high performance computing on graphics processing units: hgpu.org

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Arbitrarily large iterative tomographic reconstruction on multiple GPUs using the TIGRE toolbox

Ander Biguri, Reuben Lindroos, Robert Bryll, Hossein Towsyfyan, Hans Deyhle, Richard Boardman, Mark Mavrogordato, Manjit Dosanjh, Steven Hancock, Thomas Blumensath

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Tags: Algorithms, Computed tomography, Computer science, CUDA, nVidia, nVidia GeForce GTX 1080 Ti, Package

May 12, 2019 by hgpu

Compressed Learning of Deep Neural Networks for OpenCL-Capable Embedded Systems

Sangkyun Lee, Jeonghyun Lee

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Tags: Algorithms, ARM, Compression, Computer science, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Package

May 5, 2019 by hgpu

Wasserstein-Fisher-Rao Document Distance

Zihao Wang, Datong Zhou, Yong Zhang, Hao Wu, Chenglong Bao

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Tags: Computer science, Machine learning, Nearest neighbour, nVidia, nVidia GeForce GTX 1080 Ti

April 28, 2019 by hgpu

ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of Multipliers

Ao Ren, Tianyun Zhang, Shaokai Ye, Jiayu Li, Wenyao Xu, Xuehai Qian, Xue Lin, Yanzhi Wang

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Tags: Caffe, Computer science, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, Package, Tesla P100

January 6, 2019 by hgpu

A Study on the Acceleration of Arrival Curve Construction and Regular Specification Mining using GPUs

Nirmal Joshi Benann Rajendra

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Tags: AMD Radeon R9 390X, ATI, Computer science, CUDA, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce RTX 2080 Ti, OpenCL, Thesis

December 30, 2018 by hgpu

Performance Analysis of a Stereo Matching Implementation in OpenCL

Stephan Rotheneder

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Tags: Computer science, Computer vision, nVidia, nVidia GeForce GTX 1060, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX 750 Ti, nVidia Quadro 1000 M, OpenCL, Stereo vision

December 16, 2018 by hgpu

A Framework for Fast and Efficient Neural Network Compression

Hyeji Kim, Muhammad Umar Karim, Chong-Min Kyung

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Tags: ARM, Caffe, Compression, Computational Complexity, Computer science, Computer vision, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti

December 9, 2018 by hgpu

Accelerating Low-End Edge Computing with Cross-Kernel Functionality Abstraction

Chao Wu, Yaoxue Zhang, Yuezhi Zhou, Qiushi Li

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Performance

November 18, 2018 by hgpu

A Comparative Measurement Study of Deep Learning as a Service Framework

Yanzhao Wu, Ling Liu, Calton Pu, Wenqi Cao, Semih Sahin, Wenqi Wei, Qi Zhang

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Tags: Caffe, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080 Ti, Tesla M60, Theano

November 3, 2018 by hgpu

OpenCL Performance Prediction using Architecture-Independent Features

Beau Johnston, Greg Falzon, Josh Milthorpe

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Tags: AMD FirePro S9150, AMD Radeon R9 290X, AMD Radeon R9 295X2, AMD Radeon RX 480, ATI, ATI Radeon HD 7970, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 1080 Ti, OpenCL, Package, performance portability, Tesla K20, Tesla K40

November 3, 2018 by hgpu

Benchmarking and Optimization of Gradient Boosted Decision Tree Algorithms

Andreea Anghel, Nikolaos Papandreou, Thomas Parnell, Alessandro De Palma, Haralampos Pozidis

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Tags: Algorithms, Bayesian, Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 1080 Ti

September 16, 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

Data-Parallel Flattening by Expansion

IA-SpGEMM: An Input-aware Auto-tuning Framework for Parallel Sparse Matrix-Matrix Multiplication

MGPUSim: Enabling Multi-GPU Performance Modeling and Optimization

TensorNetwork: A library for easy and efficient manipulation of tensor networks

TensorNetwork for Machine Learning

triSYCL: Generic system-wide modern C++ for heterogeneous platforms with SYCL from Khronos Group

SYCL Code Generation for Multigrid Methods

Dynamic Distribution Pruning for Efficient Network Architecture Search

Tinker-HP: High Performance Massively Parallel Evolution of Tinker

Raising the Performance of the Tinker-HP Molecular Modeling Package on Intel’s HPC Architectures: a Living Review [Article v1.0]

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2019

May

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

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2019

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high performance computing on graphics processing units: hgpu.org

Arbitrarily large iterative tomographic reconstruction on multiple GPUs using the TIGRE toolbox

Compressed Learning of Deep Neural Networks for OpenCL-Capable Embedded Systems

Wasserstein-Fisher-Rao Document Distance

ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of Multipliers

A Study on the Acceleration of Arrival Curve Construction and Regular Specification Mining using GPUs

Performance Analysis of a Stereo Matching Implementation in OpenCL

A Framework for Fast and Efficient Neural Network Compression

Accelerating Low-End Edge Computing with Cross-Kernel Functionality Abstraction

A Comparative Measurement Study of Deep Learning as a Service Framework

OpenCL Performance Prediction using Architecture-Independent Features

Benchmarking and Optimization of Gradient Boosted Decision Tree Algorithms

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

Recent source codes

Data-Parallel Flattening by Expansion

IA-SpGEMM: An Input-aware Auto-tuning Framework for Parallel Sparse Matrix-Matrix Multiplication

MGPUSim: Enabling Multi-GPU Performance Modeling and Optimization

TensorNetwork: A library for easy and efficient manipulation of tensor networks

triSYCL: Generic system-wide modern C++ for heterogeneous platforms with SYCL from Khronos Group

PSST: A Tutorial for Parallelised Scalable Simulations in TensorFlow

MUSE: Miami University Simulation Environment

Pressure Point Analysis framework

Dynamic Distribution Pruning for Efficient Network Architecture Search

Tinker-HP: High Performance Massively Parallel Evolution of Tinker

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Featured events

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

International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2019