complexity increases with the loop index.
     Done
     Elapsed time is 1.005636 seconds.

9    Start of for-drange loop.
     The computational complexity increases with the loop index.
     Done
     Elapsed time is 1.128090 seconds.

10    Start of for-drange loop.
     The computational complexity increases with the loop index.
     Done
     Elapsed time is 1.398578 seconds.

11    Start of for-drange loop.
     The computational complexity increases with the loop index.
     Done
     Elapsed time is 1.589610 seconds.

12    Start of for-drange loop.
     The computational complexity increases with the loop index.
     Done
     Elapsed time is 1.825993 seconds.

In this algorithm the elapsed time should always be longest on the last lab. We use tic toc here so that we can compare the longest running time to a parfor. The use of profiling inside a parfor loop with mpiprofile is currently not supported.

Running the Parallel Profiler Graphical Interface

To get the profiler interface, simply type mpiprofile viewer in pmode. You can also view data from a parallel job. See the help or documentation for information on how to do this.

P>> mpiprofile viewer; % The viewer action also turns off the profiler

1    Sending  pmode lab2client  to the MATLAB client for asynchronous evaluation.

When the profiler interface opens, by default the Function Summary Report is shown for lab 1. Click Compare max vs min TotalTime to see the difference in work distribution between the first and last lab for all the functions called. Look at the pctdemo_aux_proftaskpar function:

Observing Uneven Work Distribution

Here are a few steps for spotting uneven work distribution on the MATLAB workers. Uneven work distribution almost certainly prevents optimal speedup of serial algorithms.

In the top figure of this page, only the first few labs take approximately the same amount of time; the others take significantly longer. This large difference in the total time distribution is an indicator of suboptimal load balancing.

Using PARFOR for Better Work Distribution

Optimal performance for this type of parallelism requires manual distribution of the iterations in pmode or the use of parfor with parpool. To get better work distribution (with pmode) in this type of a problem, you need to create a random distribution of the tasks rather than rely on for drange to statically partition the iterations.

Using parfor is generally better suited to this type of task. With the parfor (i=n:N) construct you get dynamic work distribution which splits the iterations at execution time, across all labs. Thus the cluster is better utilized. You can see this by running the same function outside of pmode using a parfor construct. This results in significantly higher speedup compared to the for drange.

To try this with parfor, run the following commands on the MATLAB client outside of pmode.

pmode close;

parpool;

tic;pctdemo_aux_proftaskpar('parfor');toc;

You should get an output that looks like: Done Elapsed time is 6.376887 seconds.

There is a significant speedup (it's nearly two times faster on the 4-node cluster) using parfor instead of for drange, with no change to the actual algorithm. Note that parfor operates as a standard for loop inside of pmode. Please ensure you try parfor outside of pmode to get the speedup. See help for parpool and parfor.

Parallelizing Serial for Loops

To make a serial (iteration independent) for-loop parallel you need to add the drange option when inside a parallel job, or replace for with a parfor. The parfor loop will only work as intended with parpool. You can view the different styles of for loop parallelism in the code shown in this example. See pctdemo_aux_proftaskpar. The parfor version is under the case 'parfor' and the drange version is under the case 'drange'.