high performance computing on graphics processing units: hgpu.org

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Evaluating one-sided programming models for GPU cluster computations

Jeff R. Hammond and A. Eugene DePrince III

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Tags: Chemistry, Computer science, CUDA, GPU cluster, Matrix multiplication, nVidia, Tesla T10

February 17, 2011 by hgpu

Accelerating Cosmological Data Analysis with Graphics Processors

Dylan W. Roeh, Volodymyr V. Kindratenko, Robert J. Brunner

Tags: Astrophysics, Cosmology, CUDA, GPU cluster, MPI, nVidia, Physics, Programming techniques

February 15, 2011 by hgpu

Direct Self-Consistent Field Computations on GPU Clusters

Guochun Shi, V. Kindratenko, I. Ufimtsev, T. Martinez

Tags: Chemistry, CUDA, GPU cluster, Molecular modeling, nVidia

February 15, 2011 by hgpu

Fast Analysis of Molecular Dynamics Trajectories with Graphics Processing Units – Radial Distribution Function Histogramming

Benjamin G. Levine, John E. Stone, Axel Kohlmeyer

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Tags: Chemistry, CUDA, GPU cluster, Molecular dynamics, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Tesla C1060, Tesla C2050, Tesla S1070

February 9, 2011 by hgpu

Axel: a heterogeneous cluster with FPGAs and GPUs

Kuen Hung Tsoi, Wayne Luk

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Tags: Computer science, CUDA, FPGA, GPU cluster, Heterogeneous systems, nVidia, OpenMPI, Tesla C1060

February 7, 2011 by hgpu

QP: A Heterogeneous Multi-Accelerator Cluster

Michael Showerman, Jeremy Enos, Avneesh Pant, Volodymyr Kindratenko, Craig Steffen, Robert Pennington, Wen-mei Hwu

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Tags: Computer science, CUDA, FPGA, GPU cluster, nVidia, nVidia Quadro FX 5600, Review

February 4, 2011 by hgpu

Quantifying the Impact of GPUs on Performance and Energy Efficiency in HPC Clusters

J. Enos, C. Steffen, J. Fullop, M. Showerman, G. Shi, K. Esler, V. Kindratenko, J. Stone, J. Phillips

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Tags: Computer science, CUDA, Energy-efficient computing, GPU cluster, nVidia, Performance

February 3, 2011 by hgpu

Asynchronous Communication Schemes for Finite Difference Methods on Multiple GPUs

Daniel P. Playne, Kenneth A. Hawick

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Tags: Computer science, CUDA, Finite difference, GPU cluster, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 295

January 30, 2011 by hgpu

The Saga of Landau-Gauge Propagators: Gathering New Ammo

Attilio Cucchieri, Tereza Mendes

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Tags: GPU cluster, High Energy Physics - Lattice, Monte Carlo simulation, nVidia, Physics, Tesla S1070

January 27, 2011 by hgpu

Hierarchical Agglomerative Clustering Using Graphics Processor with Compute Unified Device Architecture

S. A. Arul Shalom, Manoranjan Dash, Minh Tue, Nithin Wilson

Tags: Computer science, CUDA, GPU cluster, nVidia

January 26, 2011 by hgpu

CUDASA: Compute Unified Device and Systems Architecture

M. Strengert, C. Muller, C. Dachsbacher, and T. Ertl

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Tags: Computer science, CUDA, GPU cluster, High-level Languages, nVidia, nVidia GeForce 8800 GT, nVidia GeForce 8800 GTX, nVidia Quadro FX 5600

January 24, 2011 by hgpu

Implementing molecular dynamics on hybrid high performance computers – short range forces

W. Michael Brown, Peng Wang, Steven J. Plimpton, Arnold N. Tharrington

Tags: Chemistry, CUDA, GPU cluster, Molecular dynamics, nVidia, Physics

January 22, 2011 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

Evaluating one-sided programming models for GPU cluster computations

Accelerating Cosmological Data Analysis with Graphics Processors

Direct Self-Consistent Field Computations on GPU Clusters

Fast Analysis of Molecular Dynamics Trajectories with Graphics Processing Units – Radial Distribution Function Histogramming

Axel: a heterogeneous cluster with FPGAs and GPUs

QP: A Heterogeneous Multi-Accelerator Cluster

Quantifying the Impact of GPUs on Performance and Energy Efficiency in HPC Clusters

Asynchronous Communication Schemes for Finite Difference Methods on Multiple GPUs

The Saga of Landau-Gauge Propagators: Gathering New Ammo

Hierarchical Agglomerative Clustering Using Graphics Processor with Compute Unified Device Architecture

CUDASA: Compute Unified Device and Systems Architecture

Implementing molecular dynamics on hybrid high performance computers – short range forces

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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