high performance computing on graphics processing units: hgpu.org

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CUDA-Lite: Reducing GPU programming complexity

Sain-zee Ueng, Melvin Lathara, Sara S. Baghsorkhi, Wen-mei W. Hwu

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Programming techniques

November 1, 2010 by hgpu

Program optimization space pruning for a multithreaded gpu

Shane Ryoo, Christopher I. Rodrigues, Sam S. Stone, Sara S. Baghsorkhi, Sain Z. Ueng, John A. Stratton, Wen mei

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Optimization, Performance, Programming techniques

November 1, 2010 by hgpu

How GPUs Work

David Luebke, Greg Humphreys

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Tags: Computer science, Review

October 30, 2010 by hgpu

GPUs: A Closer Look

Kayvon Fatahalian, Mike Houston

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Tags: Computer science, Review

October 30, 2010 by hgpu

Mapping computational concepts to GPUs

Mark Harris

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Tags: Computer science, Review

October 30, 2010 by hgpu

A Memory Model for Scientific Algorithms on Graphics Processors

Naga K. Govindaraju, Scott Larsen, Jim Gray, Dinesh Manocha

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Tags: Algorithms, Computer science, FFT, Memory model, nVidia, nVidia GeForce 6800 Ultra, nVidia GeForce 7800 GTX, nVidia GeForce 7900 GTX, Sorting

October 30, 2010 by hgpu

A hardware redundancy and recovery mechanism for reliable scientific computation on graphics processors

Jeremy W. Sheaffer, David P. Luebke, Kevin Skadron

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Tags: Computer science, Error recovery

October 30, 2010 by hgpu

Mars: a MapReduce framework on graphics processors

Bingsheng He, Wenbin Fang, Qiong Luo, Naga K. Govindaraju, Tuyong Wang

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Source codes

Tags: Computer science, CUDA, Data parallelism, MapReduce, nVidia, nVidia GeForce 8800 GTX, Package, Programming techniques, Web Analysis

October 30, 2010 by hgpu

A performance study of general-purpose applications on graphics processors using CUDA

S. Che, M. Boyer, J. Meng, D. Tarjan, J. Sheaffer, K. Skadron

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 260, OpenMP, Package, Performance

October 30, 2010 by hgpu

Rise of the Graphics Processor

D. Blythe

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Tags: Computer science, Review

October 30, 2010 by hgpu

Solving quadratic assignment problems by genetic algorithms with GPU computation: a case study

Shigeyoshi Tsutsui, Noriyuki Fujimoto

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Programming techniques

October 30, 2010 by hgpu

Parallel Processing of Matrix Multiplication in a CPU and GPU Heterogeneous Environment

Satoshi Ohshima, Kenji Kise, Takahiro Katagiri, Toshitsugu Yuba

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Tags: Algorithms, Computer science, Linear Algebra, nVidia GeForce 6600 GT, nVidia GeForce 7800 GTX

October 29, 2010 by hgpu

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

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

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

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

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

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high performance computing on graphics processing units: hgpu.org

Applications

CUDA-Lite: Reducing GPU programming complexity

Program optimization space pruning for a multithreaded gpu

How GPUs Work

GPUs: A Closer Look

Mapping computational concepts to GPUs

A Memory Model for Scientific Algorithms on Graphics Processors

A hardware redundancy and recovery mechanism for reliable scientific computation on graphics processors

Mars: a MapReduce framework on graphics processors

A performance study of general-purpose applications on graphics processors using CUDA

Rise of the Graphics Processor

Solving quadratic assignment problems by genetic algorithms with GPU computation: a case study

Parallel Processing of Matrix Multiplication in a CPU and GPU Heterogeneous Environment

Recent source codes

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

Device Virtual Machine (DVM)

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

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

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

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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