high performance computing on graphics processing units: hgpu.org

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

AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles

Charles Weill, Javier Gonzalvo, Vitaly Kuznetsov, Scott Yang, Scott Yak, Hanna Mazzawi, Eugen Hotaj, Ghassen Jerfel, Vladimir Macko, Ben Adlam, Mehryar Mohri, Corinna Cortes

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Tags: Algorithms, Computer science, Machine learning, Neural networks, nVidia, Package, TensorFlow, Tesla V100

May 5, 2019 by hgpu

GPU-based Efficient Join Algorithms on Hadoop

Hongzhi Wang, Ning Li, Zheng Wang, Jianing Li

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Tags: Algorithms, Computer science, CUDA, Databases, Hadoop, nVidia

April 28, 2019 by hgpu

Methods for Accelerating Machine Learning in High Performance Computing

Robert Lim

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, Tesla P100, Thesis

March 31, 2019 by hgpu

Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey

Giang Nguyen, Stefan Dlugolinsky, Martin Bobak, Viet Tran, Alvaro Lopez Garcia, Ignacio Heredia, Peter Malik, Ladislav Hluchy

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Data mining, Deep learning, Machine learning, nVidia, OpenCL

March 31, 2019 by hgpu

HeteroMap: A Runtime Performance Predictor for Efficient Processing of Graph Analytics on Heterogeneous Multi-Accelerators

Masab Ahmad, Halit Dogan, Christopher J. Michael, Omer Khan

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Tags: Algorithms, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX 970, OpenCL, Package, Performance

March 31, 2019 by hgpu

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]

PyVisc

Classify QCD phase transition with deep learning

Bytespresso: a library for Java to C/CUDA translator based on deep reification

A Development Platform for Embedded Domain-Specific Languages

faimGraph

Breadth-First Search using Dynamic Parallelism on the GPU

Vulkan Multi-GPU application

Multi-GPU Rendering with Vulkan API

Benchmarking Analysis of Vision Kernels on Embedded CPU, GPU and FPGA

Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels

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Boston, USA

7th International Workshop on OpenCL, 2019

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Boston, MA, USA

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

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Larnaca, Cyprus

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

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26-28

Rome, Italy

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

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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

Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models

Breadth-First Search using Dynamic Parallelism on the GPU

Automatic generation of warp-level primitives and atomic instructions for fast and portable parallel reduction on GPUs

Optimizing the Linear Fascicle Evaluation Algorithm for Multi-Core and Many-Core Systems

CUDA au Coq: A Framework for Machine-validating GPU Assembly Programs

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

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

AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles

GPU-based Efficient Join Algorithms on Hadoop

Methods for Accelerating Machine Learning in High Performance Computing

Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey

HeteroMap: A Runtime Performance Predictor for Efficient Processing of Graph Analytics on Heterogeneous Multi-Accelerators

Recent source codes

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

PyVisc

Bytespresso: a library for Java to C/CUDA translator based on deep reification

faimGraph

Vulkan Multi-GPU application

Benchmarking Analysis of Vision Kernels on Embedded CPU, GPU and FPGA

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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