A Survey of General-Purpose Computation on Graphics Hardware John D. Owens, David Luebke, Naga Govindaraju, Mark Harris, Jens Krüger, Aaron E. Lefohn, and Timothy J. Purcell Abstract The rapid increase in the performance of graphics hardware, coupled with recent improvements in its programmability, have made graphics hardware a compelling platform for computationally demanding tasks in a wide variety of application domains. In this report, we describe, summarize, and analyze the latest research in mapping general-purpose computation to graphics hardware. We begin with the technical motivations that underlie general-purpose computation on graphics processors (GPGPU) and describe the hardware and software developments that have led to the recent interest in this field. We then aim the main body of this report at two separate audiences. First, we describe the techniques used in mapping general-purpose computation to graphics hardware. We believe these techniques will be generally useful for researchers who plan to develop the next generation of GPGPU algorithms and techniques. Second, we survey and categorize the latest developments in general-purpose application development on graphics hardware. This survey should be of particular interest to researchers who are interested in using the latest GPGPU applications in their systems of interest. Categories and Subject Descriptors (according to ACM CCS): I.3.1 [Computer Graphics]: Hardware Architecture; D.2.2 [Software Engineering]: Design Tools and Techniques 1. Introduction: Why GPGPU? Commodity computer graphics chips are probably today's most powerful computational hardware for the dollar. These chips, known generically as Graphics Processing Units or GPUs, have gone from afterthought peripherals to modern, powerful, and programmable processors in their own right. Many researchers and developers have become interested in harnessing the power of commodity graphics hardware for general-purpose computing. Recent years have seen an explosion in interest in such research efforts, known collectively as GPGPU (for "General Purpose GPU") computing. In this State of the Art Report we summarize the motivation and essential developments in the hardware and software behind GPGPU. We give an overview of the techniques and computational building blocks used to map general-purpose computation to graphics hardware and provide a survey of the various general-purpose computing applications to which GPUs have been applied. We begin by reviewing the motivation for and challenges of general purpose GPU computing. Why GPGPU? 1.1. Powerful and Inexpensive Recent graphics architectures provide tremendous memory bandwidth and computational horsepower. For example, the NVIDIA GeForce 6800 Ultra ($417 as of June 2005) can achieve a sustained 35.2 GB/sec of memory bandwidth; the ATI X800 XT ($447) can sustain over 63 GFLOPS (compare to 14.8 GFLOPS theoretical peak for a 3.7 GHz Intel Pentium4 SSE unit [Buc04]). GPUsA Survey of General-Purpose Computation on Graphics Hardware John D. Owens, David Luebke, Naga Govindaraju, Mark Harris, Jens Krüger, Aaron E. Lefohn, and Timothy J. Purcell Abstract The rapid increase in the performance of graphics hardware, coupled with recent improvements in its programmability, have made graphics hardware a compelling platform for computationally demanding tasks in a wide variety of application domains. In this report, we describe, summarize, and analyze the latest research in mapping general-purpose computation to graphics hardware. We begin with the technical motivations that underlie general-purpose computation on graphics processors (GPGPU) and describe the hardware and software developments that have led to the recent interest in this field. We then aim the main body of this report at two separate audiences. First, we describe the techniques used in mapping general-purpose computation to graphics hardware. We believe these techniques will be generally useful for researchers who plan to develop the next generation of GPGPU algorithms and techniques. Second, we survey and categorize the latest developments in general-purpose application development on graphics hardware. This survey should be of particular interest to researchers who are interested in using the latest GPGPU applications in their systems of interest. Categories and Subject Descriptors (according to ACM CCS): I.3.1 [Computer Graphics]: Hardware Architecture; D.2.2 [Software Engineering]: Design Tools and Techniques 1. Introduction: Why GPGPU? Commodity computer graphics chips are probably today's most powerful computational hardware for the dollar. These chips, known generically as Graphics Processing Units or GPUs, have gone from afterthought peripherals to modern, powerful, and programmable processors in their own right. Many researchers and developers have become interested in harnessing the power of commodity graphics hardware for general-purpose computing. Recent years have seen an explosion in interest in such research efforts, known collectively as GPGPU (for "General Purpose GPU") computing. In this State of the Art Report we summarize the motivation and essential developments in the hardware and software behind GPGPU. We give an overview of the techniques and computational building blocks used to map general-purpose computation to graphics hardware and provide a survey of the various general-purpose computing applications to which GPUs have been applied. We begin by reviewing the motivation for and challenges of general purpose GPU computing. Why GPGPU? 1.1. Powerful and Inexpensive Recent graphics architectures provide tremendous memory bandwidth and computational horsepower. For example, the NVIDIA GeForce 6800 Ultra ($417 as of June 2005) can achieve a sustained 35.2 GB/sec of memory bandwidth; the ATI X800 XT ($447) can sustain over 63 GFLOPS (compare to 14.8 GFLOPS theoretical peak for a 3.7 GHz Intel Pentium4 SSE unit [Buc04]). GPUs