high performance computing on graphics processing units: hgpu.org

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Autotuning GEMMs for Fermi

Jakub Kurzak, Stanimire Tomov, Jack Dongarra

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Tags: Benchmarking, BLAS, Computer science, CUDA, Linear Algebra, Mathematical Software, Matrix multiplication, nVidia, Package, Tesla S2050

November 20, 2011 by hgpu

Auto-tunable GPU BLAS

Jarle Erdal Steinsland

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Tags: Algorithms, ATI, ATI Radeon HD 5750, ATI Radeon HD 5870, BLAS, Computer science, Heterogeneous systems, Linear Algebra, Matrix multiplication, nVidia, OpenCL, Optimization, Tesla C2070, Thesis

November 18, 2011 by hgpu

Many-body quantum chemistry on graphics processing units

A. Eugene DePrince III, Jeff R. Hammond, Stephen K. Gray

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Tags: Algorithms, Chemistry, Computational chemistry, CUDA, Heterogeneous systems, Matrix multiplication, Molecular modeling, nVidia, Tesla C2050

November 11, 2011 by hgpu

The conjugate gradient solver accelerated by GPU for solving wave-propagation problems

Chao Chen

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Tags: Algorithms, Computer science, Conjugate gradient solver, CUDA, Linear Algebra, Matrix multiplication, nVidia

November 6, 2011 by hgpu

Matrix inversion speed up with CUDA

Jorge Soriano Pinedo

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Tags: Algorithms, Computer science, CUDA, Image reconstruction, Matrix inversion, Matrix multiplication, nVidia, Tesla C1060, Thesis

October 28, 2011 by hgpu

Fast Implementation of DGEMM on Fermi GPU

Guangming Tan, Linchuan Li, Sean Triechle, Everett Phillips, Yungang Bao, Ninghui Sun

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Tags: Algorithms, Benchmarking, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, Optimization, Performance, Tesla C2050

October 17, 2011 by hgpu

Acceleration of Radiance for Lighting Simulation by Using Parallel Computing with OpenCL

W. Zuo, A. McNeil, M. Wetter, E.S. Lee

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Tags: Algorithms, Computer science, Matrix multiplication, nVidia, nVidia GeForce GTX 460, OpenCL, Raytracing

October 4, 2011 by hgpu

CHPS: An Environment for Collaborative Execution on Heterogeneous Desktop Systems

Aleksandar Ilic, Leonel Sousa

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Tags: Computer science, CUDA, Data parallelism, FFT, Heterogeneous systems, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 285, Signal processing

October 2, 2011 by hgpu

Parallelizing fuzzy rule generation using GPGPU

Takesuke Uenishi, Tomoharu Nakashima and Noriyuki Fujimoto

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 295, Optimization, Performance

September 28, 2011 by hgpu

From CUDA to OpenCL: Towards a Performance-portable Solution for Multi-platform GPU Programming

Peng Du, Rick Weber, Piotr Luszczek, Stanimire Tomov, Gregory Peterson, Jack Dongarra

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Tags: ATI, ATI Radeon HD 5870, Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, OpenCL, Optimization, Performance, Tesla C2050

September 26, 2011 by hgpu

Analyzing Use of OpenCL on the Cell Broadband Engine and a Proposal for OpenCL Extensions

Jens Breitbart, Claudia Fohry

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Tags: Cell processor, Computer science, Distributed computing, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 280, OpenCL, Optimization, Playstation

September 24, 2011 by hgpu

Collaborative execution environment for heterogeneous parallel systems

Aleksandar Ilic, Leonel Sousa

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Tags: Computer science, CUBLAS, CUDA, Data parallelism, FFT, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 285, Presentation

August 28, 2011 by hgpu

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

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Nsight Python: A Python-First Profiling Toolkit for Seamless GPU Kernel Analysis (Tool)

Awesome LLM-Driven Kernel Generation

Towards Automated Kernel Generation in the Era of LLMs

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high performance computing on graphics processing units: hgpu.org

Autotuning GEMMs for Fermi

Auto-tunable GPU BLAS

Many-body quantum chemistry on graphics processing units

The conjugate gradient solver accelerated by GPU for solving wave-propagation problems

Matrix inversion speed up with CUDA

Fast Implementation of DGEMM on Fermi GPU

Acceleration of Radiance for Lighting Simulation by Using Parallel Computing with OpenCL

CHPS: An Environment for Collaborative Execution on Heterogeneous Desktop Systems

Parallelizing fuzzy rule generation using GPGPU

From CUDA to OpenCL: Towards a Performance-portable Solution for Multi-platform GPU Programming

Analyzing Use of OpenCL on the Cell Broadband Engine and a Proposal for OpenCL Extensions

Collaborative execution environment for heterogeneous parallel systems

Recent source codes

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

Nsight Python: a Python kernel profiling interface based on NVIDIA Nsight Tools

Awesome LLM-Driven Kernel Generation

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