GPU parallel computing: Programming language, debugging tools and data structures

Abstract

With many cores driven by high memory bandwidth, today’s graphics processing unit (GPU) has involved into an absolute computing workhorse. More and more scientists, researchers and software developers are using GPUs to accelerate their algorithms and applications. Developing complex programs and software on the GPU, however, is still far from easy with existing tools provided by hardware vendors. This article introduces our recent research efforts to make GPU software development much easier. Specifically, we designed BSGP, a high-level programming language for generalpurpose computation on the GPU. A BSGP program looks much the same as a sequential C program, and is thus easy to read, write and maintain. Its performance on the GPU is guaranteed by a well-designed compiler that converts the program to native GPU code. We also developed an effective debugging system for BSGP programs based on the GPU interrupt, a unique feature of BSGP that allows calling CPU functions from inside GPU code. Moreover, using BSGP, we developed GPU algorithms for constructing several widely-used spatial hierarchies for high-performance graphics applications.