high performance computing on graphics processing units: hgpu.org

Accelerating molecular dynamic simulation on graphics processing units

Mark S. Friedrichs, Peter Eastman, Vishal Vaidyanathan, Mike Houston, Scott Legrand, Adam L. Beberg, Daniel L. Ensign, Christopher M. Bruns, Vijay S. Pande

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Tags: ATI, ATI Radeon HD 4870, ATI Stream, Biology, Brook, Chemistry, Computational chemistry, Molecular dynamics, nVidia, nVidia GeForce GTX 280, Package, Solvent effects

October 30, 2010 by hgpu

CUSA and CUDE: GPU-accelerated methods for estimating solvent accessible surface area and desolvation

David Dynerman, Erick Butzlaff, Julie C. Mitchell

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Tags: Bioinformatics, Biology, Biophysics, Chemistry, Computational biology, CUDA, Molecular dynamics, nVidia, nVidia GeForce GTX 280, Solvent effects

October 29, 2010 by hgpu

GPU acceleration of cutoff pair potentials for molecular modeling applications

Christopher I. Rodrigues, David J. Hardy, John E. Stone, Klaus Schulten, Wen Mei

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Tags: Algorithms, Biology, Chemistry, CUDA, Medicine, Molecular dynamics, nVidia, Tesla C870

October 28, 2010 by hgpu

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

Lukasz Ligowski, Witold Rudnicki

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Tags: Bioinformatics, Biology, Computational biology, Computer science, CUDA, nVidia, nVidia GeForce 9800 GX2, Sequence alignment, Smith-Waterman algorithm

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

Sequence alignment with GPU: Performance and design challenges

Gregory M. Striemer, Ali Akoglu

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Tags: Bioinformatics, Biology, Computational biology, Computer science, CUDA, nVidia, Package, Sequence alignment, Smith-Waterman algorithm, Tesla C870

October 27, 2010 by hgpu

GPU computing for systems biology

Lorenzo Dematte, Davide Prandi

Tags: Biology

October 27, 2010 by hgpu

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

Shamrock: Multi-GPU hydrodynamics for astrophysics

The Shamrock code: I- Smoothed Particle Hydrodynamics on GPUs

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

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high performance computing on graphics processing units: hgpu.org

Applications

Accelerating molecular dynamic simulation on graphics processing units

CUSA and CUDE: GPU-accelerated methods for estimating solvent accessible surface area and desolvation

GPU acceleration of cutoff pair potentials for molecular modeling applications

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

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

Sequence alignment with GPU: Performance and design challenges

GPU computing for systems biology

Recent source codes

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

Shamrock: Multi-GPU hydrodynamics for astrophysics

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

Celerity Runtime: High-level C++ for Accelerator Clusters

wgpy: WebGL accelerated numpy-compatible array library for web browser

Microbenchmarking OpenMP target offload with Catch2

SUperman: Highly Efficient Permanent Computation Library

TransCL: An Automatic CUDA-to-OpenCL Programs Transformation Framework

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