Matlab ====== Matlab is a bit of a problem child on the Tardis. While the `MATLAB Distributed Computing Server`_ product aims to implement a compatibility layer to a number of PBS based clusters it just doesn't work reliably for a number of reasons. Because there is only a limited number of shared licenses available it's also not feasible to run an arbitrary number of Matlab sessions in the form of jobs. A workaround is to "compile" a script and create a standalone redistribution environment, which does not require a license to run. Different Matlab versions are available via environment modules. You can list them with :program:`module avail matlab` and activate a specific version with :program:`module load matlab/`. Regular sessions ---------------- **If** there are free licenses available and you just need a quick way to spawn a single Matlab session, there is nothing wrong with just running Matlab as is. This might especially be useful if you simply need a node with lot's of memory or if you want to test your code. In an interactive job you could simply enter "matlab" and it will warn you about there not being a display available and start in command line mode. .. code-block:: bash [krause@master ~] qsub -i -l mem=100gb -q testing qsub: waiting for job 5395814.master.tardis.mpib-berlin.mpg.de to start qsub: job 5395814.master.tardis.mpib-berlin.mpg.de ready [krause@ood-9.tardis.mpib-berlin.mpg.de ~] module load matlab/R2012b [krause@ood-9.tardis.mpib-berlin.mpg.de ~] matlab < M A T L A B (R) > Copyright 1984-2012 The MathWorks, Inc. R2012a (7.14.0.739) 64-bit (glnxa64) February 9, 2012 To get started, type one of these: helpwin, helpdesk, or demo. For product information, visit www.mathworks.com. >> _ In a job context you would just run :program:`matlab -r main` with main.m containing your script: .. code-block:: bash [krause@master ~] echo "module load matlab/R2014b; matlab -r main" | qsub -d. .. important: Always check `http://lipsupport.mpib-berlin.mpgde/licstat`_ to see if there is an available license. Compiling --------- Once you leave the testing stage and would like to spawn an arbitrary number of Matlab jobs/processes you have to compile your script with :program:`mcc`. A reliable pattern is to create a main file :file:`project.m` that contains a function with the same name and expects some arguments you would like to loop over. A little like this maybe: .. code-block:: matlab function project(subject_id, sigma) % % my main program implementing foo % % arguments % --------- % % subject_id: a string encoding the subject id % sigma: a string encoding values for sigma sigma = str2num(sigma); repmat(cellstr(subject_id), 1, sigma) Running :program:`mcc -m project.m` would then "compile" (or rather encrypt and package) your function and output a system dependent binary named :file:`project` and a wrapper script :file:`run_project.sh`. To run it you now have to combine the wrapper script, the location of a Matlab Compile Runtime or the local installation path of the Matlab instance, that was used by mcc, and a sufficient number of arguments for the function project(). Example: .. code-block:: matlab [krause@master ~] mcc -m project.m [krause@master ~] ./run_project.sh /opt/matlab/interactive 42 5 ------------------------------------------ Setting up environment variables --- LD_LIBRARY_PATH is .:/opt/matlab/interactive/runtime/glnxa64:/opt/matlab/interactive/bin/glnxa64:/opt/matlab/interactive/sys/os/glnxa64:/opt/matlab/interactive/sys/java/jre/glnxa64/jre/lib/amd64/native_threads:/opt/matlab/interactive/sys/java/jre/glnxa64/jre/lib/amd64/server:/opt/matlab/interactive/sys/java/jre/glnxa64/jre/lib/amd64/client:/opt/matlab/interactive/sys/java/jre/glnxa64/jre/lib/amd64 Warning: No display specified. You will not be able to display graphics on the screen. ans = '42' '42' '42' '42' '42' [krause@master ~] To include toolboxes in your script you have to add them during the compile step so they get included in your package. Matlab built-in toolboxes such as signal processing or statistics are detected automatically by scanning the functions used in your script and don't need to be added explicitly. Compiled scripts can't use the :program:`addpath()` function at runtime. You can guard those calls however with the function :program:`isdeployed()`, which will return 1 when Matlab detects that it runs as a compiled script and 0 otherwise. Example: Suppose you collect your project library in a toolbox called project, which in turn uses the function :program:`normrnd()` from