**Known MATLAB issues**

*Matlab 2014b and above can experience the following issues when run on BioHPC systems. BioHPC has reported these issues to the MATLAB developer, but no fix is currently available.*

*The built-in `mkdir` function to create directories gives a 'Permission Denied' error on the /project directory.*

Please use the 'system('mkdir -p mydir')' command instead of mkdir.

*If editor sessions were open when MATLAB was closed, it will fail to load with a library error when next run on a webGUI or thin-client session.*

Please start MATLAB with the -softwareopengl option 'matlab -softwareopengl' as a workaround.

* Automatically handled by Matlab, use of vector operations in place of for-loops.

* Acceleration MATLAB code with very little code changes, job will running on your workstation, thin-client or a reserved compute node

- Parallel for Loop (parfor)

- Spmd (Single Program Multiple Data)

- Pmode (Interactive environment for spmd development)

- Parallel Computing with GPU

* Directly submit matlab job to BioHPC cluster

- Matlab job scheduler integrated with SLURM

• Allow several MATLAB workers to execute individual loop iterations simultaneously.

• The only difference in parfor loop is the keyword parfor instead of for. When the loop begins, it opens a parallel pool of MATLAB sessions called workers for executing the iterations in parallel.

**Example 1: Estimating an Integration**

**quad_fun.m (serial version)**

function q = quad_fun (n, a, b) q = 0.0; w=(b-a)/n; for i = 1 : n x = (n-i) * a + (i-1) *b) /(n-1); fx = 0.2*sin(2*x); q = q + w*fx; end return endrun from command line:

>> tic q=quad_fun(120000000, 0.13, 1.53); t1=toc t1 = 5.7881

**quad_fun_parfor.m (parfor version)**

function q = quad_fun_parfor (n, a, b) q = 0.0; w=(b-a)/n; parfor i = 1 : n x = (n-i) * a + (i-1) *b) /(n-1); fx = 0.2*sin(2*x); q = q + w*fx; end return endrun from command line :

>> parpool('local'); Starting parallel pool(parpool) using the 'local' profile ... connected to 12 workers ... >> tic q=quad_fun_parfor(120000000, 0.13, 1.53); t2=toc t2 = 0.9429

**12 workers, Speedup = t1/t2 = 6.14x**

**Limitations of parfor**

- No Nested parfor loops
- Loop variable must be increasing integers
- Loop iterations must be independent

- single program -- the identical code runs on multiple workers.
- multiple data -- each worker can have different, unique data for that code.
- numlabs – number of workers.
- labindex -- each worker has a unique identifier between 1 and numlabs.
- point-to-point communications between workers : labsend, labreceive, labsendrecieve
- Ideal for: 1) programs that takes a long time to execute. 2) programs operating on large data set.

spmd labdata = load([‘datafile_’ num2str(labindex) ‘.ascii’]) % multiple data result = MyFunction(labdata); % single program end

**Example 1: Estimating an Integration**

**quad_fun_spmd.m (spmd version)**

a = 0.13; b = 1.53; n = 120000000; spmd aa = (labindex - 1) / numlabs * (b-a) + a; bb = labindex / numlabs * (b-a) + a; nn = round(n / numlabs) q_part = quad_fun(nn, aa, bb); end q = sum([q_part{:}]);run from command line :

8.26x >> tic quad_fun_spmd t3=toc t3 = 0.7006

**12 workers, Speedup = t1 / t3 = 8.26x**

**Q: why better performance compares to parfor version ?**

A: 12 communications between client and workers to update q, whereas in parfor, it needs 120,000,000 communications between client and workers.

**Distributed Array**

- Distributed Array - Data distributed from client and access readily on client. Data always distributed along the last dimension, and as evenly as possible along that dimension among the workers.
- Codistributed Array – Data distributed within spmd. Array created directly (locally) on worker.

