high performance computing on graphics processing units: hgpu.org

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Fast convolution kernels on pascal GPU with high memory efficiency

Qiong Chang, Masaki Onishi, Tsutomu Maruyama

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Tags: CNN, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080 Ti, Performance

December 4, 2022 by hgpu

Three Contributions to the Theory and Practice of Optimizing Compilers

Linxiao Wang

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Tags: Compilers, Computer science, CUDA, LLVM, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX 760 M, Package, Thesis

December 4, 2022 by hgpu

torchode: A Parallel ODE Solver for PyTorch

Marten Lienen, Stephan Günnemann

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Tags: Computer science, Deep learning, Numerical Analysis, nVidia, nVidia GeForce GTX 1080 Ti, ODEs, Ordinary differential equations, Package, PyTorch

October 30, 2022 by hgpu

Benchmarking optimization algorithms for auto-tuning GPU kernels

Richard Schoonhoven, Ben van Werkhoven, Kees Joost Batenburg

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Tags: AMD Radeon Instinct Mi50, ATI, Auto-Tuning, Benchmarking, Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX Titan X, nVidia Titan RTX, OpenCL, Performance, pyCUDA, PyOpenCL, Tesla K20, Tesla P100, Tesla V100

October 9, 2022 by hgpu

Understanding the Power of Evolutionary Computation for GPU Code Optimization

Jhe-Yu Liou, Muaaz Awan, Steven Hofmeyr, Stephanie Forrest, Carole-Jean Wu

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Tags: Bioinformatics, Biology, Computer science, CUDA, Evolutionary Computations, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX 1080 Ti, Package, performance portability, Sequence alignment, Tesla P100, Tesla V100

September 4, 2022 by hgpu

DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardware

Hanieh Hashemi, Yongqin Wang, Murali Annavaram

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Tags: Algorithms, Computer science, Deep learning, Linear Algebra, Machine learning, nVidia, nVidia GeForce GTX 1080 Ti, Security

July 10, 2022 by hgpu

Tensor Computation Based on Heterogeneous Memory

Jiawen Liu

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Tags: Computer science, Heterogeneous systems, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Prefetch, Thesis

July 3, 2022 by hgpu

cuPSO: GPU Parallelization for Particle Swarm Optimization Algorithms

Chuan-Chi Wang, Chun-Yen Ho, Chia-Heng Tu, Shih-Hao Hung

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 1080 Ti, Package, Particle swarm optimization

May 8, 2022 by hgpu

The Ecological Footprint of Neural Machine Translation Systems

Dimitar Sherionov, Eva Vanmassenhove

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, LSTM, NLP, nVidia, nVidia GeForce GTX 1080 Ti, Package, Tesla P100

February 13, 2022 by hgpu

Performance prediction of deep learning applications training in GPU as a service systems

Marco Lattuada, Eugenio Gianniti, Danilo Ardagna, Li Zhang

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Tags: Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, Performance, Tesla K80, Tesla M60

January 30, 2022 by hgpu

OpenCL-HPX Integration

Michael Schupikov

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce RTX 3080, OpenCL, Thesis

December 26, 2021 by hgpu

Evaluation of Pseudo-Random Number Generation on GPU Cards

Tair Askar, Bekdaulet Shukirgaliyev, Martin Lukac, Ernazar Abdikamalov

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 1080 Ti, nVidia GeForce RTX 3080, nVidia GeForce RTX 3090, Pseudo-random number generators

December 19, 2021 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

Fast convolution kernels on pascal GPU with high memory efficiency

Three Contributions to the Theory and Practice of Optimizing Compilers

torchode: A Parallel ODE Solver for PyTorch

Benchmarking optimization algorithms for auto-tuning GPU kernels

Understanding the Power of Evolutionary Computation for GPU Code Optimization

DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardware

Tensor Computation Based on Heterogeneous Memory

cuPSO: GPU Parallelization for Particle Swarm Optimization Algorithms

The Ecological Footprint of Neural Machine Translation Systems

Performance prediction of deep learning applications training in GPU as a service systems

OpenCL-HPX Integration

Evaluation of Pseudo-Random Number Generation on GPU Cards

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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