high performance computing on graphics processing units: hgpu.org

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Programming Many-Core Chips

Andras Vajda

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Tags: Book, Cluster computing, Computer science, CUDA, MPI, nVidia, OpenCL, OpenMP, Programming techniques

September 8, 2011 by hgpu

GPU Computation in Bioinspired Algorithms: A Review

M. G. Arenas, A. M. Mora, G. Romero and P. A. Castillo

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Tags: Algorithms, ATI, Bioinformatics, Biology, Computational biology, CUDA, nVidia, OpenCL, Overview, Review

September 8, 2011 by hgpu

Towards GPGPU Assisted Computing in Virtualized Environments

Thilo Schmitt, Alexander Weggerle, Christian Himpel, Peter Schulthess

Tags: ATI, ATI Stream, Computer science, CUDA, nVidia, OpenCL, Review, Virtualization

September 8, 2011 by hgpu

Implementing Independent Component Analysis in General-Purpose GPU Architectures

Jacquelyne Forgette, Renata Wachowiak-Smolikova, Mark Wachowiak

Tags: Algorithms, CUDA, Heterogeneous systems, Linear Algebra, nVidia, nVidia GeForce GTX 260, OpenCL, Signal processing

September 8, 2011 by hgpu

Automatic OpenCL Device Characterization: Guiding Optimized Kernel Design

Peter Thoman, Klaus Kofler, Heiko Studt, John Thomson, Thomas Fahringer

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Tags: Algorithms, ATI, ATI Radeon HD 5870, Benchmarking, Cell processor, Computer science, nVidia, nVidia GeForce GTX 275, nVidia GeForce GTX 460, OpenCL, Optimization, Tesla C2050

September 8, 2011 by hgpu

Pegasus: coordinated scheduling for virtualized accelerator-based systems

Vishakha Gupta, Karsten Schwan, Niraj Tolia, Vanish Talwar, Parthasarathy Ranganathan

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 9800 GTX, Operating systems, Task scheduling, Virtualization

September 7, 2011 by hgpu

Automatic CPU-GPU communication management and optimization

Thomas B. Jablin, Prakash Prabhu, James A. Jablin, Nick P. Johnson, Stephen R. Beard, David I. August

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Optimization, Performance, Programming techniques

September 7, 2011 by hgpu

High performance computation and interactive display of molecular orbitals on GPUs and multi-core CPUs

John E. Stone, Jan Saam, David J. Hardy, Kirby L. Vandivort, Wen-mei W. Hwu, Klaus Schulten

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Tags: Algorithms, Chemistry, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, Performance, Programming techniques, Rendering, Visualization

September 7, 2011 by hgpu

CUDA-level performance with python-level productivity for Gaussian mixture model applications

H. Cook, E. Gonina, S. Kamil, G. Friedland, D. Patterson, A. Fox

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Tags: Algorithms, Code generation, Computer science, CUDA, nVidia, OpenCL, Python

September 7, 2011 by hgpu

Operating systems must support GPU abstractions

Christopher J. Rossbach, Jon Currey, Emmett Witchel

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Tags: Computer science, nVidia, nVidia GeForce GT 230, Operating systems

September 7, 2011 by hgpu

GPU-based asynchronous particle swarm optimization

Luca Mussi, Youssef S.G. Nashed, Stefano Cagnoni

Tags: Algorithms, Computer science, CUDA, Genetic programming, nVidia, Optimization, Performance

September 7, 2011 by hgpu

FPGA vs. multi-core CPUs vs. GPUs: hands-on experience with a sorting application

Cristian Grozea, Zorana Bankovic, Pavel Laskov

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Tags: Algorithms, Computer science, CUDA, FPGA, nVidia, nVidia Quadro FX 5600, OpenMP, Sorting

September 7, 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

Programming Many-Core Chips

GPU Computation in Bioinspired Algorithms: A Review

Towards GPGPU Assisted Computing in Virtualized Environments

Implementing Independent Component Analysis in General-Purpose GPU Architectures

Automatic OpenCL Device Characterization: Guiding Optimized Kernel Design

Pegasus: coordinated scheduling for virtualized accelerator-based systems

Automatic CPU-GPU communication management and optimization

High performance computation and interactive display of molecular orbitals on GPUs and multi-core CPUs

CUDA-level performance with python-level productivity for Gaussian mixture model applications

Operating systems must support GPU abstractions

GPU-based asynchronous particle swarm optimization

FPGA vs. multi-core CPUs vs. GPUs: hands-on experience with a sorting application

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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