**Matrix Multiply (A*B) by Distributed Array**

parpool(‘local’); A = rand(3000); B = rand(3000); a = distributed(A); b = distributed(B); c = a*b; % run on workers automatically as long as c is of type distributed delete(gcp);

**Matrix Multiply (A*B) by Distributed Array**

parpool(‘local’); A = rand(3000); B = rand(3000); spmd u = codistributed(A, codistributor1d(1)); % by row v = codistributed(B, codistributor1d(2)); % by column w = u * v; end delete(gcp);

**Example 2 : linear Solver (Ax = b)**

**linearSolver.m (serial version)**

n = 10000; M = rand(n); X = ones(n, 1); A = M + M’; b = A * X; u = A \ b;run from command line :

>>tic;linearSolver;t1=toc t1 = 8.4054

**linearSolverSpmd.m (spmd version)**

n = 10000; M = rand(n); X = ones(n, 1); spmd m = codistributed(M, codistributor(‘1d’, 2)); % distribute one portion of m to each MATLAB worker x = codistributed(X, codistributor(‘1d’, 1)); % distribute one portion of x to each MATLAB worker A = m +m’; b = A * x; utmp = A \ b; end u1 = gather(utmp); % gathering u1 from all MATLAB workers to MATLAB clientrun from command line :

>>parpool('local'); Starting parallel pool(parpool) using the 'local' profile ... connected to 12 workers >>tic;linearSolverSpmd;t2=toc t2 = 16.5482

**Q: why spmd version needs more time for job completion ?**

A: It needs extra time for distribute/consolidate large data and tranfer them to/from different workers

**Factors reduce the speedup of parfor and spmd**

- Computation inside the loop is simple.
- Memory limitations. (create more data compare to serial code)
- Transfer data is time consuming
- Unbalanced computational load
- Synchronization

**Example 3 : Contrast Enhancement** (by John Burkardt @ FSU)

**contract_enhance.m **

function y = contrast_enhance (x) x = double (x); n = size (x, 1); x_average = sum ( sum (x(:,:))) /n /n ; s = 3.0; % the contrast s should be greater than 1 y = (1.0 – s ) * x_average + s * x((n+1)/2, (n+1)/2); return end

x = imread(‘surfsup.tif’); yl = nlfilter (x, [3,3], @contrast_enhance); y = uint8(y);

**contrast_spmd.m (spmd version)**

x = imread(‘surfsup.tif’); xd = distributed(x); spmd xl = getlocalPart(xd); xl = nlfilter(xl, [3,3], @contrast_enhance); end y = [xl{:}];

**12 workers, running time is 2.01 s, Speedup = 7.19x**

**Problem** : When the image is divided by columns among the workers, arti?cial internal boundaries are created !

**Reason **: Zero padding at edges when applying sliding-neighborhood operation.

**Solution**: Build up communication between workers.

**contrast_MPI.m**

x = imread('surfsup.tif'); xd = distributed(x); spmd xl = getLocalPart ( xd ); % find out previous & next by labindex if ( labindex ~= 1 ) previous = labindex - 1; else previous = numlabs; end if ( labindex ~= numlabs ) next = labindex + 1; else next = 1; end % attach ghost boundaries column = labSendReceive ( previous, next, xl(:,1) ); if ( labindex < numlabs ) xl = [ xl, column ]; end column = labSendReceive ( next, previous, xl(:,end) ); if ( 1 < labindex ) xl = [ column, xl ]; end xl = nlfilter ( xl, [3,3], @contrast_enhance ); % remove ghost boundaries if ( 1 < labindex ) xl = xl(:,2:end); end if ( labindex < numlabs ) xl = xl(:,1:end-1); end xl = uint8 ( xl ); end y = [ xl{:} ];

**12 workers, running time is 2.01 s, Speedup = 7.23x**

* pmode allows the interactive parallel execution of MATLAB® commands. pmode achieves this by defining and submitting a communicating job, and opening a Parallel Command Window connected to the workers running the job. The workers then receive commands entered in the Parallel Command Window, process them, and send the command output back to the Parallel Command Window. Variables can be transferred between the MATLAB client and the workers.

**
**

**Q: How many workers can I use for MATLAB parallel pool ?**

**Enable GPU computing of Matlab on BioHPC cluster.**

Step 1 : reserve a GPU node by remoteGPU or webGPU

Step 2 : inside terminal, type in to enable the hardware rendering.

