high performance computing on graphics processing units: hgpu.org

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FPGA-Based Accelerator Design from a Domain-Specific Language

M. Akif Ozkan, Oliver Reiche, Frank Hannig, Jurgen Teich

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Tags: Algorithms, Computer vision, FPGA, Heterogeneous systems, Image processing, nVidia, nVidia Jetson TK1, OpenCL, Tesla K20

July 26, 2016 by hgpu

Gerbil: A Fast and Memory-Efficient k-mer Counter with GPU-Support

Marius Erbert, Steffen Rechner, Matthias Muller-Hannemann

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Tags: Bioinformatics, Biology, Computer science, CUDA, nVidia, nVidia GeForce GTX 970, nVidia GeForce GTX Titan, Package

July 26, 2016 by hgpu

Algorithmic Trading: A brief, computational finance case study on data centre FPGAs

Gordon Inggs

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Tags: AMD FirePro W5000, ATI, Computational finance, Computer science, Finance, FPGA, nVidia, nVidia Quadro K4000, OpenCL

July 20, 2016 by hgpu

Lowering IrGL to CUDA

Sreepathi Pai, Keshav Pingali

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

July 20, 2016 by hgpu

THOR: A New and Flexible Global Circulation Model to Explore Planetary Atmospheres

Joao M. Mendonca, Simon L. Grimm, Luc Grosheintz, Kevin Heng

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Tags: Astrophysics, CUDA, Earth and Planetary Astrophysics, Earth and Space Sciences, nVidia, Tesla K20

July 20, 2016 by hgpu

Scientific Computing Using Consumer Video-Gaming Hardware Devices

Glenn Volkema, Gaurav Khanna

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Tags: AMD Radeon R9 Fury X, ATI, Benchmarking, Computer science, nVidia, nVidia Tegra TX1, OpenCL, Package, Performance, Tesla K40

July 20, 2016 by hgpu

Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off

Hokchhay Tann, Soheil Hashemi, R. Iris Bahar, Sherief Reda

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Tags: Algorithms, ARM, ASIC, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia Tegra TX1

July 20, 2016 by hgpu

Accelerating the Conjugate Gradient Algorithm with GPUs in CFD Simulations

Hartwig Anzt, Marc Baboulin, Jack Dongarra, Yvan Fournier, Frank Hulsemann, Amal Khabou, Yushan Wang

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Tags: cfd, Conjugate gradient solver, CUDA, Fluid dynamics, nVidia, OpenCL, Tesla K40

July 18, 2016 by hgpu

IODA: an Input/Output Deep Architecture for image labeling

J. Lerouge, R. Herault, Clement Chatelain, F. Jardin, R. Modzelewski

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Python, Tesla C1060, Theano

July 18, 2016 by hgpu

Heterogeneous Computing for Data Stream Mining

Vladimir Petko

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Tags: Algorithms, Computer science, Data mining, Heterogeneous systems, Machine learning, Nearest neighbour, nVidia, OpenCL, Tesla C2050, Thesis

July 18, 2016 by hgpu

Stability and Performance of Various Singular Value QR Implementations on Multicore CPU with a GPU

Ichitaro Yamazaki, Stanimire Tomov, Jack Dongarra

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Tags: BLAS, Computer science, CUDA, Linear Algebra, Matrix decomposition, Mixed precision, Numerical Analysis, nVidia, Performance, Tesla K20

July 16, 2016 by hgpu

An investigation of GPU-based stiff chemical kinetics integration methods

Nicholas J. Curtis, Kyle E. Niemeyer, Chih-Jen Sung

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Tags: Algorithms, Chemistry, Computational Physics, CUDA, nVidia, Physics, Tesla C2075

July 16, 2016 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

FPGA-Based Accelerator Design from a Domain-Specific Language

Gerbil: A Fast and Memory-Efficient k-mer Counter with GPU-Support

Algorithmic Trading: A brief, computational finance case study on data centre FPGAs

Lowering IrGL to CUDA

THOR: A New and Flexible Global Circulation Model to Explore Planetary Atmospheres

Scientific Computing Using Consumer Video-Gaming Hardware Devices

Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off

Accelerating the Conjugate Gradient Algorithm with GPUs in CFD Simulations

IODA: an Input/Output Deep Architecture for image labeling

Heterogeneous Computing for Data Stream Mining

Stability and Performance of Various Singular Value QR Implementations on Multicore CPU with a GPU

An investigation of GPU-based stiff chemical kinetics integration methods

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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