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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

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Beehive SPIR-V Toolkit

Experiences in Building a Composable and Functional API for Runtime SPIR-V Code Generation

TorchBench: a collection of open source benchmarks used to evaluate PyTorch performance

TorchBench: Benchmarking PyTorch with High API Surface Coverage

Prediction of Performance and Power Consumption of GPGPU Applications

Descend: an attempt at safe imperative GPU programming

Descend: A Safe GPU Systems Programming Language

Redwood heterogeneous framework

Redwood: Flexible and Portable Heterogeneous Tree Traversal Workloads

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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

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

AutoDock for GPUs and other accelerators

spotPython: Sequential Parameter Optimization in Python

Test benchmarks for CELERITY

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Beehive SPIR-V Toolkit

TorchBench: a collection of open source benchmarks used to evaluate PyTorch performance

Prediction of Performance and Power Consumption of GPGPU Applications

Descend: an attempt at safe imperative GPU programming

Redwood heterogeneous framework

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