`export CUDA_VISIBLE_DEVICES=“0”`

**You can lauch Matlab directly if you have GPU card on your workstation.**

**Example 2 : linear Solver (Ax = b)**

**linearSolverGPU.m (GPU version)**

n = 10000; M = rand(n); X = ones(n, 1); % Copy data from RAM to GPU ( ~ 0.3 s) Mgpu = gpuArray(M); Xgpu = gpuArray(X); Agpu = Mgpu + Mgpu’; Bgpu = Agpu * Xgpu; ugpu = Agpu \ bgpu % Copy data from GPU to RAM ( ~0.0002 s) u = gather(ugpu);

run from command line :

>>tic;linearSolverGPU;t3=toc t3 = 3.3101

**Speedup = t1 / t3 = 2.54x**

**Setup MATLAB environment**

Home -> ENVIRONMENT -> Parallel -> Manage Cluster Profiles

Add -> Import Cluster Profiles from file -> select profile file from (based on your matlab version):

/project/apps/MATLAB/profile/nucleus_r2016a.settings

/project/apps/MATLAB/profile/nucleus_r2015b.settings

/project/apps/MATLAB/profile/nucleus_r2015a.settings

/project/apps/MATLAB/profile/nucleus_r2014b.settings

/project/apps/MATLAB/profile/nucleus_r2014a.settings

/project/apps/MATLAB/profile/nucleus_r2013b.settings

/project/apps/MATLAB/profile/nucleus_r2013a.settings

** **

**Test MATLAB environment (optional)**

**
**

After initial setup, you should have two Cluster Profiles ready for Matlab parallel computing:

` “local"`

– for running job on your workstation, thin client or on any single compute node of BioHPC cluster (use Parallel Computing toolbox)

`“nucleus_r<version No.>” `

– for running job on BioHPC cluster with multiple nodes (use MDCS)

**Setup Slurm Environment from Matlab Command line**

ClusterInfo.setQueueName(‘128GB’); % use 128 GB partition ClusterInfo.setNNode(2); % request 2 nodes ClusterInfo.setWallTime(‘1:00:00’); % setup time limit to 1 hour ClusterInfo.setEmailAddress(‘yi.du@utsouthwestern.edu’); % email notification

**Check Slurm Environment from Matlab Command line**

ClusterInfo.getQueueName(); ClusterInfo.getNNode(); ClusterInfo.getWallTime(); ClusterInfo.getEmailAddress();

wait(job, ‘finished’); % Block session until job has finished get(job, ‘State’); % Occasionally checking on state of job

results = fetchOutputs(job); % load job results results = load(job); % load job results, only valid when submit job as a script delete(job); % delete job related data

**You can also open job monitor from Home->Parallel->Monitor Jobs**

**Example 1: Estimating an Integration**

**quad_submission.m**

% setup Slurm environment ClusterInfo.setQueueName(‘super’); ClusterInfo.setNNode(2); ClusterInfo.setWallTime(‘00:30:00’); % submit job to BioHPC cluster job = batch(@quad_fun, 1, {120000000, 0.13, 1.53}, ‘Pool’, 63, ‘profile’, ‘nucleus_r2015a’); % wait wait(job, ‘finished’); % load results after job completion Results = fetchOutputs(job);

**check Slurm settings from Matlab command window**

**check Job status from terminal ( squeue -u <username>)**

**load results**

**16 workers cross 2 nodes, running time is 24s**

Recall that the serial job only needs 5.7881 seconds, MDCS needs more time for example 1 as it is not designed for small jobs.

**Example 4 : Vessel Extraction**

parfor i = 1 : length(numSubImages) I = im2double(Bwall(:,:i)); % load binary image allS{i} = skeleton(I); % find vessel by using fast matching method end

**performance evaluation ( running time v.s. number of workers/nodes)**

Job running on 4 nodes (96 workers) requires minimum computational time, **Speedup = 28.3 x** @ 4 nodes.

**time needed for each single image various**

**Why linear speedup is impossible to obtain ?**

- overhead caused by load balancing, synchronization, communication and etc..
- limited by the longest (single/serial) job