high performance computing on graphics processing units: hgpu.org

hgpu.org » Review

Scientific and Engineering Computing Using ATI Stream Technology

Amr Bayoumi, Michael Chu, Yasser Hanafy, Patricia Harrell, Gamal Refai-Ahmed

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Tags: AMD FireStream 9270, ATI, ATI Stream, Brook, Computer science, OpenMP, Programming techniques, Review

June 26, 2011 by hgpu

Exploiting Memory Access Patterns to Improve Memory Performance in Data-Parallel Architectures

Byunghyun Jang, Dana Schaa, Perhaad Mistry, David Kaeli

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Tags: ATI, ATI Radeon HD 3870, Brook, Computer science, CUDA, Data parallelism, Memory model, nVidia, nVidia GeForce GTX 285, Review

June 17, 2011 by hgpu

Recent trends in software and hardware for GPGPU computing: A comprehensive survey

B. Neelima, P.S. Raghavendra

Tags: Computer science, Programming techniques, Review

May 31, 2011 by hgpu

A Micro-benchmark Suite for AMD GPUs

Ryan Taylor, Xiaoming Li

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Tags: ATI, ATI IL, ATI Stream, Benchmarking, Computer science, Presentation, Review, RV770, RV870

May 30, 2011 by hgpu

A Summary of Recent GPU Developments and Key Enabling Technologies for Digital Media Applications

I.M. Williams

Tags: Computer science, Performance, Review

May 29, 2011 by hgpu

The GPU enters computing’s mainstream

Michael Macedonia

Tags: ATI, Computer science, nVidia, Review

May 16, 2011 by hgpu

K3 Moore’s Law in the Era of GPU Computing

Chien-Ping Lu

Tags: Computer science, Hardware Architecture, nVidia, Review

May 15, 2011 by hgpu

Fermi GF100 GPU Architecture

Craig M. Wittenbrink, Emmett Kilgariff, Arjun Prabhu

Tags: Computer science, CUDA, Hardware Architecture, nVidia, Review

May 15, 2011 by hgpu

Why does PHM matter? – Nvidia’s GPU problems reviewed

P.K. Rundle

Tags: Medicine, nVidia, Review

May 13, 2011 by hgpu

Where is the data? Why you cannot debate CPU vs. GPU performance without the answer

Chris Gregg, Kim Hazelwood

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Tags: Computer science, nVidia, nVidia GeForce 9800 GT, nVidia GeForce GT 330 M, nVidia GeForce GTX 480, Performance, Review, Tesla C2050

May 11, 2011 by hgpu

Graphics hardware & GPU computing: past, present, and future

David Luebke

Tags: Computer science, CUDA, Games, nVidia, nVidia GeForce GTX 280, Review

May 4, 2011 by hgpu

Tutorial 3: Methodologies and Performance Impacts of General Purpose Computing on GPUs

Shinichi Yamagiwa

Tags: Computer science, Review, Tutorial

April 20, 2011 by hgpu

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

Scientific and Engineering Computing Using ATI Stream Technology

Exploiting Memory Access Patterns to Improve Memory Performance in Data-Parallel Architectures

Recent trends in software and hardware for GPGPU computing: A comprehensive survey

A Micro-benchmark Suite for AMD GPUs

A Summary of Recent GPU Developments and Key Enabling Technologies for Digital Media Applications

The GPU enters computing’s mainstream

K3 Moore’s Law in the Era of GPU Computing

Fermi GF100 GPU Architecture

Why does PHM matter? – Nvidia’s GPU problems reviewed

Where is the data? Why you cannot debate CPU vs. GPU performance without the answer

Graphics hardware & GPU computing: past, present, and future

Tutorial 3: Methodologies and Performance Impacts of General Purpose Computing 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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