Difference between revisions of "GPU Computing"
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| + | GPGPU stands for General-Purpose computation on Graphics Processing Units, also known as GPU Computing. Graphics Processing Units (GPUs) are high-performance many-core processors capable of very high computation and data throughput. Once specially designed for computer graphics and difficult to program, today’s GPUs are general-purpose parallel processors with support for accessible programming interfaces and industry-standard languages such as C. Developers who port their applications to GPUs often achieve speedups of orders of magnitude vs. optimized CPU implementations. The goal of GPGPU.org is to catalog the current and historical use of GPUs for general-purpose computation, and to provide a central resource for GPGPU software developers. | ||
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| + | The term GPGPU was coined and GPGPU.org was founded by Mark Harris in 2002 when he recognized an early trend of using GPUs for non-graphics applications. GPGPU.org has grown from an obscure site visited by few into a popular destination for developers and researchers. | ||
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| + | Modern graphics processing units (GPUs) contain hundreds of arithmetic units and can be harnessed to provide tremendous acceleration for numerically intensive scientific applications such as molecular modeling. The increased capabilities and flexibility of recent GPU hardware combined with high level GPU programming languages such as CUDA and OpenCL has unlocked this computational power and made it accessible to computational scientists. The key to effective GPU computing is the design and implementation of data-parallel algorithms that scale to hundreds of tightly coupled processing units. Many molecular modeling applications are well suited to GPUs, due to their extensive computational requirements, and because they lend themselves to data-parallel implementations. Several exemplary results from our GPU computing work are presented in Klaus Schulten's Keynote Lecture from the 2010 GPU Technology Conference. | ||
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==Referensi== | ==Referensi== | ||
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* http://gpgpu.org/forums/ | * http://gpgpu.org/forums/ | ||
* http://www.gpucomputing.net/ | * http://www.gpucomputing.net/ | ||
| + | * http://www.ks.uiuc.edu/Research/gpu/ | ||
Latest revision as of 09:07, 2 July 2012
GPGPU stands for General-Purpose computation on Graphics Processing Units, also known as GPU Computing. Graphics Processing Units (GPUs) are high-performance many-core processors capable of very high computation and data throughput. Once specially designed for computer graphics and difficult to program, today’s GPUs are general-purpose parallel processors with support for accessible programming interfaces and industry-standard languages such as C. Developers who port their applications to GPUs often achieve speedups of orders of magnitude vs. optimized CPU implementations. The goal of GPGPU.org is to catalog the current and historical use of GPUs for general-purpose computation, and to provide a central resource for GPGPU software developers.
The term GPGPU was coined and GPGPU.org was founded by Mark Harris in 2002 when he recognized an early trend of using GPUs for non-graphics applications. GPGPU.org has grown from an obscure site visited by few into a popular destination for developers and researchers.
Modern graphics processing units (GPUs) contain hundreds of arithmetic units and can be harnessed to provide tremendous acceleration for numerically intensive scientific applications such as molecular modeling. The increased capabilities and flexibility of recent GPU hardware combined with high level GPU programming languages such as CUDA and OpenCL has unlocked this computational power and made it accessible to computational scientists. The key to effective GPU computing is the design and implementation of data-parallel algorithms that scale to hundreds of tightly coupled processing units. Many molecular modeling applications are well suited to GPUs, due to their extensive computational requirements, and because they lend themselves to data-parallel implementations. Several exemplary results from our GPU computing work are presented in Klaus Schulten's Keynote Lecture from the 2010 GPU Technology Conference.