Adjusting SPM settings
A defaults variable 'maxmem' indicates how much memory can be used at the same time when estimating a model. If you have loads of memory, you can increase that memory setting in spm_defaults.m:
defaults.stats.maxmem = 2^30;
- 2^30 = 1GB
- 2^29 = 512MB
Compiling the MEX files
The compiled MEX files provided with SPM are built in such a way to be compatible with most platforms and MATLAB versions but you might benefit from compiling them for your exact platform/MATLAB version - some C compilers might also produce better optimised binaries (such as Intel Compilers). See the installation pages for more details on how to recompile SPM MEX files.
Note that it is not recommended to disable the JAVA Virtual Machine when launching MATLAB (matlab -nojvm). If you don't want to use the MATLAB desktop, you can preferably launch MATLAB with:
Recent MATLABs support implicit multiprocessing allowing to run multiple threads on a single machine without any change to the MATLAB code itself: this requires a multiple CPU (multiprocessor or multicore) system. The gain in compute time with SPM is not dramatic though.
If you run many MATLAB sessions in parallel to manually distribute your SPM processings, it is recommended to set the number of computational threads to one.
Install the latest Basic Linear Algebra Subroutines (BLAS)/Linear Algebra PACKage (LAPACK) for your system, as MATLAB doesn't ship the latest version in its releases.
The main choices are between:
- ATLAS - Automatically Tuned Linear Algebra Software (open source)
- MKL - Intel Math Kernel Library
- ACML - AMD Core Math Library
- GotoBLAS - GotoBLAS (Texas Advanced Computing Center)
For more information on how to upgrade your MATLAB libraries with ATLAS/MKL, see:
See pSPM for SPM2.
There is currently work in progress to provide an official parallel/distributed version of SPM.
See also the Sun Grid Engine Project (SGE):
Using the Graphics Processing Unit (GPU)
It is possible for MATLAB to take advantage of the GPU (the processor of the graphic card, by opposition to the CPU), to perform some operations, resulting in significant speed improvements. A number of toolboxes are available:
- Parallel Computing Toolbox GPU Computing with MATLAB
- GPUmat GPU toolbox for MATLAB
- AccelerEyes Jacket Software for MATLAB GPU computing
Jacket & SPM Accelerating SPM & fMRI with GPUs for Neuroimaging