Introduction to GPU programming for EDA

Advances in GPU technology have propelled the GPU into arenas far afield from the traditional, isolated roles they have previously played. With hundreds of processing units in a single GPU, substantial speedups can be achieved by harnessing their power to augment the performance of the traditional single- or multi-core CPU on certain compute-intensive applications. However, utilizing the GPU requires both a change in the programmer's traditional algorithmic model as well as a judicious selection of algorithm being used for the problem. This paper reviews the GPU architecture and the tools available to utilize this valuable resource. It also provides insight into the type of problem best suited for the GPU as well as programming styles required to fully harness the power of the GPU. We present examples of specific EDA algorithms that can benefit from GPU acceleration, using both the CUDA and OpenCL environments.