high performance computing on graphics processing units: hgpu.org

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High-throughput Analysis of Large Microscopy Image Datasets on CPU-GPU Cluster Platforms

George Teodoro, Tony Pan, Tahsin M. Kurc, Jun Kong, Lee A. D. Cooper, Norbert Podhorszki, Scott Klasky, Joel H. Saltz

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Tags: CUDA, GPU cluster, Image processing, Microscopy, nVidia, Prefetch, Tesla M2090

January 6, 2013 by hgpu

Molecular dynamics simulations with many-body potentials on multiple GPUs – the implementation, package and performance

Qing Hou, Min Li, Yulu Zhou, Jiechao Cui, Zhenguo Cui, Jun Wang

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Tags: Computational Physics, CUDA, GPU cluster, Materials Science, Molecular dynamics, nVidia, Physics, Tesla C2050

January 2, 2013 by hgpu

Coulomb, Landau and Maximally Abelian Gauge Fixing in Lattice QCD with Multi-GPUs

Mario Schrock, Hannes Vogt

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Tags: CUDA, GPU cluster, High Energy Physics - Lattice, Mixed precision, Monte Carlo simulation, MPI, nVidia, Physics, QCD, Tesla C2070

December 23, 2012 by hgpu

IceCubes GPGPU’s cluster for extensive MC production

Martin Merck, Dmitry Chirkin, Juan Carlos Diaz Velez, Heath Skarlupka

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Tags: Computational Physics, CUDA, GPU cluster, Monte Carlo simulation, Nuclear physics, nVidia, Particle physics and field theory, Physics, Raytracing, Tesla M2070

December 20, 2012 by hgpu

A Static Load Balancing Scheme for Parallel Volume Rendering on Multi-GPU Clusters

Shusen Liu, Venkatram Vishwanath, Joseph Insley, Mark Hereld, Michael E. Papka, Valerio Pascucci

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Tags: Computer science, CUDA, GPU cluster, nVidia, Rendering, Tesla M2070

December 14, 2012 by hgpu

Multi-level Parallelism for Incompressible Flow Computations on GPU Clusters

Dana A. Jacobsen, Inanc Senocak

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Tags: Computer science, CUDA, Fluid dynamics, GPU cluster, MPI, nVidia, Tesla C1060, Tesla S1070

December 12, 2012 by hgpu

ValuePack: value-based scheduling framework for CPU-GPU clusters

Vignesh T. Ravi, Michela Becchi, Gagan Agrawal, Srimat Chakradhar

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Tags: Cloud, Computer science, CUDA, GPU cluster, Heterogeneous systems, nVidia, Tesla C2050

November 21, 2012 by hgpu

Device specialization in heterogeneous multi-GPU environments

Gabriele Cocco, Antonio Cisternino

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Tags: AMD Fusion, ATI, ATI IL, ATI Radeon HD 5870, Computer science, GPU cluster, Heterogeneous systems, Task scheduling

November 15, 2012 by hgpu

Efficient implementation of data flow graphs on multi-gpu clusters

Vincent Boulos, Sylvain Huet, Vincent Fristot, Luc Salvo, Dominique Houzet

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Tags: Algorithms, CUDA, GPU cluster, Image processing, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 460 M, Signal processing

November 7, 2012 by hgpu

Adapting Irregular Computations to Large CPU-GPU Clusters in the MADNESS Framework

Vlad Slavici, Raghu Varier, Gene Cooperman, Robert J. Harrison

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Tags: Chemistry, Computational chemistry, Computer science, CUBLAS, CUDA, GPU cluster, nVidia, Tesla M2090

November 6, 2012 by hgpu

An Intermediate Library for Multi-GPUs Computing Skeletons

Le Duc Tung, Nguyen Huu Duc, Pham Tuan Anh, Ngo Huy Hoang, Nguyen Minh Thap

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Tags: Code generation, Computer science, CUDA, GPU cluster, nVidia, nVidia GeForce GTX 295

November 1, 2012 by hgpu

Large-scale Monte Carlo simulation of two-dimensional classical XY model using multiple GPUs

Yukihiro Komura, Yutaka Okabe

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Tags: Algorithms, Condensed matter, CUDA, GPU cluster, Monte Carlo simulation, nVidia, nVidia GeForce GTX 580, Physics, Statistical Mechanics

October 24, 2012 by hgpu

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Generating Literature-Driven Scientific Theories at Scale

See all packages

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high performance computing on graphics processing units: hgpu.org

High-throughput Analysis of Large Microscopy Image Datasets on CPU-GPU Cluster Platforms

Molecular dynamics simulations with many-body potentials on multiple GPUs – the implementation, package and performance

Coulomb, Landau and Maximally Abelian Gauge Fixing in Lattice QCD with Multi-GPUs

IceCubes GPGPU’s cluster for extensive MC production

A Static Load Balancing Scheme for Parallel Volume Rendering on Multi-GPU Clusters

Multi-level Parallelism for Incompressible Flow Computations on GPU Clusters

Device specialization in heterogeneous multi-GPU environments

Efficient implementation of data flow graphs on multi-gpu clusters

Adapting Irregular Computations to Large CPU-GPU Clusters in the MADNESS Framework

An Intermediate Library for Multi-GPUs Computing Skeletons

Large-scale Monte Carlo simulation of two-dimensional classical XY model using multiple GPUs

Recent source codes

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Vortex-Optimized Light-weight Toolchain (VOLT)

SciDef: Automated Definition Extraction from Scientific Literature

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

Benchmark suite for LLM inference on NVIDIA consumer GPUs

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

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