high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications » Chemistry

MDLab: A molecular dynamics simulation prototyping environment

Trevor Cickovski, Santanu Chatterjee, Jacob Wenger, Christopher R. Sweet, Jesus A. Izaguirre

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Tags: Chemistry, Computational chemistry, CUDA, Molecular dynamics, nVidia, Package, Python

November 6, 2010 by hgpu

High-Throughput All-Atom Molecular Dynamics Simulations Using Distributed Computing

I. Buch, M. J. Harvey, T. Giorgino, D. P. Anderson, G. De Fabritiis

Tags: Biochemistry, Chemistry, Molecular dynamics, Thermodynamics

November 6, 2010 by hgpu

Iterative induced dipoles computation for molecular mechanics on GPUs

Frederico Pratas,Ricardo A. Mata,Leonel Sousa

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Tags: Chemistry, Computational chemistry, Computer science, CUDA, Molecular dynamics, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 285

November 5, 2010 by hgpu

Molecular Simulation of ab Initio Protein Folding for a Millisecond Folder NTL9(1-39)

Vincent A. Voelz, Gregory R. Bowman, Kyle Beauchamp, Vijay S. Pande

Tags: Bioinformatics, Biology, Chemistry, Molecular dynamics

November 4, 2010 by hgpu

Parallel, stochastic measurement of molecular surface area

D. Juba, A. Varshney

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Tags: Chemistry, Computational chemistry, CUDA, Molecular modeling, Molecular surface, nVidia, nVidia GeForce 8800 GTX

November 3, 2010 by hgpu

PAPER – Accelerating parallel evaluations of ROCS

Imran S. Haque, Vijay S. Pande

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Tags: Chemistry, Computational chemistry, CUDA, Molecular overlay, nVidia, nVidia GeForce 8600 GT, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, Package, ROCS, Shape comparison

November 3, 2010 by hgpu

Accelerating Resolution-of-the-Identity Second-Order Moller-Plesset Quantum Chemistry Calculations with Graphical Processing Units

Leslie Vogt, Roberto Olivares-Amaya, Sean Kermes, Yihan Shao, Carlos Amador-Bedolla, Alan Aspuru-Guzik

Tags: Chemistry, Computational chemistry, CUBLAS, CUDA, Molecular modeling, nVidia, nVidia Quadro FX 5600

November 2, 2010 by hgpu

Multilevel summation of electrostatic potentials using graphics processing units

David J. Hardy, John E. Stone, Klaus Schulten

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Tags: Chemistry, CUDA, Electrostatics, Molecular dynamics, Molecular modeling, Multilevel summation, nVidia, nVidia GeForce GTX 280, Physics, Tesla C870, Tesla D870

November 2, 2010 by hgpu

General purpose molecular dynamics simulations fully implemented on graphics processing units

J. Anderson, C. Lorenz, A. Travesset

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Tags: Chemistry, CUDA, GPU cluster, Molecular dynamics, nVidia, nVidia GeForce 8800 GTX, Physics, Polymer Systems

November 2, 2010 by hgpu

Quantum Chemistry on Graphical Processing Units. 1. Strategies for Two-Electron Integral Evaluation

Ivan S. Ufimtsev, Todd J. Martinez

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Tags: Chemistry, Computational chemistry, CUDA, Molecular modeling, nVidia, nVidia GeForce 8800 GTX

October 30, 2010 by hgpu

Accelerating Density Functional Calculations with Graphics Processing Unit

Koji Yasuda

Tags: Chemistry, Computational chemistry, Molecular modeling

October 30, 2010 by hgpu

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

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

MDLab: A molecular dynamics simulation prototyping environment

High-Throughput All-Atom Molecular Dynamics Simulations Using Distributed Computing

Iterative induced dipoles computation for molecular mechanics on GPUs

Molecular Simulation of ab Initio Protein Folding for a Millisecond Folder NTL9(1-39)

Parallel, stochastic measurement of molecular surface area

PAPER – Accelerating parallel evaluations of ROCS

Accelerating Resolution-of-the-Identity Second-Order Moller-Plesset Quantum Chemistry Calculations with Graphical Processing Units

Multilevel summation of electrostatic potentials using graphics processing units

General purpose molecular dynamics simulations fully implemented on graphics processing units

Quantum Chemistry on Graphical Processing Units. 1. Strategies for Two-Electron Integral Evaluation

Accelerating Density Functional Calculations with Graphics Processing Unit

Accelerating molecular modeling applications with graphics processors

Recent source codes

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

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