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GPU Computing with Python: Performance, Energy Efficiency and Usability

Håvard H. Holm, André R. Brodtkorb, Martin L. Sætra

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 780, nVidia GeForce GTX 840 M, OpenCL, Performance, Python, Tesla K20, Tesla M2090, Tesla P100, Tesla V100

December 8, 2019 by hgpu

Automated Generation of OpenCL Programs Based on Algebra-Algorithmic Approach

Anatoliy Doroshenko, Oleksii Beketov, Mykola Bondarenko, Olena Yatsenko

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 840 M, OpenCL

June 30, 2019 by hgpu

Parallel LZ77 Decoding using a GPU

Emmanuel Morfiadakis

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia GeForce GTX 840 M, Thesis

September 23, 2018 by hgpu

The performances of R GPU implementations of the GMRES method

Bogdan Oancea, Richard Pospisil

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 840 M, OpenCL, Performance, R

February 17, 2018 by hgpu

Distributed learning of CNNs on heterogeneous CPU/GPU architectures

Jose Marques, Gabriel Falcao, Luis A. Alexandre

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Tags: ATI, ATI Radeon HD 7500 M, CNN, Computer science, CUDA, Deep learning, Heterogeneous systems, Matlab, Neural networks, nVidia, nVidia GeForce GTX 840 M, nVidia GeForce GTX 940 M, nVidia GeForce GTX 950 M, OpenCL, Tesla K20

December 10, 2017 by hgpu

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KLARAPTOR: A Tool for Dynamically Finding Optimal Kernel Launch Parameters Targeting CUDA Programs

Three Contributions to the Theory and Practice of Optimizing Compilers

HETU: a high-performance distributed deep learning system targeting large-scale and automated distributed training

Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism

TorchOpt: an efficient library for differentiable optimization built upon PyTorch

TorchOpt: An Efficient Library for Differentiable Optimization

NaturalCC: An Open-Source Toolkit for Code Intelligence

A Study on Neural-based Code Summarization in Low-resource Settings

mt4g: Memory Topology for GPUs

Capturing the Memory Topology of GPUs

See all packages

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high performance computing on graphics processing units: hgpu.org

GPU Computing with Python: Performance, Energy Efficiency and Usability

Automated Generation of OpenCL Programs Based on Algebra-Algorithmic Approach

Parallel LZ77 Decoding using a GPU

The performances of R GPU implementations of the GMRES method

Distributed learning of CNNs on heterogeneous CPU/GPU architectures

Recent source codes

KLARAPTOR: A Tool for Dynamically Finding Optimal Kernel Launch Parameters Targeting CUDA Programs

HETU: a high-performance distributed deep learning system targeting large-scale and automated distributed training

SYCL-DNN: a library implementing neural network algorithms written using SYCL

SW#SYCL

Flink-TornadoVM-Artifact

StateMover: a checkpoint-based debugging framework for FPGAs

Kernel Tuner

TorchOpt: an efficient library for differentiable optimization built upon PyTorch

NaturalCC: An Open-Source Toolkit for Code Intelligence

mt4g: Memory Topology for GPUs

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