title: Accelerating MATLAB code using GPUs?
body: <p>AccelerEyes announced in December 2012 that it works with Mathworks on the GPU code and has discontinued its product Jacket for MATLAB:</p>
  
  <p><a href="http://blog.accelereyes.com/blog/2012/12/12/exciting-updates-from-accelereyes/" rel="noreferrer">http://blog.accelereyes.com/blog/2012/12/12/exciting-updates-from-accelereyes/</a></p>
  
  <p>Unfortunately they do not sell Jacket licences anymore.</p>
  
  <p>As far as I understand, the Jacket GPU Array solution based on ArrayFire was much faster than the gpuArray solution provided by MATLAB. </p>
  
  <p>I started working with gpuArray, but I see that many functions are implemented poorly. For example a simple </p>
  
  <pre><code>myArray(:) = 0 
  </code></pre>
  
  <p>is very slow. I have written some custom CUDA-Kernels, but the poorly-implemented standard MATLAB functionality adds a lot of overhead, even if working with gpuArrays consistently throughout the code. I fixed some issues by replacing MATLAB code with hand written CUDA code - but I do not want to reimplement the MATLAB standard functionality.</p>
  
  <p>Another feature I am missing is sparse GPU matrices.</p>
  
  <p>So my questions are:</p>
  
  <p>How do is speed up the badly implemented default GPU implementations provided by MATLAB? In particular, how do I speed up sparse matrix operations in MATLAB using the GPU?</p>