high performance computing on graphics processing units: hgpu.org

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Population Parallel GP on the G80 GPU

Denis Robilliard, Virginie Marion-Poty, Cyril Fonlupt

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Tags: Bioinformatics, Biology, CUDA, Genetics, nVidia, nVidia GeForce 8800 GTX, Package

December 29, 2010 by hgpu

A SIMD Interpreter for Genetic Programming on GPU Graphics Cards

William B. Langdon, Wolfgang Banzhaf

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Tags: Bioinformatics, Biology, DirectX, Genetics, nVidia, nVidia GeForce 8800 GTX, OpenGL

December 29, 2010 by hgpu

Real-world comparison of CPU and GPU implementations of SNPrank: a network analysis tool for GWAS

Nicholas A. Davis, Ahwan Pandey, B. A. McKinney

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Tags: Bioinformatics, Biology, CUDA, Genetics, Medicine, nVidia, Package, Tesla M1060

December 13, 2010 by hgpu

Hierarchical clustering of gene expression profiles with graphics hardware acceleration

Q. Zhang, Y. Zhang

Tags: Biology, Genetics, Pattern recognition

December 4, 2010 by hgpu

Many-core algorithms for statistical phylogenetics

Marc A. Suchard, Andrew Rambaut

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Tags: Bioinformatics, Biology, CUDA, Genetics, nVidia, nVidia GeForce GTX 280

November 23, 2010 by hgpu

permGPU: Using graphics processing units in RNA microarray association studies

Ivo Shterev, Sin H. Jung, Stephen George, Kouros Owzar

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Tags: Bioinformatics, Biology, CUDA, Genetics, nVidia, nVidia GeForce GTX 280, Package, RNA

November 17, 2010 by hgpu

SHEsisEpi, a GPU-enhanced genome-wide SNP-SNP interaction scanning algorithm, efficiently reveals the risk of genetic epistasis in bipolar disorder

Xiaohan Hu, Qiang Liu, Zhao Zhang, Zhiqiang Li, Shilin Wang, Lin He, Yongyong Shi

Tags: Bioinformatics, Biology, Genetics

November 17, 2010 by hgpu

Multifactor dimensionality reduction for graphics processing units enables genome-wide testing of epistasis in sporadic ALS

Casey S. Greene, Nicholas A. Sinnott-Armstrong, Daniel S. Himmelstein, Paul J. Park, Jason H. Moore, Brent T. Harris

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Tags: Bioinformatics, Biology, CUDA, Genetics, nVidia, nVidia GeForce GTX 295, Package

November 7, 2010 by hgpu

Accelerating epistasis analysis in human genetics with consumer graphics hardware

Nicholas S. Armstrong, Casey Greene, Fabio Cancare, Jason Moore

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Tags: Bioinformatics, Biology, CUDA, Genetics, nVidia, nVidia GeForce GTX 280

November 7, 2010 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

Population Parallel GP on the G80 GPU

A SIMD Interpreter for Genetic Programming on GPU Graphics Cards

Real-world comparison of CPU and GPU implementations of SNPrank: a network analysis tool for GWAS

Hierarchical clustering of gene expression profiles with graphics hardware acceleration

Many-core algorithms for statistical phylogenetics

permGPU: Using graphics processing units in RNA microarray association studies

SHEsisEpi, a GPU-enhanced genome-wide SNP-SNP interaction scanning algorithm, efficiently reveals the risk of genetic epistasis in bipolar disorder

Multifactor dimensionality reduction for graphics processing units enables genome-wide testing of epistasis in sporadic ALS

Accelerating epistasis analysis in human genetics with consumer graphics hardware

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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