high performance computing on graphics processing units: hgpu.org

Applications

Accelerating molecular modeling applications with graphics processors

John E. Stone, James C. Phillips, Peter L. Freddolino, David J. Hardy, Leonardo G. Trabuco, Klaus Schulten

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Tags: Chemistry, Computational chemistry, CUDA, Electrostatic potential, Molecular dynamics, Molecular modeling, Multilevel summation, nVidia, nVidia GeForce 8800 GTX

October 30, 2010 by hgpu

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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Source codes

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

Current performance gains from utilizing the GPU or the ASIC MDGRAPE-3 within an enhanced Poisson Boltzmann approach

Tetsu Narumi, Kenji Yasuoka, Makoto Taiji, Siegfried Hofinger

Tags: ASIC, Chemistry, Computational chemistry, MDGRAPE, Poisson Boltzmann, 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 Implementation of the Smooth Particle Mesh Ewald Method on GPU Hardware

M. J. Harvey, G. De Fabritiis

Tags: Chemistry, Computational chemistry, Molecular dynamics, nVidia

October 28, 2010 by hgpu

GPU acceleration of a production molecular docking code

Bharat Sukhwani, Martin C. Herbordt

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Tags: Bioinformatics, Biophysics, Chemistry, FPGA, Molecular dynamics, nVidia, Tesla C1060

October 27, 2010 by hgpu

ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale

M. J. Harvey, G. Giupponi, G. De Fabritiis

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Tags: Biological Physics, Chemistry, Computational Physics, CUDA, GPU cluster, Molecular dynamics, nVidia, nVidia GeForce GTX 280, Package, Physics, Tesla C1060

October 27, 2010 by hgpu

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Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark

MapReduce for Counting Word Frequencies with MPI and GPUs

CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices

SYCLCOMP Utilities

On the Compilation Performance of Current SYCL Implementations

M1 GPUs for (scientific) computations in C++

Seamless GPU acceleration for C++ based physics with the Metal Shading Language on Apple’s M series unified chips

CUB: Cooperative primitives for CUDA C++

Onesweep: A Faster Least Significant Digit Radix Sort for GPUs

Simulator and workload generator for Edge-to-Cloud environments

FELARE: Fair Scheduling of Machine Learning Applications on Heterogeneous Edge Systems

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

Accelerating molecular modeling applications with graphics processors

Accelerating molecular dynamic simulation on graphics processing units

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

Current performance gains from utilizing the GPU or the ASIC MDGRAPE-3 within an enhanced Poisson Boltzmann approach

GPU acceleration of cutoff pair potentials for molecular modeling applications

An Implementation of the Smooth Particle Mesh Ewald Method on GPU Hardware

GPU acceleration of a production molecular docking code

ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale

Recent source codes

Rodinia Benchmarks for DPC++

DCA++: a state of the art implementation of the dynamical cluster approximation

tntorch - Tensor Network Learning with PyTorch

Open-source FPGA-ML codesign for the MLPerf Tiny Benchmark

MapReduce for Counting Word Frequencies with MPI and GPUs

CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices

SYCLCOMP Utilities

M1 GPUs for (scientific) computations in C++

CUB: Cooperative primitives for CUDA C++

Simulator and workload generator for Edge-to-Cloud environments

Most viewed papers (last 30 days)