Brook for GPUs: stream computing on graphics hardware

Authors:
Ian Buck;Tim Foley;Daniel Horn;Jeremy Sugerman;Kayvon Fatahalian;Mike Houston;Pat Hanrahan
Affiliations:
Stanford University;Stanford University;Stanford University;Stanford University;Stanford University;Stanford University;Stanford University
Venue:
ACM SIGGRAPH 2004 Papers
Year:
2004

Citing 31
Cited 289

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Abstract

In this paper, we present Brook for GPUs, a system for general-purpose computation on programmable graphics hardware. Brook extends C to include simple data-parallel constructs, enabling the use of the GPU as a streaming co-processor. We present a compiler and runtime system that abstracts and virtualizes many aspects of graphics hardware. In addition, we present an analysis of the effectiveness of the GPU as a compute engine compared to the CPU, to determine when the GPU can outperform the CPU for a particular algorithm. We evaluate our system with five applications, the SAXPY and SGEMV BLAS operators, image segmentation, FFT, and ray tracing. For these applications, we demonstrate that our Brook implementations perform comparably to hand-written GPU code and up to seven times faster than their CPU counterparts.