GPGPU
General-Purpose Computation Using Graphics Hardware

Introduction

GPGPU stands for General-Purpose computation on GPUs. With the increasing programmability of commodity graphics processing units (GPUs), these chips are capable of performing more than the specific graphics computations for which they were designed. They are now capable coprocessors, and their high speed makes them useful for a variety of applications. The goal of this page is to catalog the current and historical use of GPUs for general-purpose computation.

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NVIDIA Announces CUDA GPU Computing Architecture

NVIDIA Corporation today unveiled NVIDIA CUDA technology, a new architecture for computing on NVIDIA GPUs, and the industry's first C-compiler development environment for the GPU. From the NVIDIA Press Release: "GPU computing with CUDA is a new approach to computing where hundreds of on-chip processor cores simultaneously communicate and cooperate to solve complex computing problems up to 100 times faster than traditional approaches. This breakthrough architecture is complemented by another first: the NVIDIA C-compiler for the GPU. This complete development environment gives developers the tools they need to solve new problems in computation-intensive applications such as product design, data analysis, technical computing, and game physics. CUDA-enabled GPUs offer dedicated features for computing, including the Parallel Data Cache, which allows 128, 1.35GHz processor cores in newest generation NVIDIA GPUs to cooperate with each other while performing intricate computations. Developers access these new features through a separate computing driver that communicates with DirectX and OpenGL, and the new NVIDIA C compiler for the GPU, which obsoletes streaming languages for GPU computing." CUDA website: http://www.nvidia.com/cuda

Posted: 09 Nov 2006 [GPGPU /High-Level Languages] #


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