high performance computing on graphics processing units: hgpu.org

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Towards metaprogramming for parallel systems on a chip

Lee Howes, Anton Lokhmotov, Alastair F. Donaldson, Paul H.J. Kell

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Performance, Programming techniques

August 22, 2011 by hgpu

Load Balancing versus Occupancy Maximization on Graphics Processing Units: The Generalized Hough Transform as a Case Study

Juan Gomez-Luna, Jose Maria Gonzalez-Linares, Jose Ignacio Benavides, Emilio L. Zapata, Nicolas Guil

Tags: Computer science, CUDA, Image processing, nVidia, Programming techniques

August 22, 2011 by hgpu

Top ten ways to make formal methods for HPC practical

Ganesh Gopalakrishnan, Robert M. Kirby

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Tags: Computer science, CUDA, MPI, nVidia, OpenCL, OpenMP, Performance

August 22, 2011 by hgpu

Compiler and runtime support for enabling generalized reduction computations on heterogeneous parallel configurations

Vignesh T. Ravi, Wenjing Ma, David Chiu, Gagan Agrawal

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 9800 GTX

August 22, 2011 by hgpu

Reusable software components for accelerator-based clusters

M. Mustafa Rafique, Ali R. Butt, Eli Tilevich

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 9600 M GT, Performance, Playstation

August 22, 2011 by hgpu

Improving programmability of heterogeneous many-core systems via explicit platform descriptions

Martin Sandrieser, Siegfried Benkner, Sabri Pllana

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, OpenCL, OpenMP, Presentation

August 22, 2011 by hgpu

Accelerating Haskell array codes with multicore GPUs

Manuel M.T. Chakravarty, Gabriele Keller, Sean Lee, Trevor L. McDonell, Vinod Grover

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Tags: Code generation, Computer science, CUBLAS, CUDA, Data parallelism, nVidia, Performance, Programming Languages, Tesla T10

August 22, 2011 by hgpu

A programming model for GPU-based parallel computing with scalability and abstraction

Balazs Domonkos, Gabor Jakab

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Tags: 3D Graphics and Realism, Computer science, CUDA, GPU cluster, MPI, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 280, OpenMP, Programming techniques

August 22, 2011 by hgpu

EpiGPU

Gibran Hemani, Athanasios Theocharidis, Wenhua Wei, Chris Haley

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Tags: ATI, Biology, Genetics, nVidia, OpenCL

August 21, 2011 by hgpu

Multi- and many-core data mining with adaptive sparse grids

Alexander Heinecke, Dirk Pfluger

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Tags: Algorithms, Computer science, Data mining, Mathematical Software, nVidia, nVidia GeForce GTX 470, OpenCL, Performance, Presentation

August 21, 2011 by hgpu

Sponge: portable stream programming on graphics engines

Amir H. Hormati, Mehrzad Samadi, Mark Woh, Trevor Mudge, Scott Mahlke

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Tags: Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Optimization, Performance, Programming Languages

August 21, 2011 by hgpu

Breaking the GPU programming barrier with the auto-parallelising SAC compiler

Jing Guo, Jeyarajan Thiyagalingam, Sven-Bodo Scholz

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

August 21, 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

Towards metaprogramming for parallel systems on a chip

Load Balancing versus Occupancy Maximization on Graphics Processing Units: The Generalized Hough Transform as a Case Study

Top ten ways to make formal methods for HPC practical

Compiler and runtime support for enabling generalized reduction computations on heterogeneous parallel configurations

Reusable software components for accelerator-based clusters

Improving programmability of heterogeneous many-core systems via explicit platform descriptions

Accelerating Haskell array codes with multicore GPUs

A programming model for GPU-based parallel computing with scalability and abstraction

EpiGPU

Multi- and many-core data mining with adaptive sparse grids

Sponge: portable stream programming on graphics engines

Breaking the GPU programming barrier with the auto-parallelising SAC compiler

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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