high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications

GPU-accelerated differential evolutionary Markov Chain Monte Carlo method for multi-objective optimization over continuous space

Weihang Zhu, Yaohang Li

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Tags: Mathematics, nVidia, nVidia GeForce GTX 280, Optimization

December 17, 2010 by hgpu

GPU-based Monte Carlo simulation for light propagation in complex heterogeneous tissues

Nunu Ren, Jimin Liang, Xiaochao Qu, Jianfeng Li, Bingjia Lu, Jie Tian

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Tags: CUDA, Monte Carlo simulation, nVidia, nVidia GeForce GTX 260, Optics, Package, Physics

December 17, 2010 by hgpu

Programming massively parallel processors : A Hands – on approach

David B Kirk and Wen-Mei W. Hwu

Tags: Algorithms, Book, Computer science, CUDA, nVidia, OpenCL, Review

December 17, 2010 by hgpu

Accelerating Correlated Quantum Chemistry Calculations Using Graphical Processing Units

Mark Watson, Roberto Olivares-Amaya, Richard G. Edgar, Alan Aspuru-Guzik

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Tags: Chemistry, Computational chemistry, Molecular modeling, nVidia, Tesla C1060

December 17, 2010 by hgpu

A GPU solvent-solvent interaction calculation accelerator for biomolecular simulations using the GROMOS software

Nathan Schmid, Mathias Botschi, Wilfred F. Van Gunsteren

Tags: Chemistry, Computational chemistry, CUDA, Molecular dynamics, nVidia, nVidia Quadro FX 5800, Solvent effects

December 17, 2010 by hgpu

Molecular Dynamics on a Grand Scale

Matthew James Mawson Breeze

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Tags: ATI, ATI Radeon X1600, Chemistry, GLSL, Molecular dynamics, nVidia, nVidia GeForce 6200, nVidia GeForce 8800 GTS, OpenGL, Thesis

December 17, 2010 by hgpu

The Accelerated Universe

Adrian Pope, Salman Habib, Zarija Lukic, David Daniel, Patricia Fasel, Nehal Desai, and Katrin Heitmann

Tags: Astrophysics, Physics

December 17, 2010 by hgpu

Accelerating Monte Carlo simulations with an NVIDIA graphics processor

Paul Martinsen, Johannes Blaschke, Rainer Kunnemeyer and Robert Jordan

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Tags: CUDA, Monte Carlo simulation, nVidia, nVidia GeForce 8800 GT, Physics

December 16, 2010 by hgpu

OpenMM: A Hardware-Independent Framework for Molecular Simulations

Peter Eastman, Vijay Pande

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Tags: Chemistry, Computational chemistry, Molecular modeling, Package

December 16, 2010 by hgpu

A massively multicore parallelization of the Kohn-Sham energy gradients

Philip Brown, Christopher J. Woods, Simon McIntosh-Smith, Frederick R. Manby

Tags: Chemistry, Computational chemistry, Molecular modeling

December 16, 2010 by hgpu

Real-time optical micro-manipulation using optimized holograms generated on the GPU

S. Bianchi, R. Di Leonardo

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Tags: Computer holography, CUDA, nVidia, nVidia GeForce GTX 260, Optics, Physics

December 16, 2010 by hgpu

Revolutionary technologies for acceleration of emerging petascale applications

Rupak Biswas, Leonid Oliker and Jeffrey Vetter

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Tags: Computer science, Programming techniques

December 16, 2010 by hgpu

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

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

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high performance computing on graphics processing units: hgpu.org

Applications

GPU-accelerated differential evolutionary Markov Chain Monte Carlo method for multi-objective optimization over continuous space

GPU-based Monte Carlo simulation for light propagation in complex heterogeneous tissues

Programming massively parallel processors : A Hands – on approach

Accelerating Correlated Quantum Chemistry Calculations Using Graphical Processing Units

A GPU solvent-solvent interaction calculation accelerator for biomolecular simulations using the GROMOS software

Molecular Dynamics on a Grand Scale

The Accelerated Universe

Accelerating Monte Carlo simulations with an NVIDIA graphics processor

OpenMM: A Hardware-Independent Framework for Molecular Simulations

A massively multicore parallelization of the Kohn-Sham energy gradients

Real-time optical micro-manipulation using optimized holograms generated on the GPU

Revolutionary technologies for acceleration of emerging petascale applications

Recent source codes

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

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

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