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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Workshop this weekend: Data-Parallel Programming Models for Many-Core Architectures Data-parallel programming models are emerging as an extremely attractive
model for parallel programming, driven by several factors. Through
deterministic semantics and constrained synchronization mechanisms, they
provide race-free parallel-programming semantics. Furthermore,
data-parallel programming models free programmers from reasoning about
the details of the underlying hardware and software mechanisms for
achieving parallel execution and facilitate effective compilation.
Finally, efforts in the GPGPU movement and elsewhere have matured
implementation technologies for streaming and data-parallel programming
models to the point where high performance can be reliably achieved.
This workshop gathers commercial and academic researchers, vendors, and
users of data-parallel programming platforms to discuss implementation
experience for a broad range of many-core architectures and to speculate
on future programming-model directions. Participating institutions include
AMD, Electronic Arts, Intel, Microsoft, NVIDIA, PeakStream, RapidMind,
and The University of New South Wales. (
Link to Call for Participation, Data-Parallel Programming Models for Many-Core Architectures)
Posted: 07 Mar 2007 [GPGPU /Conferences] # |
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