MATLAB: exercises

hpc2n · Aug 19, 2024 · 923030b · 923030b
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diff --git a/docs/software.md b/docs/software.md
@@ -6,12 +6,12 @@
 
 Matlab is available through the Menu bar if you are using ThinLinc client (recommended). Additionally, you can load 
 a Matlab module on a Linux terminal on Kebnekaise. Details for these two options can be found 
-[here](https://www.hpc2n.umu.se/resources/software/matlab). 
+[here](https://www.hpc2n.umu.se/resources/software/matlab){:target="_blank"}. 
 
 ### First time configuration
 
 The first time you access Matlab on Kebnekaise, you need to configure it by following these guidelines 
-[Configuring Matlab](https://www.hpc2n.umu.se/resources/software/configure-matlab-2018)
+[Configuring Matlab](https://www.hpc2n.umu.se/resources/software/configure-matlab-2018){:target="_blank"}.
 
 ### Tools for efficient simulations 
 
@@ -33,25 +33,24 @@ Chart flow for a more efficient Matlab code using existing tools (adapted from[^
 
 !!! Note "Exercise 2: Matlab parallel job"
 
-    * PARFOR folder contains an example of a parallelized loop with the "parfor" directive. A pause()
+    * PARFOR folder contains an [example](https://raw.githubusercontent.com/hpc2n/intro-course/master/exercises/MATLAB/PARFOR/parallel_example.m){:target="_blank"} of a parallelized loop with the "parfor" directive. A pause()
     function is included in the loop to make it heavy. This function can be
-    submitted to the queue by running the script [submit.m](https://raw.githubusercontent.com/hpc2n/intro-course/master/exercises/MATLAB/PARFOR/submit.m) in the MATLAB GUI.
+    submitted to the queue by running the script [submit.m](https://raw.githubusercontent.com/hpc2n/intro-course/master/exercises/MATLAB/PARFOR/submit.m){:target="_blank"} in the MATLAB GUI.
     The number of workers can be set by replacing the string *FIXME* (in the "submit.m"
     file) with the number you desire. 
-
-      Try different values for the number of workers from 1 to 28 and take a note
+      Try different values for the number of workers from 1 to 10 and take a note
       of the simulation time output at the end of the simulation. Where does the
       code achieve its peak performance? 
 
-    * SPMD folder presents an example of a parallelized code using SPMD paradigm. You
-    can submit this job to the queue through the MATLAB GUI.
+    * SPMD folder presents an example of a parallelized code using [SPMD](https://raw.githubusercontent.com/hpc2n/intro-course/master/exercises/MATLAB/SPMD/spmdex.m){:target="_blank"} paradigm. Submit this job to the queue through the MATLAB GUI. This
+    example illustrates the use of *parpool* to run parallel code in a more interactive manner.
 
-!!! Note "Exercise 2: Matlab GPU job"
+!!! Note "Exercise 3: Matlab GPU job"
 
     GPU folder contains a test case that computes a Mandelbrot set both 
-    on CPU [mandelcpu.m](https://raw.githubusercontent.com/hpc2n/intro-course/master/exercises/MATLAB/GPU/mandelcpu.m) 
-    and on GPU [mandelgpu.m](https://raw.githubusercontent.com/hpc2n/intro-course/master/exercises/MATLAB/GPU/mandelgpu.m). You can submit the jobs through 
-    the MATLAB GUI using the [submitcpu.m](https://raw.githubusercontent.com/hpc2n/intro-course/master/exercises/MATLAB/GPU/submitcpu.m) and [submitgpu.m](https://raw.githubusercontent.com/hpc2n/intro-course/master/exercises/MATLAB/GPU/submitgpu.m)  files. 
+    on CPU [mandelcpu.m](https://raw.githubusercontent.com/hpc2n/intro-course/master/exercises/MATLAB/GPU/mandelcpu.m){:target="_blank"} 
+    and on GPU [mandelgpu.m](https://raw.githubusercontent.com/hpc2n/intro-course/master/exercises/MATLAB/GPU/mandelgpu.m){:target="_blank"}. You can submit the jobs through 
+    the MATLAB GUI using the [submitcpu.m](https://raw.githubusercontent.com/hpc2n/intro-course/master/exercises/MATLAB/GPU/submitcpu.m){:target="_blank"} and [submitgpu.m](https://raw.githubusercontent.com/hpc2n/intro-course/master/exercises/MATLAB/GPU/submitgpu.m){:target="_blank"}  files. 
 
