title: Matlab and GPU/CUDA programming
body: <p>I need to run several independent analyses on the same data set.
  Specifically, I need to run bunches of 100 glm (generalized linear models) analyses and was thinking to take advantage of my video card (GTX580).</p>
  
  <p>As I have access to Matlab and the Parallel Computing Toolbox (and I'm not good with C++), I decided to give it a try.</p>
  
  <p>I understand that a single GLM is not ideal for parallel computing, but as I need to run 100-200 in parallel, I thought that using parfor could be a solution.</p>
  
  <p>My problem is that it is not clear to me which approach I should follow. I wrote a gpuArray version of the matlab function glmfit, but using parfor doesn't have any advantage over a standard "for" loop.</p>
  
  <p>Has this anything to do with the matlabpool setting? It is not even clear to me how to set this to "see" the GPU card. By default, it is set to the number of cores in the CPU (4 in my case), if I'm not wrong.
  Am I completely wrong on the approach?</p>
  
  <p>Any suggestion would be highly appreciated.</p>
  
  <p><strong>Edit</strong></p>
  
  <p>Thanks. I'm aware of GPUmat and Jacket, and I could start writing in C without too much effort, but I'm testing the GPU computing possibilities for a department where everybody uses Matlab or R. The final goal would be a cluster based on C2050 and the Matlab Distribution Server (or at least this was the first project).
  Reading the ADs from Mathworks I was under the impression that parallel computing was possible even without C skills. It is impossible to ask the researchers in my department to learn C, so I'm guessing that GPUmat and Jacket are the better solutions, even if the limitations are quite big and the support to several commonly used routines like glm is non-existent.</p>
  
  <p>How can they be interfaced with a cluster? Do they work with some job distribution system?</p>