the statistics package: .. code-block:: bash [krause@master ~] cat matlab/tools/project/myrnd.m function X = myrnd(arg) X = normrnd(0, 1, arg, arg); You can then either use the "-a" or the "-I" switch of mcc to add your own toolbox. + **-a** will add the functions or directories listed directly to the compiled package/archive + **-I** (uppercase i) will add the location to the mcc search path so it get's included implicitly Both options should work fine. The example below uses mcc from matlab R2014b, but you can use any version. The important part is to use the same Matlab version as MCR upon script invocation with :program:`run_project.sh`. .. code-block:: matlab [krause@master ~] module load matlab/R2014b [krause@master ~] cat project.m function project(arg1) myrnd(str2num(arg1)) [krause@master ~] mcc -m project.m -a matlab/tools/project [...] [krause@master ~] ./run_project.sh /opt/matlab/R2014b 3 ------------------------------------------ Setting up environment variables --- LD_LIBRARY_PATH is .:/opt/matlab/R2014b/runtime/glnxa64:/opt/matlab/R2014b/bin/glnxa64:/opt/matlab/R2014b/sys/os/glnxa64:/opt/matlab/R2014b/sys/opengl/lib/glnxa64 ans = 0.5377 0.8622 -0.4336 1.8339 0.3188 0.3426 -2.2588 -1.3077 3.5784 [krause@master ~] .. note:: You only have to compile your project once and can then use it any number of times. Matlab extracts your package to a shared hidden folder called `.mcrCache`. Those folders sometimes get corrupted by Matlab, especially when multiple jobs start at exactly the same time. The only workaround so far is to add a sleep 1s between qsub calls and hope there is no collision. Also, it makes sense to regularly remove those directories. But make sure all your jobs have finished before removing them with :file:`rm -rf .mcrCache*`. SPM --- There are environment modules available to choose from and activate SPM12. At the time of writing these are: .. code-block:: bash [krause@master ~] module avail spm12 -------------- /opt/environment/modules --------------- spm12/6225 spm12/6470 spm12/6685 spm12/6906 spm12/7219 Usually users are exporting a number of batch files with the spm gui on their local machine, change the paths to reflect the names on the Tardis and then call :program:`run_spm12.sh` with the **batch** parameter for each batch file. Example: segmentation for a number of nifti images. The file batch.template contains the string :file:`%%IMAGE%%` as a placeholder so we can easily replace it with the current image path and create a number of new batches from a single template: .. code-block:: bash #!/bin/bash i=0 for image in tp2/Old/*.nii ; do fullpath=$PWD/$image sed "s#%%IMAGE%%#$fullpath#" batch.template > batch_${i}.m echo "module load spm12 ; run_spm12.sh /mcr batch $PWD/batch_${i}.m" | qsub -d. i=$((i+1)) done **SPM Memory** By default, SPM uses very conservative memory settings. Combined with large time-series data and a networked file system this can quickly result in inefficient I/O patterns due to unnecessary data chunking and hasty saving of intermediate results. (thank you Nir Moneta for finding and reporting this!) For that reason, our pre-compiled SPM modules now contain these new default values: .. code-block:: matlab global defaults; defaults.stats.maxmem = 2^32; % increases chunk size defaults.stats.resmem = true; % do not save intermediate steps If you need different defaults (possibly up to `2^38` / 256GB ), you need to include a custom :file:`spm_my_defaults.m` and re-compile SPM12. **(Re-)Compiling** Sometimes it *might be* necessary to recompile the spm toolbox yourself, for instance if you need a specific version, if you want to add external toolboxes to SPM (e.g. cat12) or if you need to change some spm defaults. .. code-block:: matlab [krause@master ~] matlab Warning: No display specified. You will not be able to display graphics on the screen. < M A T L A B (R) > Copyright 1984-2012 The MathWorks, Inc. R2012a (7.14.0.739) 64-bit (glnxa64) February 9, 2012 To get started, type one of these: helpwin, helpdesk, or demo. For product information, visit www.mathworks.com. >> %addpath(genpath('/home/mpib/krause/matlab/tools/spm12')) % overkill >> addpath('/home/mpib/krause/matlab/tools/spm12') >> addpath('/home/mpib/krause/matlab/tools/spm12/config') >> spm_make_standalone() [... lot's of output and warnings ...] Processing /opt/matlab/R2012a/toolbox/matlab/mcc.enc [... lot's of output and warnings ...] Generating file "/home/mpib/krause/matlab/tools/spm_exec/readme.txt".Gen Generating file "/home/mpib/krause/matlab/tools/spm12/../spm_exec/run_spm12.sh". >> This should create a folder :file:`spm_exec` or :file:`standalone` below the spm toolbox location containing the fresh :program:`spm12` and :program:`run_spm12.sh` which you can then use in your jobs just like above. Fieldtrip --------- To properly add the fieldtrip toolbox we have to jump through some more hoops. For now the only reliable and flexible way is to run :program:`mcc()` from within a Matlab session and make sure to run :program:`ft_defaults()` first. Also, some of the provided mex files won't work out of the box, so we have to recompile them using :program:`ft_compile_mex()`. This however stumbles over some external C file called :file:`CalcMD5.c` using non-standard comments. The following Matlab Script has been successfully used to create a compiled script from a :file:`main.m` file, which relies on internal fieldtrip functions. .. code-block:: matlab % setup path basepath='/home/mpib/krause/matlab/tools/ConMemEEGTools/' addpath([basepath, '/fieldtrip-20150930']) ft_defaults() % re-compile mex functions (this has to be done only once per fieldtrip version) % "fix" the CalcMD5.c file system(['sed -i ''s#//.*##g'' ', basepath, '/fieldtrip-20150930/external/fileexchange/CalcMD5.c']) % and compile ft_compile_mex(true) % build the runtime environment mcc('-m', 'main.m') Efficient Saving ---------------- We have noticed a couple of times now that Matlab's :program:`save()` function can lead to undesirable performance issues. This occurs especially when a large number of jobs try to save big objects at the same time. This is a bit of a complex issue and I will try to go through them in this section using some examples. You are free to adapt some of the things highlighted here in your code, but you don't have to. There are two aspects to consider when saving larger objects with Matlab: 1. shared Input/Output Your home directories are connected via a network file system (NFS). While NFS bandwidth, storage size and IO performance is matched to our cluster size, its capacity can be quickly saturated. We recommend to carefully watch your saving and loading operations in your scripts, especially when there are a large number of jobs accessing files at the same time. An indication for bad IO performance is a job cpu efficiency value considerably less than 1.0 (check the `Tardis Website`_ for that value). The worst case for a networked file system are a huge number of tiny file operations, like reading or writing a few bytes or constantly checking for changed file attributes (size, modification time and so on). Try to avoid these situations by only writing large chunks of data at a time. 2. Compression Sometimes it's much faster to compress large chunks of data **before** sending it over the wire (i.e. saving an object). Also, storage size is a valuable resource and you should therefore consider compressing data with Matlab. With objects larger than 2GB unfortunately, people usually resort to saving with the Matlab File format v7.3. The default behaviour of Matlab's :program:`save(.., '-v7.3')` is suboptimal and you might want to save your objects differently. The following examples highlight why this is necessary and also why it's not trivial to just recommend a better alternative. Consider two extreme variants of data, a truly random matrix and a matrix full of zeros. Both with a size of 5GB: .. code-block:: bash R = randn(128,1024,1024,5); Z = zeros(128,1024,1024,5); On a drained Tardis, saving these two objects naively takes **forever** .. code-block:: bash tic; save('~/test.mat', ['R'], '-v7.3'); toc; Elapsed time is 346.156656 seconds. tic; save('~/test.mat', ['Z'], '-v7.3'); toc; Elapsed time is 145.100863 seconds There are two reasons for this. Firstly, the compression algorithm Matlab uses appears to be quite slow. And even with perfectly compressible data (Z can be easily expressed with 5 bytes), Matlab needs more than two minutes to save the object, while **still** creating a considerable amount of IO operations. There is an option to disable compression (which is generally not advisable anyway). But even then saving the "R" object takes more than 3 minutes: .. code-block:: bash tic; save('~/test.mat', ['R'], '-v7.3', '-nocompression'); toc Elapsed time is 220.320252 seconds. With version 7.3 Matlab changed the binary format for object serialization to something based on HDF5. Luckily, the lower level functions for HDF5 file manipulations are available to you. For this simple case, a matrix of numbers saving directly to HDF5 