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VOCL: An Optimized Environment for Transparent Virtualization of Graphics Processing Units

Shucai Xiao, Pavan Balaji, Qian Zhu, Rajeev Thakur, Susan Coghlan, Heshan Lin, Gaojin Wen, Jue Hong, Wu-chun Feng

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Tags: Computer science, nVidia, OpenCL, Tesla M2070, Virtualization

March 18, 2012 by hgpu

Globally scheduled real-time multiprocessor systems with GPUs

Glenn A. Elliott, James H. Anderson

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 295, Task scheduling

March 16, 2012 by hgpu

CUDA 2D Stencil Computations for the Jacobi Method

Jose Maria Cecilia, Jose Manuel Garcia, Manuel Ujaldon

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Tags: Algorithms, Computer science, CUDA, Data parallelism, Differential equations, Linear Algebra, nVidia, Optimization, Tesla C1060, Tesla C870

March 16, 2012 by hgpu

Parallel Sparse Linear Algebra for Multi-core and Many-core Platforms: Parallel Solvers and Preconditioners

Dimitar Lukarski

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Tags: Algorithms, Computer science, CUDA, Differential equations, Factorization, FEM, Finite difference, Finite element method, Linear Algebra, nVidia, nVidia GeForce GTX 580, Partial differential equations, PDEs, Sparse matrix, Tesla M1060, Tesla S1070, Thesis

March 16, 2012 by hgpu

Developing a CUDA solver for large sparse matrices for MARIN

Martijn de Jong

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Tags: Computer science, CUDA, Mathematics, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 580, nVidia Quadro NVS 295, Thesis

March 16, 2012 by hgpu

Multi-platform Linear Algebra

E. Ketelaer, D. Lukarski

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Tags: Computer science, CUDA, Differential equations, Distributed data structures, Linear Algebra, nVidia, Package, Partial differential equations, PDEs

March 16, 2012 by hgpu

On the Use of Small 2D Convolutions on GPUs

Shams A.H. Al Umairy, Alexander S. van Amesfoort, Irwan D. Setija, Martijn C. van Beurden, Henk J. Sips

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Tags: Computer science, CUDA, Electrodynamics, FFT, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, Optimization, Physics, Tesla C1060

March 15, 2012 by hgpu

Iterative Statistical Kernels on Contemporary GPUs

Thilina Gunarathne, Bimalee Salpitikorala, Arun Chauhan, Geoffrey Fox

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Tags: Algorithms, Cloud, Clustering, Computer science, nVidia, OpenCL, Sparse matrix, Tesla C1060, Tesla C2070, Tesla M2050

March 15, 2012 by hgpu

Performance analysis and optimization of the OP2 framework on many-core architectures

M.B. Giles, G.R. Mudalige, Z. Sharif, G. Markall, P.H.J. Kelly

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Tags: Benchmarking, Computer science, CUDA, Fluid dynamics, nVidia, OpenMP, Optimization, Package, Performance, Tesla C2050

March 15, 2012 by hgpu

Compressed Multiple-Row Storage Format

Zbigniew Koza, Maciej Matyka, Sebastian Szkoda, Lukasz Miroslaw

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Tags: Algorithms, Compression, Computational Physics, CUDA, nVidia, nVidia GeForce GTX 480, Physics, Sparse matrix, Tesla C2070

March 15, 2012 by hgpu

A Spiking Neural P system simulator based on CUDA

Francis Cabarle, Henry Adorna, Miguel A. Martinez-del-Amor

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Tags: Algorithms, Computer science, CUDA, Membrane computing, nVidia, nVidia GeForce 9400 M, Package

March 15, 2012 by hgpu

Targeting heterogeneous architectures via macro data flow

M. Aldinucci, M. Danelutto, P. Kilpatrick, M. Torquati

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Tags: Algorithms, Computer science, Heterogeneous systems, nVidia, Tesla C2050

March 13, 2012 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

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high performance computing on graphics processing units: hgpu.org

Applications

VOCL: An Optimized Environment for Transparent Virtualization of Graphics Processing Units

Globally scheduled real-time multiprocessor systems with GPUs

CUDA 2D Stencil Computations for the Jacobi Method

Parallel Sparse Linear Algebra for Multi-core and Many-core Platforms: Parallel Solvers and Preconditioners

Developing a CUDA solver for large sparse matrices for MARIN

Multi-platform Linear Algebra

On the Use of Small 2D Convolutions on GPUs

Performance analysis and optimization of the OP2 framework on many-core architectures

Compressed Multiple-Row Storage Format

A Spiking Neural P system simulator based on CUDA

Targeting heterogeneous architectures via macro data flow

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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