high performance computing on graphics processing units: hgpu.org

Jairo Panetta, Thiago Teixeira, Paulo R. P. de Souza Filho, Carlos A. da Cunha Finho, David Sotelo, Fernando M. da Motta, Silvio S. Pinheiro, Ivan P. Junior, Andre L. Rosa, Luiz R. Monnerat, Leandro T. Carneiro, Carlos H. B. de Albrecht

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Tags: CUDA, Geoscience, GPU cluster, nVidia, Physics, Tesla C1060

October 28, 2010 by hgpu

Adapting a message-driven parallel application to GPU-accelerated clusters

James C. Phillips, John E. Stone, Klaus Schulten

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Tags: Computer science, CUDA, GPU cluster, Molecular dynamics, nVidia, nVidia Quadro FX 5600

October 28, 2010 by hgpu

GAMER: a GPU-Accelerated Adaptive Mesh Refinement Code for Astrophysics

Hsi-Yu Schive, Yu-Chih Tsai, Tzihong Chiueh

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Tags: Astrophysics, Cosmology and Extragalactic Astrophysics, CUDA, GPU cluster, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia

October 28, 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

GPU Cluster for High Performance Computing

Zhe Fan, Feng Qiu, A. Kaufman, S. Yoakum-Stover

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Tags: Computer science, Fluid dynamics, GPU cluster, Lattice Boltzmann model, nVidia, nVidia GeForce FX 5800 Ultra

October 27, 2010 by hgpu

Multi-GPU accelerated multi-spin Monte Carlo simulations of the 2D Ising model

Benjamin Block,Peter Virnau,Tobias Preis

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Tags: CUDA, GPU cluster, Ising model, Monte Carlo simulation, nVidia, Package, Physics, Tesla C1060

October 27, 2010 by hgpu

SeedFold: Scaling Biomolecular Structure Prediction

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

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Implementation of a Lattice–Boltzmann method for numerical fluid mechanics using the nVIDIA CUDA technology

General purpose molecular dynamics simulations fully implemented on graphics processing units

High-order finite-element seismic wave propagation modeling with MPI on a large GPU cluster

AUTO-GC: Automatic translation of data mining applications to GPU clusters

Accelerating Kirchhoff Migration by CPU and GPU Cooperation

Adapting a message-driven parallel application to GPU-accelerated clusters

GAMER: a GPU-Accelerated Adaptive Mesh Refinement Code for Astrophysics

ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale

GPU Cluster for High Performance Computing

Multi-GPU accelerated multi-spin Monte Carlo simulations of the 2D Ising model

Recent source codes

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

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

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

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