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PyTorch: An Imperative Style, High-Performance Deep Learning Library

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, Soumith Chintala

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia Quadro GP100, Package, Python, PyTorch

December 8, 2019 by hgpu

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

PyTorchPipe: a framework for rapid prototyping of pipelines combining language and vision

Tomasz Kornuta

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Tags: Computer science, Computer vision, Machine learning, Neural networks, Package, Python

October 27, 2019 by hgpu

Model-Based Warp-Level Tiling for Image Processing Programs on GPUs

Abhinav Jangda, Arjun Guha

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Tags: CUDA, Image procesing, nVidia, nVidia GeForce GTX 1080 Ti, Python, Tesla V100

September 22, 2019 by hgpu

ALPyNA: Acceleration of Loops in Python for Novel Architectures

Dejice Jacob, Jeremy Singer

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 1060, Python

September 22, 2019 by hgpu

Parallel scalable simulations of biological neural networks using TensorFlow: A beginner’s guide

Saptarshi Soham Mohanta, Collins Assisi

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Tags: Biology, Computer science, CUDA, Deep learning, Differential equations, Machine learning, Neural networks, nVidia, ODEs, Ordinary differential equations, Package, Partial differential equations, PDEs, Python, TensorFlow, Tutorial

June 12, 2019 by hgpu

TensorFlow Doing HPC

Steven W. D. Chien, Stefano Markidis, Vyacheslav Olshevsky, Yaroslav Bulatov, Erwin Laure, Jeffrey S. Vetter

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Tags: Benchmarking, Computer science, CUDA, Deep learning, FFT, Heterogeneous systems, HPC, Machine learning, nVidia, nVidia Quadro K420, OpenMPI, Package, Performance, Python, TensorFlow, Tesla K80, Tesla V100

March 17, 2019 by hgpu

A Package for Multi-Dimensional Monte Carlo Integration on Multi-GPUs

Hong-zhong Wu, Junjie Zhang, Long-gang Pang, Qun Wang

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Tags: Computational Physics, Monte Carlo integration, nVidia, Package, Physics, Python, TensorFlow, Tesla K40

February 24, 2019 by hgpu

ThunderGBM: Fast GBDTs and Random Forests on GPUs

Zeyi Wen, Jiashuai Shi, Bingsheng He, Qinbin Li, Jian Chen

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Tags: Computer science, CUDA, Data mining, Machine learning, nVidia, Package, Python, Tesla P100

February 3, 2019 by hgpu

HeteroCL: A Multi-Paradigm Programming Infrastructure for Software-Defined Reconfigurable Computing

Yi-Hsiang Lai, Yuze Chi, Yuwei Hu, Jie Wang, Cody Hao Yu, Yuan Zhou, Jason Cong, Zhiru Zhang

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Tags: Algorithms, Compilers, Computer science, FPGA, Heterogeneous systems, HLS, OpenCL, Python

January 6, 2019 by hgpu

LeFlow: Enabling Flexible FPGA High-Level Synthesis of Tensorflow Deep Neural Networks

Daniel H. Noronha, Bahar Salehpour, Steven J.E. Wilton

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Tags: Computer science, Deep learning, FPGA, LSTM, Machine learning, Neural networks, Package, Python, TensorFlow

July 21, 2018 by hgpu

Calamari – A High-Performance Tensorflow-based Deep Learning Package for Optical Character Recognition

Christoph Wick, Christian Reul, Frank Puppe

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Tags: Computer science, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, Package, Python, TensorFlow

July 7, 2018 by hgpu

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Masivo: Parallel public transport routes simulation

Masivo: Parallel Simulation Model Based on OpenCL for Massive Public Transportation Systems’ Routes

FBLAS: BLAS implementation for Intel FPGA

FBLAS: Streaming Linear Algebra Kernels on FPGA

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

Titan: A CUDA-based physics simulation sandbox for high-performance robotics, physics, and reinforcement learning applications

Titan: A Parallel Asynchronous Library for Multi-Agent and Soft-Body Robotics using NVIDIA CUDA

onYourMarks: A deep learning benchmark implementation for Nvidia's Jetson TX2

Benchmarking Deep Learning Models on Jetson TX2

Hacking Neural Networks: A small course on exploiting and defending neural networks

Hacking Neural Networks: A Short Introduction

A Computing Kernel for Network Binarization on PyTorch

Deep Learning Based FPGA-CPU Acceleration

KTT: Kernel Tuning Toolkit

Framework for Parallel Kernels Auto-tuning

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Munich, Germany

8th International Workshop on OpenCL, including SYCLCon, 2019

2019

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13-15

Boston, USA

7th International Workshop on OpenCL, 2019

2019

May

Boston, MA, USA

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

2019

May

Larnaca, Cyprus

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Neural Networks (ICFNN), 2019

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high performance computing on graphics processing units: hgpu.org

PyTorch: An Imperative Style, High-Performance Deep Learning Library

GPU Computing with Python: Performance, Energy Efficiency and Usability

PyTorchPipe: a framework for rapid prototyping of pipelines combining language and vision

Model-Based Warp-Level Tiling for Image Processing Programs on GPUs

ALPyNA: Acceleration of Loops in Python for Novel Architectures

Parallel scalable simulations of biological neural networks using TensorFlow: A beginner’s guide

TensorFlow Doing HPC

A Package for Multi-Dimensional Monte Carlo Integration on Multi-GPUs

ThunderGBM: Fast GBDTs and Random Forests on GPUs

HeteroCL: A Multi-Paradigm Programming Infrastructure for Software-Defined Reconfigurable Computing

LeFlow: Enabling Flexible FPGA High-Level Synthesis of Tensorflow Deep Neural Networks

Calamari – A High-Performance Tensorflow-based Deep Learning Package for Optical Character Recognition

Recent source codes

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Masivo: Parallel public transport routes simulation

FBLAS: BLAS implementation for Intel FPGA

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

Titan: A CUDA-based physics simulation sandbox for high-performance robotics, physics, and reinforcement learning applications

onYourMarks: A deep learning benchmark implementation for Nvidia's Jetson TX2

Hacking Neural Networks: A small course on exploiting and defending neural networks

A Computing Kernel for Network Binarization on PyTorch

Deep Learning Based FPGA-CPU Acceleration

KTT: Kernel Tuning Toolkit

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Featured events

8th International Workshop on OpenCL, including SYCLCon, 2019

7th International Workshop on OpenCL, 2019

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

International Conference on Frontiers of Neural Networks (ICFNN), 2019