matlab shared c++ libraries and OpenCL

I have a project that requires lots of image processing and wanted to add GPU support to speed things up.

I was wondering if i compiled my matlab into c++ shared library and called it from within OpenCL p开发者_如何学Pythonrogram, does that mean that the matlab code is going to be run on GPU?

My own (semi-educated) guess is that you are going to find this very difficult to do. But, others have trodden the same path. This paper might be a good place to start your research. And Googling turned up Accelereyes and a couple of references to items on the Mathworks File Exchange which you might want to follow up.

Everything in jacket is written in c/ c++ / cuda. Infact we now have a beta version libjacket (http://www.accelereyes.com/downloadLibjacket) which can be used to extend not just matlab but other languages if you are willing.

@OSaad Most of our functions are the fastest options out there. Be it in C or matlab.

The Parallel Computing Toolbox in the upcoming release R2010b (due September 1st) supports GPU processing for several functions. Unfortunately, it only supports CUDA (version 1.3 and later), so with an ATI graphics card, you're out of luck. However, you may just want to buy a dedicated GPU, anyway.

Typically, if you can write your Matlab code in a "vectorized" way, then the packages like AccelerEyes and Jacket have a reasonable chance of making things run on the GPU. You can verify this to some extent beforehand by checking whether Matlab itself is able to run on multiple cores on the CPU (these days Matlab will use multiple cores if things are parallelizable in an obvious way).

If that doesn't work, then you need to drop down to C/C++ via mex and then, from there, call OpenCL yourself. Mex is how Matlab talks to C code, so you write C code that is called by Matlab (and receives the matrices, etc), then initialises and calls OpenCL. This is more work, but may be your only route (and, even if the automated packages work to some extent, this approach can still give more speedups because you can be smarter about memory management, for example, if you know what your are doing).