     The final output if everything ran well are two .png figures
     which display the timings for both architectures. Use the "eom" command on the

diff --git a/exercises/MATLAB/GPU/mandelcpu.m b/exercises/MATLAB/GPU/mandelcpu.m
@@ -1,65 +1,39 @@
 function [cpuTime]=mandelcpu
 maxIterations = 1000;
-
 gridSize=1000;
 
 xlim = [-0.748766713922161, -0.748766707771757];
-
 ylim = [ 0.123640844894862,  0.123640851045266];
 
 t = tic();
 
 x = linspace( xlim(1), xlim(2), gridSize );
-
 y = linspace( ylim(1), ylim(2), gridSize );
 
 [xGrid,yGrid] = meshgrid( x, y );
-
 z0 = xGrid + 1i*yGrid;
-
 count = ones( size(z0) );
 
-
-
 % Calculate
-
 z = z0;
 
 for n = 0:maxIterations
-
     z = z.*z + z0;
-
     inside = abs( z )<=2;
-
     count = count + inside;
-
 end
 
-
-
 % show
 
 count = log( count );
-
 cpuTime = toc( t );
 
-
-
 figure;
-
 fig = gcf;
-
 fig.Position = [200 200 600 600];
-
 imagesc( x, y, count );
-
-
-
 axis image
-
 colormap( [jet();flipud( jet() );0 0 0] );
-
 title( sprintf( '%1.2fsecs (CPU)', cpuTime ) );
-
 print('out-cpu','-dpng');
-end
+end
diff --git a/exercises/MATLAB/GPU/mandelgpu.m b/exercises/MATLAB/GPU/mandelgpu.m
@@ -1,65 +1,40 @@
 function [nativeGPUTime]=mandelgpu
 maxIterations = 1000;
-
 gridSize=1000;
 
 xlim = [-0.748766713922161, -0.748766707771757];
-
 ylim = [ 0.123640844894862,  0.123640851045266];
 
 t = tic();
 
 x = gpuArray.linspace( xlim(1), xlim(2), gridSize );
-
 y = gpuArray.linspace( ylim(1), ylim(2), gridSize );
 
 [xGrid,yGrid] = meshgrid( x, y );
-
 z0 = complex( xGrid, yGrid );
-
 count = ones( size(z0), 'gpuArray' );
 
-
-
 % Calculate
-
 z = z0;
 
 for n = 0:maxIterations
-
     z = z.*z + z0;
-
     inside = abs( z )<=2;
-
     count = count + inside;
-
 end
 
 count = log( count );
 
-
-
 % show
 
 count = gather( count ); % Fetch the data back from the GPU
-
 nativeGPUTime = toc( t );
-
 figure;
-
 fig = gcf;
-
 fig.Position = [200 200 600 600];
-
 imagesc( x, y, count );
-
-
-
 axis image
-
 colormap( [jet();flipud( jet() );0 0 0] );
-
 title( sprintf( '%1.2fsecs (GPU)', nativeGPUTime ) );
-
 print('out-gpu','-dpng');
-end
+end
diff --git a/exercises/MATLAB/GPU/submitcpu.m b/exercises/MATLAB/GPU/submitcpu.m
@@ -1,7 +1,7 @@
 % Get a handle to the cluster
-% See the page for configuring and setup of MATLAB 2018b for details
+% See the page for configuring and setup of MATLAB > 2018b for details
 c=parcluster('kebnekaise')
-% Run the jobs on 4 workers
+% Run the job on CPU
 j = c.batch(@mandelcpu, 1, {})
 % Wait till the job has finished. Use j.State if you just want to poll the
 % status and be able to do other things while waiting for the job to finish.

diff --git a/exercises/MATLAB/GPU/submitgpu.m b/exercises/MATLAB/GPU/submitgpu.m
@@ -1,5 +1,5 @@
 % Get a handle to the cluster
-% See the page for configuring and setup of MATLAB 2018b for details
+% See the page for configuring and setup of MATLAB > 2018b for details
 c=parcluster('kebnekaise')
 % Run the jobs on 4 workers
 j = c.batch(@mandelgpu, 1, {})

diff --git a/exercises/MATLAB/PARFOR/parallel_example.m b/exercises/MATLAB/PARFOR/parallel_example.m
@@ -1,8 +1,7 @@
 function t = parallel_example(iter) 
 t0 = tic; 
 
-parfor idx = 1:iter 
-    A(idx) = idx; 
+parfor idx = 1:iter  
     pause(2) 
 end 
 

diff --git a/exercises/MATLAB/PARFOR/submit.m b/exercises/MATLAB/PARFOR/submit.m
@@ -1,8 +1,8 @@
 % Get a handle to the cluster
 % See the page for configuring and setup of MATLAB for details
 c=parcluster('kebnekaise')
-% Run the jobs on 4 workers
-j = c.batch(@parallel_example, 1, {32}, 'pool', *FIXME*)
+% Run the jobs on X workers
+j = c.batch(@parallel_example, 1, {7}, 'pool', *FIXME*)
 % Wait till the job has finished. Use j.State if you just want to poll the
 % status and be able to do other things while waiting for the job to finish.
 j.wait