high performance computing on graphics processing units: hgpu.org

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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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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

Performance study of interference on GPU and CPU resources with multiple applications

Shinichi Yamagiwa, Koichi Wada

Tags: Algorithm optimization, Computer science, Performance

October 29, 2010 by hgpu

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

See all packages

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high performance computing on graphics processing units: hgpu.org

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

Performance study of interference on GPU and CPU resources with multiple applications

Recent source codes

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

ThunderKittens: Tile primitives for speedy kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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