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Over the last decade, commodity graphics processors (GPUs) have evolved from fixed-function graphics units into powerful, programmable data-parallel processors. Today's GPU is capable of sustaining computation rates substantially greater than today's modern CPUs, with technology trends indicating a widening gap in the future. Researchers in the rapidly evolving field of GPU computing have demonstrated mappings to these processors for a wide range of computationally intensive tasks, and new programming environments offer the promise of a wider role for GPU computing in the coming years. In this talk I will begin by discussing the motivation and background for GPU computing and describe some of the recent advances in the field. The field of GPU computing has substantially changed over its short lifetime due to new applications, techniques, programming models, and hardware. As parallel computing has decidedly moved into the mainstream, the lessons of GPU computing are applicable to both today's systems as well as to the designers of tomorrow's systems. I will address the way a GPU-CPU system is a heterogeneous system and why it is an interesting one, and discuss some case studies on how one can program and use such a system.