high performance computing on graphics processing units: hgpu.org

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Polymer Field-Theory Simulations on Graphics Processing Units

Kris T. Delaney, Glenn H. Fredrickson

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Tags: Computational Physics, CUDA, Field equations, MPI, nVidia, Physics, Polymer Systems, Soft Condensed Matter, Statistical Mechanics, Tesla C2050

April 25, 2012 by hgpu

LAMMPScuda – a new GPU accelerated Molecular Dynamics Simulations Package and its Application to Ion-Conducting Glasses

Christian Robert Trott

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Tags: Biology, Computational Physics, CUDA, Heterogeneous systems, Molecular dynamics, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 470, Package, Physics, Polymer Systems, Tesla C1060, Thesis

February 13, 2012 by hgpu

A GPU approach to parallel replica-exchange polymer simulations

Jonathan Gross, Wolfhard Janke, Michael Bachmann

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Tags: CUDA, Monte Carlo simulation, nVidia, Physics, Polymer Systems, Programming techniques

September 27, 2011 by hgpu

Massively parallelized replica-exchange simulations of polymers on GPUs

Jonathan Gross, Wolfhard Janke, Michael Bachmann

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Tags: Biomolecules, Computational Physics, Condensed matter, CUDA, Monte Carlo simulation, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Physics, Polymer Systems, Tesla C1060

July 19, 2011 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

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

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

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

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high performance computing on graphics processing units: hgpu.org

Polymer Field-Theory Simulations on Graphics Processing Units

LAMMPScuda – a new GPU accelerated Molecular Dynamics Simulations Package and its Application to Ion-Conducting Glasses

A GPU approach to parallel replica-exchange polymer simulations

Massively parallelized replica-exchange simulations of polymers on GPUs

General purpose molecular dynamics simulations fully implemented on graphics processing units

Recent source codes

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

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

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

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