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Using the High Productivity Language Chapel to Target GPGPU Architectures

Albert Sidelnik, Maria J. Garzaran, David Padua, Bradford L. Chamberlain

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Tags: Compilers, Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 280, Optimization

November 19, 2011 by hgpu

Anisotropic mesh coarsening and refinement on GPU architecture

Matthew Potter

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Tags: Algorithms, CUDA, Differential equations, Finite volume method, Fluid dynamics, nVidia, Partial differential equations, PDEs, Tesla M2050, Thesis

November 19, 2011 by hgpu

Exploiting concurrent kernel execution on graphic processing units

Lingyuan Wang, Miaoqing Huang, Tarek El-Ghazawi

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Tags: Computer science, CUDA, nVidia, Performance, Programming techniques, Tesla C2070

November 19, 2011 by hgpu

Towards Efficient GPU Sharing on Multicore Processors

Lingyuan Wang, Miaoqing Huang, Tarek El-Ghazawi

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Tags: Computer science, CUDA, GPU cluster, nVidia, Performance, Programming techniques, Tesla C1060, Tesla C2070

November 19, 2011 by hgpu

ShoveRand: a model-driven framework to easily generate random numbers on GP-GPU

Jonathan Passerat-Palmbach, Claude Mazel, David R.C. Hill

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Tags: Computer science, CUDA, nVidia, Pseudo-random number generators, Stochastic simulation, Tesla C1060

November 19, 2011 by hgpu

SGPU 2: a runtime system for using large applications on clusters of hybrid nodes

Matthieu Ospici, Dimitri Komatitsch, Jean-Francois Mehaut, Thierry Deutsch

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Tags: Benchmarking, Computer science, CUDA, Hybrid computing, nVidia, Tesla T10

November 19, 2011 by hgpu

Predictive Modeling and Analysis of OP2 on Distributed Memory GPU Clusters

G.R. Mudalige, M.B. Giles, C. Bertolli, P.H.J Kelly

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Tags: Benchmarking, Computer science, CUDA, Fluid dynamics, GPU cluster, Heterogeneous systems, nVidia, nVidia GeForce GTX 560 Ti, Performance, Tesla C2070

November 19, 2011 by hgpu

Tapping the supercomputer under your desk: Solving dynamic equilibrium models with graphics processors

Eric M. Aldrich, Jesus Fernandez-Villaverde, A. Ronald Gallant, Juan F. Rubio-Ramirez

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Tags: Algorithms, CUDA, Finance, nVidia, nVidia GeForce GTX 280, Package

November 18, 2011 by hgpu

Optimizing the multipole-to-local operator in the fast multipole method for graphical processing units

Toru Takahashi1, Cris Cecka, William Fong, Eric Darve

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Tags: Algorithms, CUDA, Fast multipole method, nVidia, Package, Physics, Tesla C1060, Tesla C2050

November 18, 2011 by hgpu

Dax Toolkit: A Proposed Framework for Data Analysis and Visualization at Extreme Scale

Kenneth Moreland, Utkarsh Ayachit, Berk Geveci, Kwan-Liu Ma

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Tags: Algorithms, Computer science, CUDA, nVidia, Programming techniques, Tesla C2050, Visualization

November 17, 2011 by hgpu

Compilation for Heterogeneous Computing: Automating Analyses, Transformations and Decisions

Serge Guelton, Francois Irigoin, Ronan Keryell

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Tags: Code generation, Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, nVidia Quadro FX 2700 M, Package, Signal processing

November 17, 2011 by hgpu

Programming Future Parallel Architectures with Haskell and Intel ArBB

Bo Joel Svensson, Ryan Newton

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Performance, Programming Languages

November 17, 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

Programming

Using the High Productivity Language Chapel to Target GPGPU Architectures

Anisotropic mesh coarsening and refinement on GPU architecture

Exploiting concurrent kernel execution on graphic processing units

Towards Efficient GPU Sharing on Multicore Processors

ShoveRand: a model-driven framework to easily generate random numbers on GP-GPU

SGPU 2: a runtime system for using large applications on clusters of hybrid nodes

Predictive Modeling and Analysis of OP2 on Distributed Memory GPU Clusters

Tapping the supercomputer under your desk: Solving dynamic equilibrium models with graphics processors

Optimizing the multipole-to-local operator in the fast multipole method for graphical processing units

Dax Toolkit: A Proposed Framework for Data Analysis and Visualization at Extreme Scale

Compilation for Heterogeneous Computing: Automating Analyses, Transformations and Decisions

Programming Future Parallel Architectures with Haskell and Intel ArBB

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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