3. Run the Command (In Your Matlab Command Window)

Matlab (R2012a) on Schrödinger

1. Copy the files from (MATLABROOT)/toolbox/distcomp/examples/integration/sge/nonshared to (MATLABROOT)/toolbox/local on your local computer.

(All descriptions are in a local linux format, Windows or Mac local format should be very similar. The cluster format is linux)

2. Start matlab on your local computer.

3. Run the command (in your matlab command window)

pctconfig('hostname', 'LOCAL_FQDN') where LOCAL_FQDN is the Fully Qualified Domain Name of your local computer. You can obtain it by logging in (using ssh) to the cluster and using the “who” command.

4. Setup a generic scheduler. Parallel->Manage Cluster Profiles. The pictures below show local and remote data locations into which matlab will insert job data. The number of workers is optional but be aware that jobs requesting more than the available number of cores will wait (and it may be a long time). It may be easier to ensure that your job runs locally (perhaps with multiple processes) and start correspondingly small on Schrödinger. (You must have an account on Schrödinger for this to work.) The command

qstat -u “*” on erwin will list all running and queued jobs on the Schrödinger cluster and qstat by itself will list just yours.

The data locations shown are set according to your configuration. Schrodinger_username is a placeholder for your cluster login name and local_username a placeholder for your matlab client desktop name. The functions referred to above with the '@...' designations must be in $matlabroot/toolbox/local. I found them in $matlabroot/toolbox/distcomp/examples/integration/sge/nonshared. The nonshared indicates that your workstation/desktop does not share a filesystem with the cluster. If your matlab parallel computing toolbox does not have them, they are available on the Schrödinger cluster..

The matlab scripts below are examples I found to work with the software. The hpccLinpack.m file performs an spmd job. clear c=parcluster('Schrodinger') j=createJob(c) t=createTask(j,@hpccLinpack, 2, {}) submit(j) wait(j) out=get(t,'OutputArguments') out{:}

I added the hpccLinpack.m file to this document. I had to change hpccLinpack.m to force it to produce two output arguments. You see that 2 in the createTask command. If the script(s) are 'parallel' scripts you can substitute clear j=batch('hpccLinpack','matlabpool',8,'CaptureDiary',true) wait(j) diary(j) the wait function causes matlab to wait until the job is finished. The above set runs 9 separate worker processes; the 8 listed above + 1 as a driver. The matlab “save” command can be used in a script to save a file on the remote system. Otherwise, data can be passed back to the local client using function arguments.

If you have questions or issues, please let me know.

Dr. Marty Bylander System Support Analyst-HPC [email protected] 512-245-7866 office 512-245-5806 fax The original versions of the hpcchallenge files are also on Schrödinger under /usr/local/matlab2012a/toolbox/distcomp/examples/benchmark/hpcchallenge. The altered hpccLinpack.m is shown below: function [datasize, perf ] = hpccLinpack( m ) %HPCCLINPACK An implementation of the HPCC Global HPL benchmark % % hpccLinpack(m) creates a random codistributed real matrix A of size % m-by-m and a real random codistributed vector B of length m. It then % measures the time to perform the matrix division of A into B (X = A\B, % which is the solution to the equation A*X = B) in a parallel way using % the currently available resources (MATLAB pool). This time indicates % the performance metric. Finally the function computes the scaled % residuals to ensure that the error on the computation is within % acceptable bounds. % % If you do not specify m, the default value is that returned from % hpccGetProblemSize('hpl'), which assumes that each process in the pool % has 256 MB of memory available. This is expected to be smaller than % the actual memory available. % % Details of the HPC Challenge benchmarks can be found at % www.hpcchallenge.org and the specific Class 2 specs are linked off % that page. (At the time of writing, the specs are linked at % www.hpcchallenge.org/class2specs.pdf.) % % Examples: % % % Without a matlabpool open % tic; hpccLinpack; toc % Data size: 0.108665 GB % Performance: 16.351622 GFlops % Elapsed time is 2.791896 seconds. % % % With a local matlabpool of size 4 % tic; hpccLinpack; toc % Data size: 0.434774 GB % Performance: 18.650758 GFlops % Elapsed time is 21.647003 seconds. % % See also: hpccGetProblemSize, matlabpool

% If no size provided then get a default size if nargin < 1 m = hpccGetProblemSize( 'hpl' ); end spmd % Create a distributed matrix in the 2d block cyclic distribution and a % distributed column vector in 1d A = codistributed.randn(m, m, codistributor2dbc); b = codistributed.rand(m, 1);

% Time the solution of the linear system tic x = A\b; t = toc; % Need to convert to a 1d distribution for the checking code below A = redistribute(A, codistributor1d); % Compute scaled residuals r1 = norm(A*x-b,inf)/(eps*norm(A,1)*m); r2 = norm(A*x-b,inf)/(eps*norm(A,1)*norm(x,1)); r3 = norm(A*x-b,inf)/(eps*norm(A,inf)*norm(x,inf)*m); % This test is specified in the benchmark definition if max([r1 r2 r3]) > 16 error('Failed the HPC HPL Benchmark'); end end % Performance in gigaflops datasize = 8*m2/(10243); perf = (2/3*m3 + 3/2*m2)/max([t{:}])/1.e9; fprintf('Data size: %f GB\nPerformance: %f GFlops\n', 8*m2/(10243), perf);