high performance computing on graphics processing units: hgpu.org

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Efficient Simulation Techniques for Large-Scale Applications

Jen-Cheng Huang

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Tags: Analytical model, Computer science, CUDA, nVidia, nVidia Quadro 6000, Performance, Thesis

September 26, 2015 by hgpu

Adaptive and Transparent Cache Bypassing for GPUs

Ang Li, Gert-Jan van den Braak, Akash Kumar, Henk Corporaal

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Tags: Benchmarking, cache, Computer science, Memory, nVidia, nVidia GeForce GTX 460, nVidia GeForce GTX 570, nVidia GeForce GTX 690, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX 980, Performance, PTX, Tesla K40, Tesla K80

September 24, 2015 by hgpu

Autotuning Wavefront Patterns for Heterogeneous Architectures

Siddharth Mohanty

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, OpenCL, Performance, Thesis

September 19, 2015 by hgpu

Automatic Online Tuning (AutoTune): Fully Extended Analysis

Eduardo Cesar, Robert Mijacovic, Carmen Navarrete, Carla Guillien, Siegfried Benkner, Martin Sandrieser, Enes Bajrovic, Laurent Morin, Gertvjola Saveta, Anna Sikora

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Tags: Computer science, MPI, OpenCL, Performance

September 19, 2015 by hgpu

CLTune: A Generic Auto-Tuner for OpenCL Kernels

Cedric Nugteren, Valeriu Codreanu

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Source codes

Tags: ATI, ATI Radeon HD 7970, Computer science, Intel HD 5100, nVidia, nVidia GeForce GTX 480, OpenCL, Package, Performance, Tesla K40

September 17, 2015 by hgpu

A Performance Comparison of Algebraic Multigrid Preconditioners on CPUs, GPUs, and Xeon Phis

K. Rupp, J. Weinbub, F. Rudolf, A. Morhammer, T. Grasser, A. Jungel

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Tags: AMD FirePro W9100, ATI, Benchmarking, Computer science, CUDA, Intel Xeon Phi, Linear Algebra, nVidia, OpenCL, OpenMP, Performance, Tesla K20

September 9, 2015 by hgpu

Boosting Java Performance using GPGPUs

James Clarkson, Christos Kotselidis, Gavin Brown, Mikel Lujan

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, Java, nVidia, Performance, Tesla K20

August 28, 2015 by hgpu

Performance Evaluations of Document-Oriented Databases using GPU and Cache Structure

Shin Morishima, Hiroki Matsutani

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Tags: Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 980, Performance

August 24, 2015 by hgpu

Optimizing OpenCL Local Work Group Size With Machine Learning

Markus Schlafli

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Tags: ATI, ATI Radeon HD 7850, Computer science, Machine learning, OpenCL, Performance, PTX

August 18, 2015 by hgpu

A Tool for Automatically Suggesting Source-Code Optimizations for Complex GPU Kernels

Saeed Taheri, Apan Qasem, Martin Burtscher

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Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, Performance, Tesla K20

August 14, 2015 by hgpu

Performance analysis of parallel gravitational N-body codes on large GPU cluster

Siyi Huang, Rainer Spurzem, Peter Berczik

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Tags: Astrophysics, CUDA, GPU cluster, Gravitation, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia, Performance, Tesla M2050, Tesla M2070

August 12, 2015 by hgpu

Optimized Event-Driven Runtime Systems for Programmability and Performance

Sagnak Tasrlar

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Tags: Computer science, Intel Xeon Phi, OpenMP, Performance, Thesis

August 3, 2015 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

Efficient Simulation Techniques for Large-Scale Applications

Adaptive and Transparent Cache Bypassing for GPUs

Autotuning Wavefront Patterns for Heterogeneous Architectures

Automatic Online Tuning (AutoTune): Fully Extended Analysis

CLTune: A Generic Auto-Tuner for OpenCL Kernels

A Performance Comparison of Algebraic Multigrid Preconditioners on CPUs, GPUs, and Xeon Phis

Boosting Java Performance using GPGPUs

Performance Evaluations of Document-Oriented Databases using GPU and Cache Structure

Optimizing OpenCL Local Work Group Size With Machine Learning

A Tool for Automatically Suggesting Source-Code Optimizations for Complex GPU Kernels

Performance analysis of parallel gravitational N-body codes on large GPU cluster

Optimized Event-Driven Runtime Systems for Programmability and Performance

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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