GPGPU |
General-Purpose Computation Using Graphics Hardware
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IntroductionGPGPU 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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Cg Release 1.3 Beta 2 has been released with support for the latest GeForce 6 Series (NV4X) GPUs. This version of Cg offers the following features and improvements:
Posted: 19 Aug 2004 [GPGPU /Miscellaneous/Developer Resources] # GPU Cluster for High Performance Computing This paper by Fan et. al. at Stony Brook University presents the use of a cluster of commodity GPUs for high performance scientific computing. As an example application, they have developed a parallel flow simulation using the lattice Boltzmann model (LBM) on a GPU cluster and have simulated the dispersion of airborne contaminants in the Times Square area of New York City. Using 30 GPU nodes, their simulation can compute a 480 x 400 x 80 LBM in 0.31 second/step, a speed which is 4.6 times faster than that of their previous CPU cluster implementation. Besides the LBM, the paper also discusses other potential applications of the GPU cluster, such as cellular automata, PDE solvers, and FEM. (Zhe Fan, Feng Qiu, Arie Kaufman, Suzanne Yoakum-Stover, GPU Cluster for High Performance Computing, To Appear in Proceedings of the ACM/IEEE SuperComputing 2004 (SC'04), November, 2004)
Posted: 19 Aug 2004 [GPGPU /Scientific Computing] # SIMD Optimization of Linear Expressions for Programmable Graphics Hardware Linear expressions constitute one of the most basic operations in scientific computations. This paper by proposes a SIMD code optimization technique that enables efficient shader codes to be generated for evaluating linear expressions. Performance can be improved considerably by efficiently packing arithmetic operations into four-wide SIMD instructions through reordering of the operations in linear expressions. We demonstrate that this technique can be used effectively for programming both vertex and pixel shaders for a variety of mathematical applications.
( SIMD Optimization of Linear Expressions for Programmable
Graphics Hardware. C. Bajaj, I. Ihm, J. Min, and J. Oh)
Posted: 19 Aug 2004 [GPGPU /Scientific Computing] # Ne@tware Player for Real-time Video Post-Processing Ne@tware Player 2004 supports the latest DirectX 9.0c graphic and media technologies. It allows you to design and watch visual special effects in real-time. The Shader Model 3.0 and High Level Shader Language (HLSL) support make Ne@tware Player a shader development platform for video processing in Graphic Processing Unit as well. Fullscreen, Multithread Video Engine, Action Mapper, and International Languages are other new features. (http://www.neatware.com/player/)
Posted: 19 Aug 2004 [GPGPU /Image And Volume Processing] # |
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