A Tutorial on Leveraging GPU Computing for MATLAB Applications

TR – 2009 – 03 A Tutorial on Leveraging double-precision GPU Computing for MATLAB Applications Makarand Datar (datar@wisc.edu) Dan Negrut thJune 8 2009 1 Introduction Advances in microprocessor performance in recent years have led to wider use of Computational Multi-Body Dynamic Simulations to reduce production costs, decrease product delivery time, and reproduce scenarios difficult or expensive to study experimentally. With the introduction of massively parallel Graphics Processing Units (GPUs), the general public has access to large amounts of computing power. GPUs offer a large number of computing cores and, like super computers, are highly parallel. Unlike super computers, however, GPU’s are readily available at low costs. The GPU is a device originally designed for graphics processing. Its applications generally relate to visualization in video games and other graphically intensive programs. These applications require a device that is capable of rendering, at high rates, hundreds of thousands of polygons in every frame. The computations performed are relatively simple and could easily be done by a CPU; it is the sheer number of calculations that makes CPU rendering impractical. The divide between CPUs and GPUs can be benchmarked by measuring their FLoating-point OPeration rate (FLOP rate). This benchmark also demonstrates how the unique architecture of the GPU gives it an enormous amount of computing ...