high performance computing on graphics processing units: hgpu.org

Using hardware acceleration via General Programming on stock GPUs (GPGPU), I’ve sped up my algorithms by more than tenfold. This article shows how you can achieve these results too! Programmers have been interested in leveraging the highly parallel processing power of video cards to speed up applications that are not graphic in nature for a long time. Here, I explain how to do this with the CUDA API from NVIDIA. If your GPU is not from NVIDIA, you are not out of luck, as the same can be achieved with other APIs, such as the ATI-based Stream SDK or OpenCL.