high performance computing on graphics processing units: hgpu.org

hgpu.org » Benchmarking

Applying graphics processor acceleration in a software defined radio prototyping environment

William Plishker, George F. Zaki, Shuvra S. Bhattacharyya, Charles Clancy, John Kuykendall

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Tags: Benchmarking, CUDA, nVidia, nVidia GeForce GTX 260, Signal processing

July 24, 2011 by hgpu

A characterization of the Rodinia benchmark suite with comparison to contemporary CMP workloads

Shuai Che, Jeremy W. Sheaffer, Michael Boyer, Lukasz G. Szafaryn, Liang Wang, Kevin Skadron

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Tags: Benchmarking, CMP, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480

July 22, 2011 by hgpu

Linpack evaluation on a supercomputer with heterogeneous accelerators

Toshio Endo, Akira Nukada, Satoshi Matsuoka, Naoya Maruyama

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Tags: Benchmarking, Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, nVidia, Performance, Tesla S1070

July 17, 2011 by hgpu

A comparative benchmarking of the FFT on Fermi and Evergreen GPUs

M.F. Ahmed, O. Haridy

Tags: ATI, ATI Radeon HD 5870, Benchmarking, Computer science, FFT, Hardware Architecture, nVidia, nVidia GeForce GTX 480, Performance

June 30, 2011 by hgpu

Hybrid Embarrassingly Parallel on heterogeneous platform

Chunye Gong, Jie Liu, Jin Qin, Qingfeng Hu, Zhenghu Gong

Tags: Benchmarking, Computer science, Heterogeneous systems, Pseudo-random number generators

June 8, 2011 by hgpu

Efficient Embarrassingly Parallel on Graphics Processor Unit

Chunye Gong, Jie Liu, Jin Qin, Qingfeng Hu, Zhenghu Gong

Tags: Benchmarking, Computer science, CUDA, nVidia, Performance, Pseudo-random number generators, Tesla T10

June 5, 2011 by hgpu

Swarm’s flight: Accelerating the particles using C-CUDA

Lucas de P. Veronese, Renato A. Krohling

Tags: Algorithms, Benchmarking, Computer science, CUDA, nVidia, Optimization

June 2, 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

Evaluating the potential of graphics processors for high performance embedded computing

Shuai Mu, Chenxi Wang, Ming Liu, Dongdong Li, Maohua Zhu, Xiaoliang Chen, Xiang Xie, Yangdong Deng

Tags: ASIC, Benchmarking, Computer science, DSP, Embedded high-performance computing

May 30, 2011 by hgpu

Performance Analysis of a Hybrid MPI/CUDA Implementation of the NAS-LU Benchmark

S.J. Pennycook, S.D. Hammond, S.A. Jarvis, G.R. Mudalige

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Tags: Benchmarking, Computer science, CUDA, MPI, nVidia, Performance, Tesla C1060, Tesla C2050

May 25, 2011 by hgpu

Program Optimization of Array-Intensive SPEC2k Benchmarks on Multithreaded GPU Using CUDA and Brook+

Guibin Wang, Tao Tang, Xudong Fang, Xiaoguang Ren

Tags: Benchmarking, Brook, Computer science, CUDA, nVidia, Performance

April 8, 2011 by hgpu

Exploring GPGPU workloads: Characterization methodology, analysis and microarchitecture evaluation implications

Nilanjan Goswami, Ramkumar Shankar, Madhura Joshi, Tao Li

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Tags: Benchmarking, Computer science, CUDA, nVidia, Performance, PTX

April 4, 2011 by hgpu

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

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

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

Fortran xDSL dialects

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

See all packages

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high performance computing on graphics processing units: hgpu.org

Applying graphics processor acceleration in a software defined radio prototyping environment

A characterization of the Rodinia benchmark suite with comparison to contemporary CMP workloads

Linpack evaluation on a supercomputer with heterogeneous accelerators

A comparative benchmarking of the FFT on Fermi and Evergreen GPUs

Hybrid Embarrassingly Parallel on heterogeneous platform

Efficient Embarrassingly Parallel on Graphics Processor Unit

Swarm’s flight: Accelerating the particles using C-CUDA

A Micro-benchmark Suite for AMD GPUs

Evaluating the potential of graphics processors for high performance embedded computing

Performance Analysis of a Hybrid MPI/CUDA Implementation of the NAS-LU Benchmark

Program Optimization of Array-Intensive SPEC2k Benchmarks on Multithreaded GPU Using CUDA and Brook+

Exploring GPGPU workloads: Characterization methodology, analysis and microarchitecture evaluation implications

Recent source codes

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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

hls4ml: Machine learning on FPGAs using HLS

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

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

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

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