high performance computing on graphics processing units: hgpu.org

Jairo Panetta, Thiago Teixeira, Paulo R. P. de Souza Filho, Carlos A. da Cunha Finho, David Sotelo, Fernando M. da Motta, Silvio S. Pinheiro, Ivan P. Junior, Andre L. Rosa, Luiz R. Monnerat, Leandro T. Carneiro, Carlos H. B. de Albrecht

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Tags: CUDA, Geoscience, GPU cluster, nVidia, Physics, Tesla C1060

October 28, 2010 by hgpu

The Heisenberg spin glass model on GPU: myths and actual facts

M. Bernaschi, G. Parisi, L. Parisi

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Tags: Condensed matter, CUDA, Disordered Systems and Neural Networks, nVidia, nVidia GeForce GTX 480, Package, Physics, Spin Systems, Tesla C1060, Tesla C2050

October 28, 2010 by hgpu

Analyzing CUDA workloads using a detailed GPU simulator

Ali Bakhoda, George L. Yuan, Wilson W. L. Fung, Henry Wong, Tor M. Aamodt

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8600 GTS, PTX

October 28, 2010 by hgpu

CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment

Svetlin Manavski, Giorgio Valle

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Tags: Bioinformatics, Biology, Computational biology, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Package, Sequence alignment, Smith-Waterman algorithm

October 28, 2010 by hgpu

cuInspiral: prototype gravitational waves detection pipeline fully coded on GPU using CUDA

Leone B. Bosi

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Tags: Computational Physics, CUDA, FFT, General Relativity and Quantum Cosmology, nVidia, nVidia GeForce GTX 275, Physics

October 28, 2010 by hgpu

GpuCV: A GPU-Accelerated Framework for Image Processing and Computer Vision

Yannick Allusse, Patrick Horain, Ankit Agarwal, Cindula Saipriyadarshan

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Tags: Computer science, Computer vision, CUDA, GLSL, Image processing, nVidia, nVidia GeForce GTX 280, OpenGL, Package

October 28, 2010 by hgpu

Adapting a message-driven parallel application to GPU-accelerated clusters

James C. Phillips, John E. Stone, Klaus Schulten

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Tags: Computer science, CUDA, GPU cluster, Molecular dynamics, nVidia, nVidia Quadro FX 5600

October 28, 2010 by hgpu

The gputools package enables GPU computing in R

Joshua Buckner, Justin Wilson, Mark Seligman, Brian Athey, Stanley Watson, Fan Meng

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Tags: Computer science, CUBLAS, CUDA, nVidia, Package

October 28, 2010 by hgpu

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high performance computing on graphics processing units: hgpu.org

An efficient implementation of Smith Waterman algorithm on GPU using CUDA, for massively parallel scanning of sequence databases

String Matching on a Multicore GPU Using CUDA

Compute Pairwise Manhattan Distance and Pearson Correlation Coefficient of Data Points with GPU

Motion Compensation and Reconstruction of H.264/AVC Video Bitstreams using the GPU

Accelerating Kirchhoff Migration by CPU and GPU Cooperation

The Heisenberg spin glass model on GPU: myths and actual facts

Analyzing CUDA workloads using a detailed GPU simulator

CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment

cuInspiral: prototype gravitational waves detection pipeline fully coded on GPU using CUDA

GpuCV: A GPU-Accelerated Framework for Image Processing and Computer Vision

Adapting a message-driven parallel application to GPU-accelerated clusters

The gputools package enables GPU computing in R

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