Why GPUs Sample Clauses

Why GPUs. ‌ There are many parallel architectures, from big clusters to many-core CPUs. We consider approaches that can exploit the architecture of graphical pro- cessing units (GPUs). GPUs are massively parallel processors, whose origi- nal purpose is for processing 2D or 3D graphics, but they are also good for stream processing. They allow complex floating point operations of large amounts of data. GPUs are readily available from the consumer market and are already installed in many commodity desktop and laptop comput- ers. General-purpose computing on graphics processing units (GPGPU) is an affordable way to attain vast parallel computing power. GPUs have also entered the arena of high performance computing. In June 2010 a super- computer built with GPUs won second place on the Top500 list4 and fourth place on the Green500 list5. GPU vendors have released application programming interfaces (API) for general purpose programming. Among them, the most mature API is NVIDIA’s C for CUDA (Compute Unified Device Architecture). Recently, many companies have joined forces to create a standard called OpenCL (Open Computing Language) for programming heterogeneous platforms (e.g. CPUs, GPUs), but it is still yet to be adopted by many users. There are many success stories with NVIDIA CUDA architectures in the scientific community. One examples is the XXXX@xxxx projects6. Many other examples can be found on the CUDA Showcase website7. Because of these success stories, we choose NVIDIA GPUs as our parallel programming platform. The vast availability of NVIDIA GPUs on the market provides a large potential audience for our parallel software package.
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