could look like this: .. code-block:: bash >> h5create('test.h5', '/R', size(R), 'ChunkSize', [128,1024,1024,1]); >> tic; h5write('test.h5', '/R', R); toc; Elapsed time is 15.225184 seconds. In comparison to the naive :program:`save()`, we are faster by a factor of 22. But this is not the whole story unfortunately. One downside to this approach is connected to the internal structure of HDF5 objects. Saving a struct with multiple, nested objects of different types (think string annotations, integer arrays and float matrices) is much more tedious. Timothy E. Holy wrote a wrapper script, that kind of automatically creates the necessary structures and published the function :program:`savefast` on `Matlab's filexchange`_. It has a similar interface to the original save and can be used as a drop-in replacement in many cases. You need to add the function to your search path of course. .. code-block:: bash >> tic; savefast('test.mat', 'R'); toc Elapsed time is 16.242634 seconds. Unfortunately, we can't just stop here, because savefast by default is not compressing anything and the whole point of using HDF5 is because we need to store **large** matrices, bigger than 2GB. Blindly storing them to storage will waste storage space and saturates the disk arrays with only 4 concurrent jobs (based on the 15s benchmark above). In other words, using some kind of compression is necessary - unless you **know** that you generated random or close to random data. To highlight the differences that come with compression level, let's look at the 5GB of zeros stored in Z again. Matlab is a bit faster in compressing and saving with :program:`save()`. But it still takes 145 seconds. You might be tempted to combine the savefast() approach and simply compress with something fast like ::program:`gzip` afterwards. This would actually speed things up *and* save a lot of space: .. code-block:: bash >> Z = zeros(128,1024,1024,5); >> tic; save('~/test.mat', ['Z'], '-v7.3'); toc; Elapsed time is 145.100863 seconds. >> tic; savefast('~/test2.mat', ['Z']); system('gzip test2.mat'); toc; Elapsed time is 53.527696 seconds. -rw-r--r-- 1 krause domain users 31M May 16 17:57 test.mat -rw-r--r-- 1 krause domain users 5.0M May 16 18:00 test2.mat.gz On a first glance this appears to be faster and more efficient. But this approach does not scale well to multiple jobs, as it saves the whole 5GB uncompressed, then reads it all in again (from NFS cache, but still over the network) and then, after compression, saves it back to disk. Using HDF5's low level function you can fine tune the compression level from 0 (lowest and fastest) to 9 (highest and slowest). If you set the compression level however, you also need to set a chunk size, probably because compression is done chunk-wise. Recommending a generic compression level is hard and depends very much on your data. Of course you don't want to waste time by maximizing the compression ratio, gaining only a couple of megabytes, but you also don't want to waste bandwidth by saving overhead data. Consider our zeros again: .. code-block:: bash >> h5create('test.h5', '/Z', size(Z), 'ChunkSize', [128,1024,1024,1], 'Deflate', 9); >> tic; h5write('test.h5', '/Z', Z); toc; Elapsed time is 35.333514 seconds. >> ls -lh test.h5 -rw-r--r-- 1 krause domain users 5.0M May 16 18:35 test.h5 >> h5create('test.h5', '/Z', size(Z), 'ChunkSize', [128,1024,1024,1], 'Deflate', 6); >> tic; h5write('test.h5', '/Z', Z); toc; Elapsed time is 36.646509 seconds. >> ls -lh test.h5 -rw-r--r-- 1 krause domain users 5.0M May 16 18:36 test.h5 >> h5create('test.h5', '/Z', size(Z), 'ChunkSize', [128,1024,1024,1], 'Deflate', 3); >> tic; h5write('test.h5', '/Z', Z); toc; Elapsed time is 20.002455 seconds. >> ls -lh test.h5 -rw-r--r-- 1 krause domain users 23M May 16 18:37 test.h5 >> h5create('test.h5', '/Z', size(Z), 'ChunkSize', [128,1024,1024,1], 'Deflate', 0); >> tic; h5write('test.h5', '/Z', Z); toc; Elapsed time is 50.847998 seconds. >> ls -lh test.h5 -rw-r--r-- 1 krause domain users 5.1G May 16 18:38 test.h5 Here I picked a chunk size of 1GB, compressing with levels 9, 6, 3, and 0. Not surprisingly the optimal value in this group is somewhere in the middle (3) and it only takes 20seconds to save the data, while still reducing the 5GB file to 23MB. .. _`MATLAB Distributed Computing Server`: http://de.mathworks.com/help/mdce/index.html .. _`Tardis Website`: https://tardis.mpib-berlin.mpg.de/nodes .. _`Matlab's fileexchange`: https://de.mathworks.com/matlabcentral/fileexchange/39721-save-mat-files-more-quickly