high performance computing on graphics processing units: hgpu.org

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Nsight Python: A Python-First Profiling Toolkit for Seamless GPU Kernel Analysis (Tool)

Bastian Hagedorn, Alexander Collins, Tony Mongkolsmai, Vinod Grover

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Tags: Computer science, CUDA, nVidia, nVidia B200, Package, Performance, Profiling, Python, Triton

February 2, 2026 by hgpu

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

Erik D. Huckvale, Hunter N.B. Moseley

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Tags: Computer science, CUDA, nVidia, Package, Performance, Profiling, Python

April 7, 2024 by hgpu

Low-Overhead Trace Collection and Profiling on GPU Compute Kernels

Sébastien Darche, Michel R. Dagenais

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Tags: AMD Radeon Instinct MI100, ATI, Benchmarking, Computer science, CUDA, HIP, nVidia, Package, Performance, Profiling

March 3, 2024 by hgpu

cf4ocl: a C framework for OpenCL

Nuno Fachada, Vitor V. Lopes, Rui C. Martins, Agostinho C. Rosa

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Tags: GPGPU, HPC, OpenCL, Profiling

September 6, 2016 by fakenmc

GPU performance analysis of a nodal discontinuous Galerkin method for acoustic and elastic models

Axel Modave, Amik St-Cyr, Tim Warburton

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Tags: BLAS, Computational Physics, CUBLAS, CUDA, FEM, Finite element method, Geoscience, Linear Algebra, nVidia, nVidia GeForce GTX 980, OCCA, Physics, Profiling, Seismic modeling, Seismology

March 1, 2016 by hgpu

Guided Profiling for Auto-Tuning Array Layouts on GPUs

Nicolas Weber, Sandra C. Amend and Michael Goesele

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Tags: Array Layouts, Auto-Tuning, CUDA, Profiling

February 9, 2016 by mergian

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

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

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high performance computing on graphics processing units: hgpu.org

Nsight Python: A Python-First Profiling Toolkit for Seamless GPU Kernel Analysis (Tool)

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

Low-Overhead Trace Collection and Profiling on GPU Compute Kernels

cf4ocl: a C framework for OpenCL

GPU performance analysis of a nodal discontinuous Galerkin method for acoustic and elastic models

Guided Profiling for Auto-Tuning Array Layouts on GPUs

Recent source codes

CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation

CUDABench: Benchmarking LLMs for Text-to-CUDA Generation

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

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