high performance computing on graphics processing units: hgpu.org

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Scalable parallel programming with CUDA

John Nickolls, Ian Buck, Michael Garland, Kevin Skadron

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Tags: Computer science, CUDA, nVidia, Programming techniques, Review

November 17, 2010 by hgpu

Developing and Deploying Advanced Algorithms to Novel Supercomputing Hardware

Robert J. Brunner, Volodymyr V. Kindratenko, Adam D. Myers

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Tags: Algorithms, Astrophysics, Computer science, FPGA, Review

November 13, 2010 by hgpu

Recent algorithm and machine developments for lattice QCD

Ken-Ichi Ishikawa

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Tags: High Energy Physics - Lattice, Monte Carlo simulation, Physics, QCD, Review

November 13, 2010 by hgpu

Implementation of float-float operators on graphics hardware

Guillaume Da Gracca, David Defour

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Tags: Cg, Computer science, nVidia, nVidia GeForce 7800 GTX, Review

November 13, 2010 by hgpu

The Graphics Card as a Streaming Computer

Suresh Venkatasubramanian

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Tags: Computer science, Review

November 13, 2010 by hgpu

Cg in Two Pages

Mark J. Kilgard

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Tags: Cg, Computer science, High-level Languages, HLSL, nVidia, Review

November 13, 2010 by hgpu

QCD on GPUs: cost effective supercomputing

M. A. Clark

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Tags: High Energy Physics - Lattice, Monte Carlo simulation, nVidia, Physics, QCD, Review

November 13, 2010 by hgpu

A Survey of General-Purpose Computation on Graphics Hardware

John D. Owens, David Luebke, Naga Govindaraju, Mark Harris, Jens Kruger, Aaron E. Lefohn, and Timothy J. Purcell

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Tags: Algorithms, Computer science, Review

November 5, 2010 by hgpu

NVIDIA Tesla: A Unified Graphics and Computing Architecture

Erik Lindholm, J. Nickolls, S. Oberman, J. Montrym

Tags: Computer science, Hardware, nVidia, Review, Tesla

November 5, 2010 by hgpu

Issues and challenges in compiling for graphics processors

Norm Rubin

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Tags: Computer science, High-level Languages, Presentation, Review

November 4, 2010 by hgpu

Approximate Dynamic Programming and Neural Networks on Game Hardware

R. J. Meuth, D. C. Wunsch

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Tags: Computer science, Neural networks, Review

November 4, 2010 by hgpu

Optimal loop unrolling for GPGPU programs (thesis)

Giridhar S. Murthy

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, OpenCL, Review, Thesis

November 3, 2010 by hgpu

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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high performance computing on graphics processing units: hgpu.org

Scalable parallel programming with CUDA

Developing and Deploying Advanced Algorithms to Novel Supercomputing Hardware

Recent algorithm and machine developments for lattice QCD

Implementation of float-float operators on graphics hardware

The Graphics Card as a Streaming Computer

Cg in Two Pages

QCD on GPUs: cost effective supercomputing

A Survey of General-Purpose Computation on Graphics Hardware

NVIDIA Tesla: A Unified Graphics and Computing Architecture

Issues and challenges in compiling for graphics processors

Approximate Dynamic Programming and Neural Networks on Game Hardware

Optimal loop unrolling for GPGPU programs (thesis)

Recent source codes

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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