In this post, I will discuss techniques you can use to maximize the performance of your GPU-accelerated MATLAB® code. First I explain how to write MATLAB code which is inherently parallelizable. This technique, known as vectorization, benefits all your code whether or not it uses the GPU. Then I present a family of function wrappers—bsxfun, pagefun, and arrayfun—that take advantage of GPU hardware…
]]>In an earlier post we showed how MATLAB® can support CUDA kernel prototyping and development by providing an environment for quick evaluation and visualization using the object. In this post I will show you how to integrate an existing library of both host and device code implemented in C++ or another CUDA-accelerated language using MEX. With MEX you can extend and customize MATLAB…
]]>