high performance computing on graphics processing units: hgpu.org

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Parallel processing on NVIDIA graphics processing units using CUDA

Erik Wynters

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Tags: Computer science, CUDA, Education, nVidia, nVidia GeForce 9800 GT, Tutorial

September 23, 2011 by hgpu

Acceleration of Functional Validation Using GPGPU

Lalith Suresh, Navaneeth Rameshan, M. S. Gaur, Mark Zwolinski, Vijay Laxmi

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Tags: Algorithms, Benchmarking, Computer science, Electronic design automation, nVidia, nVidia Quadro FX 1700, Tesla C870

September 23, 2011 by hgpu

Simple optimizations for an applicative array language for graphics processors

Bradford Larsen

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, Optimization, Programming Languages, Programming techniques

September 23, 2011 by hgpu

Compact data structure and scalable algorithms for the sparse grid technique

Alin Murarasu, Josef Weidendorfer, Gerrit Buse, Daniel Butnaru, Dirk Pfluger

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Tags: Algorithms, Compression, Computer science, CUDA, Numerical Analysis, nVidia, Optimization, Programming techniques, Tesla C1060

September 23, 2011 by hgpu

Colored stochastic shadow maps

Morgan McGuire, Eric Enderton

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Tags: 3D Graphics and Realism, Algorithms, Computer science, Image generation, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Presentation

September 23, 2011 by hgpu

Unstructured grid applications on GPU: performance analysis and improvement

Lizandro Solano-Quinde, Zhi Jian Wang, Brett Bode, Arun K. Somani

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Tags: Algorithms, Computer science, CUDA, nVidia, Performance, Software Engineering, Tesla T10

September 23, 2011 by hgpu

Quantifying NUMA and contention effects in multi-GPU systems

Kyle Spafford, Jeremy S. Meredith, Jeffrey S. Vetter

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Tesla M2070

September 23, 2011 by hgpu

Register packing for cyclic reduction: a case study

Andrew Davidson, John D. Owens

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Tags: Algorithms, Computer science, CUDA, Mathematical Software, nVidia, nVidia GeForce GTX 460, Performance

September 22, 2011 by hgpu

On-the-fly elimination of dynamic irregularities for GPU computing

Eddy Z. Zhang, Yunlian Jiang, Ziyu Guo, Kai Tian, Xipeng Shen

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Tags: Algorithms, Compilers, Computer science, CUDA, nVidia, Optimization, Performance, Programming Languages, Tesla C1060

September 22, 2011 by hgpu

Reducing branch divergence in GPU programs

Tianyi David Han, Tarek S. Abdelrahman

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Tags: Algorithms, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Optimization, Programming Languages

September 22, 2011 by hgpu

A case for neuromorphic ISAs

Atif Hashmi, Andrew Nere, James Jamal Thomas, Mikko Lipasti

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Tags: Algorithms, Computer science, CUDA, Java, MPI, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 280, Optimization, Performance

September 22, 2011 by hgpu

Acceleration of the speed of tissue characterization algorithm for coronary plaque by employing GPGPU technique

Takanori Koga, Shota Furukawa, Noriaki Suetake, Eiji Uchino

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Tags: Algorithms, CUDA, Medicine, Nearest neighbour, nVidia, Surgical simulation, Tesla C1060

September 22, 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Parallel processing on NVIDIA graphics processing units using CUDA

Acceleration of Functional Validation Using GPGPU

Simple optimizations for an applicative array language for graphics processors

Compact data structure and scalable algorithms for the sparse grid technique

Colored stochastic shadow maps

Unstructured grid applications on GPU: performance analysis and improvement

Quantifying NUMA and contention effects in multi-GPU systems

Register packing for cyclic reduction: a case study

On-the-fly elimination of dynamic irregularities for GPU computing

Reducing branch divergence in GPU programs

A case for neuromorphic ISAs

Acceleration of the speed of tissue characterization algorithm for coronary plaque by employing GPGPU technique

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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