high performance computing on graphics processing units: hgpu.org

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gpuSPHASE – A shared memory caching implementation for 2D SPH using CUDA

Daniel Winkler, Michael Meister, Massoud Rezavand, Wolfgang Rauch

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Tags: Algorithms, CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan X, Physics

December 20, 2016 by hgpu

Autotuning CUDA Compiler Parameters for Heterogeneous Applications using the OpenTuner Framework

Pedro Bruel, Marcos Amaris, Alfredo Goldman

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX 980, Package, Performance, Tesla K40

November 16, 2016 by hgpu

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 750, Package, Python

September 27, 2016 by hgpu

Vulnerable GPU Memory Management: Towards Recovering Raw Data from GPU

Zhe Zhou, Wenrui Diao, Xiangyu Liu, Zhou Li, Kehuan Zhang, Rui Liu

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Tags: AMD Radeon R7 250, ATI, Cloud, Computer science, nVidia, nVidia GeForce GTX 750, OpenCL, Operating systems, Security

May 26, 2016 by hgpu

Architecture-Adaptive Code Variant Tuning

Saurav Muralidharan, Amit Roy, Mary Hall, Michael Garland, Piyush Rai

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Tags: Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 750, nVidia GeForce GTX 770, nVidia GeForce GTX 980, Package, Performance, Tesla C2075, Tesla K20

May 21, 2016 by hgpu

BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Matthieu Courbariaux, Yoshua Bengio

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Tags: Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX 750, Package, Python

February 10, 2016 by hgpu

High Performance Histograms on SIMT and SIMD Architectures

M.E.R. Berger

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Tags: AMD Radeon R9 290X, Computer science, CUDA, nVidia, nVidia GeForce GTX 570, nVidia GeForce GTX 660, nVidia GeForce GTX 750, OpenCL, Performance, Thesis

December 10, 2015 by hgpu

Orchestrating Multiple Data-Parallel Kernels on Multiple Devices

Janghaeng Lee, Mehrzad Samadi, Scott Mahlke

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX 760, OpenCL

November 29, 2015 by hgpu

Fast OBJ file importing and parsing in CUDA

Aidan L. Possemiers, Ickjai Lee

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX 970, OpenGL

November 10, 2015 by hgpu

GPU concurrency: Weak behaviours and programming assumptions

Jade Alglave, Mark Batty, Alastair F. Donaldson, Ganesh Gopalakrishnan, Jeroen Ketema, Daniel Poetzl, Tyler Sorensen, John Wickerson

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Tags: ATI, ATI Radeon HD 6570, ATI Radeon HD 7970, Computer science, Memory, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 540 M, nVidia GeForce GTX 660, nVidia GeForce GTX 750, nVidia GeForce GTX Titan, OpenCL, Performance, PTX, Tesla C2075

January 21, 2015 by hgpu

Testing and Exposing Weak Graphics Processing Unit Memory Models

Tyler Rey Sorensen

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Tags: Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX Titan, PTX, Tesla C2075, Thesis

October 27, 2014 by hgpu

OCLADock - OpenCL Accelerated Molecular Docking

A Case Study in Using OpenCL on FPGAs: Creating an Open-Source Accelerator of the AutoDock Molecular Docking Software

sentiment-discovery: Unsupervised Language Modeling at scale for robust sentiment classification

Large Scale Language Modeling: Converging on 40GB of Text in Four Hours

Parallax: A Tool for Automatic Parallelization of Deep Learning Training in Distributed Multi-GPU Environments

Parallax: Automatic Data-Parallel Training of Deep Neural Networks

OpenCLIPER: an OpenCL-based C++ Framework for Overhead-Reduced Medical Image processing and Reconstruction on heterogeneous Devices

OpenCLIPER: an OpenCL-based C++ Framework for Overhead-Reduced Medical Image Processing and Reconstruction on Heterogeneous Devices

Spatial: Specify Parameterized Accelerators Through Inordinately Abstract Language

Spatial: A Language and Compiler for Application Accelerators

cuPentBatch: A batched pentadiagonal solver for NVIDIA GPUs

cuPentBatch – A batched pentadiagonal solver for NVIDIA GPUs

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

CloudCL: Single-Paradigm Distributed Heterogeneous Computing for Cloud Infrastructures

Calamari: OCR Engine based on OCRopy and Kraken

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

FluidFFT: efficient and easy Fast Fourier Transform for Python

FluidFFT: common API (C++ and Python) for Fast Fourier Transform HPC libraries

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2018

August

20-21

Guangzhou, China

The 3rd International Conference on Computer Systems and Communication Technology (ICCSCT), 2018

2018

November

02-04

Academic Exchange Center in Ocean University of China, Qingdao, China

The 4th International Conference on Communication and Information Processing (ICCIP), 2018

2018

November

27-30

Hida Takayama, Japan

The Third International Workshop on GPU Computing and AI (GCA), 2018

2018

September

19-21

Nagoya University, Japan

The 5th International Conference on Power and Energy Systems Engineering (CPESE), 2018

2018

September

22-24

MediaCityUK, Salford Quays, Greater Manchester, England

The 10th International Conference on Information Management and Engineering (ICIME), 2018

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high performance computing on graphics processing units: hgpu.org

gpuSPHASE – A shared memory caching implementation for 2D SPH using CUDA

Autotuning CUDA Compiler Parameters for Heterogeneous Applications using the OpenTuner Framework

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

Architecture-Adaptive Code Variant Tuning

BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

High Performance Histograms on SIMT and SIMD Architectures

Orchestrating Multiple Data-Parallel Kernels on Multiple Devices

Fast OBJ file importing and parsing in CUDA

GPU concurrency: Weak behaviours and programming assumptions

Testing and Exposing Weak Graphics Processing Unit Memory Models

Recent source codes

OCLADock - OpenCL Accelerated Molecular Docking

sentiment-discovery: Unsupervised Language Modeling at scale for robust sentiment classification

Parallax: A Tool for Automatic Parallelization of Deep Learning Training in Distributed Multi-GPU Environments

OpenCLIPER: an OpenCL-based C++ Framework for Overhead-Reduced Medical Image processing and Reconstruction on heterogeneous Devices

Spatial: Specify Parameterized Accelerators Through Inordinately Abstract Language

cuPentBatch: A batched pentadiagonal solver for NVIDIA GPUs

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

CloudCL: Single-Paradigm Distributed Heterogeneous Computing for Cloud Infrastructures

Calamari: OCR Engine based on OCRopy and Kraken

FluidFFT: efficient and easy Fast Fourier Transform for Python

Most viewed papers (last 30 days)

Featured events

The 3rd International Conference on Computer Systems and Communication Technology (ICCSCT), 2018

The 4th International Conference on Communication and Information Processing (ICCIP), 2018

The Third International Workshop on GPU Computing and AI (GCA), 2018

The 5th International Conference on Power and Energy Systems Engineering (CPESE), 2018

The 10th International Conference on Information Management and Engineering (ICIME), 2018