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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Exploring Graphics Processor Performance for General Purpose Applications This paper by P. Trancoso and M. Charalambous at the University of Cyprus presents a comprehensive study of the
performance of general-purpose applications on the GPU, and determines the conditions that make the GPU work efficiently. Also, as the GPU is cheaper and consumes less power than a high-end CPU, the authors show the benefits of using the graphics card to extend the life-time of an existing computer system.
(Exploring Graphics Processor Performance for General Purpose Applications. P. Trancoso and M. Charalambous.
Proceedings of the Eighth Euromicro Conference on Digital System Design (DSD 2005))
Posted: 11 Jun 2005 [GPGPU /GPUs] # Stack Implementation on Programmable Graphics Hardware This paper by Ernst et al. describes a stack implementation for the GPU using textures for storage. For a predefined maximum stack depth, k, either k data textures, or a single large texture with k stack layers side by side are used. Additionally a stack pointer texture is needed. The paper argues that both push and pop can become O(1) operations using fragment program branching. Both push and pop require separate rendering passes. The technique is demonstrated in a kd-tree traversal implementation. (gpu stack bibtex)
Posted: 11 Jun 2005 [GPGPU /Scientific Computing/Data Compression/Data Structures] # |
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