high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications » Chemistry

Accelerating electrostatic surface potential calculation with multi-scale approximation on graphics processing units

Ramu Anandakrishnan, Tom R. W. Scogland, Andrew T. Fenley, John C. Gordon, Wu-chun Feng, Alexey V. Onufriev

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Tags: ATI, ATI CAL, ATI Radeon HD 4870, Brook, Chemistry, Molecular modeling, Solvent effects

November 22, 2010 by hgpu

Quantum Chemistry on Graphical Processing Units. 2. Direct Self-Consistent-Field Implementation

Ivan S. Ufimtsev, Todd J. Martinez

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Tags: Chemistry, Computational chemistry, CUDA, Molecular modeling, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280

November 21, 2010 by hgpu

Using Graphics Processors to Facilitate Explicit Digital Electrochemical Simulation: Theory of Elliptical Disc Electrodes

Ian Cutress, Richard Compton

Tags: Chemistry, Electrochemistry

November 19, 2010 by hgpu

Direct numerical simulation of sub-grid structures in gas-solid flow — GPU implementation of macro-scale pseudo-particle modeling

Qingang Xiong, Bo Li, Feiguo Chen, Jingsen Ma, Wei Ge, Jinghai Li

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Tags: Chemistry, Fluid dynamics, Navier-Stokes equations, NSEs, nVidia

November 17, 2010 by hgpu

Enhanced molecular dynamics performance with a programmable graphics processor

D. C. Rapaport

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Tags: Chemistry, Computational chemistry, Computational Physics, CUDA, Molecular dynamics, nVidia, nVidia Quadro FX 770 M, Physics

November 13, 2010 by hgpu

Sop-GPU: Accelerating biomolecular simulations in the centisecond timescale using graphics processors

A. Zhmurov, R. I. Dima, Y. Kholodov, V. Barsegov

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Tags: Bioinformatics, Biomolecular assemblies, Chemistry, CUDA, Dynamic force spectroscopy, Langevin dynamics, Molecular dynamics, nVidia, nVidia GeForce GTX 295

November 9, 2010 by hgpu

A molecular docking system using CUDA

Youngtae Roh, Jun Lee, Sungjun Park, Jee I. Kim

Tags: Bioinformatics, Biology, Biophysics, Chemistry, Molecular dynamics

November 9, 2010 by hgpu

SIML: A Fast SIMD Algorithm for Calculating LINGO Chemical Similarities on GPUs and CPUs

Imran S. Haque, Vijay S. Pande, W. Patrick Walters

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Source codes

Tags: Chemistry, CUDA, nVidia, nVidia GeForce GTS 250, OpenMP, Package, Tesla T10

November 9, 2010 by hgpu

Theory of square, rectangular, and microband electrodes through explicit GPU simulation

Ian Cutress, Richard Compton

Tags: Chemistry, Electrochemistry, Finite difference

November 8, 2010 by hgpu

Large-scale mixer simulations using massively parallel GPU architectures

Charles A. Radeke, Benjamin J. Glasser, Johannes G. Khinast

Tags: Chemistry, CUDA, nVidia, Pharmaceuticals, Powder processing

November 8, 2010 by hgpu

Biomolecular electrostatics simulation with a parallel FMM-based BEM, using up to 512 GPUs

Rio Yokota, Jaydeep P. Bardhan, Matthew G. Knepley, L. A. Barba, Tsuyoshi Hamada

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Tags: Chemistry, CUDA, GPU cluster, Molecular modeling, nVidia, nVidia GeForce GTX 295, Package

November 7, 2010 by hgpu

Efficient nonbonded interactions for molecular dynamics on a graphics processing unit

P. Eastman, V. S. Pande

Tags: Chemistry, Computational chemistry, Molecular dynamics, Solvent effects

November 7, 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

Accelerating electrostatic surface potential calculation with multi-scale approximation on graphics processing units

Quantum Chemistry on Graphical Processing Units. 2. Direct Self-Consistent-Field Implementation

Using Graphics Processors to Facilitate Explicit Digital Electrochemical Simulation: Theory of Elliptical Disc Electrodes

Direct numerical simulation of sub-grid structures in gas-solid flow — GPU implementation of macro-scale pseudo-particle modeling

Enhanced molecular dynamics performance with a programmable graphics processor

Sop-GPU: Accelerating biomolecular simulations in the centisecond timescale using graphics processors

A molecular docking system using CUDA

SIML: A Fast SIMD Algorithm for Calculating LINGO Chemical Similarities on GPUs and CPUs

Theory of square, rectangular, and microband electrodes through explicit GPU simulation

Large-scale mixer simulations using massively parallel GPU architectures

Biomolecular electrostatics simulation with a parallel FMM-based BEM, using up to 512 GPUs

Efficient nonbonded interactions for molecular dynamics on a graphics processing unit

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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