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

Bridge++: a code set for numerical simulations of lattice gauge theory including QCD

Wilson and Domainwall Kernels on Oakforest-PACS

Synkhronos: a Multi-GPU Theano Extension for Data Parallelism

SoAx: A generic C++ Structure of Arrays for handling Particles in HPC Codes

GPGPU-Treewidth: A GPU implementation for computing the treewidth of a graph

Computing Treewidth on the GPU

containers-benchs: Just a bunch of method and scripts to test performances with containers

Performance Evaluation of Container-based Virtualization for High Performance Computing Environments

OpenCL Actors - Adding Data Parallelism to Actor-based Programming with CAF

OpenCL Actors – Adding Data Parallelism to Actor-based Programming with CAF

GALARIO: a GPU Accelerated Library for Analysing Radio Interferometer Observations

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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

Bridge++: a code set for numerical simulations of lattice gauge theory including QCD

Synkhronos: a Multi-GPU Theano Extension for Data Parallelism

SoAx: A generic C++ Structure of Arrays for handling Particles in HPC Codes

GPGPU-Treewidth: A GPU implementation for computing the treewidth of a graph

containers-benchs: Just a bunch of method and scripts to test performances with containers

OpenCL Actors - Adding Data Parallelism to Actor-based Programming with CAF

GALARIO: a GPU Accelerated Library for Analysing Radio Interferometer Observations

CCSQCDSolverBench: CCS QCD Solver benchmark program (MIC(KNC) version)

TuneBench: Simple tunable OpenCL kernels for many-core accelerators

ZZGemmOoc: GPU GEMM out-of-core solvers

Most viewed papers (last 30 days)

Featured events

6th International Workshop on OpenCL (IWOCL), 2018

The 2nd International Conference on Machine Learning and Soft Computing (ICMLSC), 2018

10th International Conference on Computer and Automation Engineering (ICCAE), 2018

The 2nd International Conference on Mechanical, Aeronautical and Automotive Engineering (ICMAA), 2018

The 2nd International Conference on Cryptography, Security and Privacy (ICCSP), 2018