GPU Computing (original) (raw)

2000, Proceedings of the IEEE

The graphics processing unit (GPU) has become an integral part of today's mainstream computing systems. Over the past six years, there has been a marked increase in the performance and capabilities of GPUs. The modern GPU is not only a powerful graphics engine but also a highly parallel programmable processor featuring peak arithmetic and memory bandwidth that substantially outpaces its CPU counterpart. The GPU's rapid increase in both programmability and capability has spawned a research community that has successfully mapped a broad range of computationally demanding, complex problems to the GPU. This effort in generalpurpose computing on the GPU, also known as GPU computing, has positioned the GPU as a compelling alternative to traditional microprocessors in high-performance computer systems of the future. We describe the background, hardware, and programming model for GPU computing, summarize the state of the art in tools and techniques, and present four GPU computing successes in game physics and computational biophysics that deliver order-of-magnitude performance gains over optimized CPU applications. KEYWORDS | General-purpose computing on the graphics processing unit (GPGPU); GPU computing; parallel computing I. INTRODUCTION Parallelism is the future of computing. Future microprocessor development efforts will continue to concentrate on adding cores rather than increasing single-thread performance. One example of this trend, the heterogeneous nine-core Cell broadband engine, is the main processor in the Sony Playstation 3 and has also attracted substantial interest from the scientific computing community. Similarly, the highly parallel graphics processing unit (GPU) is rapidly gaining maturity as a powerful engine for computationally demanding applications. The GPU's performance and potential offer a great deal of promise for future computing systems, yet the architecture and programming model of the GPU are markedly different than most other commodity single-chip processors. The GPU is designed for a particular class of applications with the following characteristics. Over the past few years, a growing community has identified other applications with similar characteristics and successfully mapped these applications onto the GPU. Computational requirements are large. Real-time rendering requires billions of pixels per second, and each pixel requires hundreds or more operations. GPUs must deliver an enormous amount of compute performance to satisfy the demand of complex real-time applications. Parallelism is substantial. Fortunately, the graphics pipeline is well suited for parallelism. Operations on vertices and fragments are well matched to finegrained closely coupled programmable parallel compute units, which in turn are applicable to many other computational domains. Throughput is more important than latency. GPU implementations of the graphics pipeline prioritize throughput over latency. The human visual system operates on millisecond time scales, while operations within a modern processor take nanoseconds. This six-order-of-magnitude gap means that the latency of any individual operation is unimportant. As a consequence, the graphics pipeline is quite