high performance computing on graphics processing units: hgpu.org

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Regression Modelling of Power Consumption for Heterogeneous Processors

Tahir Diop

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Tags: Benchmarking, Computer science, Energy-efficient computing, Heterogeneous systems, nVidia, OpenCL, Tesla M2090, Thesis

November 27, 2013 by hgpu

Bohrium: Unmodified NumPy Code on CPU, GPU, and Cluster

Mads R. B. Kristensen, Simon A. F. Lund, Troels Blum, Kenneth Skovhede, Brian Vinter

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Tags: ATI, ATI Radeon HD 7850, Benchmarking, Computer science, GPU cluster, nVidia, nVidia GeForce GTX 680, OpenCL, Python

November 22, 2013 by hgpu

Performance and Power Comparisons Between Fermi and Cypress GPUs

Ying Zhang

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Tags: ATI, ATI Radeon HD 5870, Benchmarking, Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 580, OpenCL, Performance, Thesis

November 18, 2013 by hgpu

An Empirical Study of Intel Xeon Phi

Jianbin Fang, Ana Lucia Varbanescu, Henk Sips, Lilun Zhang, Yonggang Che, Chuanfu Xu

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Tags: Benchmarking, Computer science, Intel Phi, Intel Xeon Phi, Performance

October 25, 2013 by hgpu

Understanding and Modeling the Synchronization Cost in the GPU Architecture

James T. Letendre

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Tesla C2075, Thesis

October 17, 2013 by hgpu

Performance Comparison of GPU, DSP and FPGA implementations of image processing and computer vision algorithms in embedded systems

Egil Fykse

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Tags: Benchmarking, Computer science, Computer vision, CUDA, DSP, FPGA, Image processing, nVidia, nVidia GeForce GTX 660 Ti, nVidia Quadro 1000 M, OpenCV, Performance, Thesis

October 15, 2013 by hgpu

Performance Analysis of a Large Memory Application on Multiple Architectures

P.M.Kogge

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Tags: Benchmarking, Heterogeneous systems, nVidia, nVidia Tesla, Performance

October 9, 2013 by hgpu

Compiling a High-level Directive-Based Programming Model for GPGPUs

Xiaonan Tian, Rengan Xu, Yonghong Yan, Zhifeng Yun, Sunita Chandrasekaran, Barbara Chapman

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Tags: Benchmarking, Computer science, nVidia, OpenACC, Tesla K20

September 30, 2013 by hgpu

Data-parallel Acceleration of PARSEC Black-Scholes Benchmark

August Andren, Patrik Hagernas

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Tags: Benchmarking, Computer science, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 680, OpenACC, Tesla C2050, Tesla M2090

September 30, 2013 by hgpu

BenchFriend: Correlating the Performance of GPU Benchmarks

Shuai Che, Kevin Skadron

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Tags: Benchmarking, Computer science, CUDA, GPGPU-sim, nVidia, Tesla C2050, Tesla K20

September 23, 2013 by hgpu

Processing MPI Derived Datatypes on Noncontiguous GPU-Resident Data

John Jenkins, James Dinan, Pavan Balaji, Tom Peterka, Nagiza F. Samatova, Rajeev Thakur

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Tags: Algorithms, Benchmarking, Computer science, CUDA, MPI, nVidia, Tesla C2050

September 23, 2013 by hgpu

Porting to the Intel Xeon Phi: Opportunities and Challenges

C. Rosales

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, Intel Phi, MPI, nVidia, Optimization, Performance

September 15, 2013 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

Regression Modelling of Power Consumption for Heterogeneous Processors

Bohrium: Unmodified NumPy Code on CPU, GPU, and Cluster

Performance and Power Comparisons Between Fermi and Cypress GPUs

An Empirical Study of Intel Xeon Phi

Understanding and Modeling the Synchronization Cost in the GPU Architecture

Performance Comparison of GPU, DSP and FPGA implementations of image processing and computer vision algorithms in embedded systems

Performance Analysis of a Large Memory Application on Multiple Architectures

Compiling a High-level Directive-Based Programming Model for GPGPUs

Data-parallel Acceleration of PARSEC Black-Scholes Benchmark

BenchFriend: Correlating the Performance of GPU Benchmarks

Processing MPI Derived Datatypes on Noncontiguous GPU-Resident Data

Porting to the Intel Xeon Phi: Opportunities and Challenges

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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