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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

Go game move prediction using convolutional neural network

Marek Korenciak

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Tags: Caffe, CNN, Computer science, CUDA, Deep learning, Games, nVidia, nVidia GeForce GTX 1080 Ti, Package, Thesis

June 24, 2018 by hgpu

BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism

Nicolas Weber, Florian Schmidt, Mathias Niepert, Felipe Huici

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Tags: AI, Artificial intelligence, cache, CNN, Computer science, cpu, CUDA, Deep learning, GPU, Machine learning, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX 1080 Ti

April 25, 2018 by hgpu

Fast and Flexible GPU Accelerated Binding Free Energy Calculations within the AMBER Molecular Dynamics Package

Daniel J. Mermelstein, Charles Lin, Gard Nelson, Rachael Kretsch, J. Andrew McCammon, Ross C. Walker

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Tags: Algorithms, Bioinformatics, Biology, CUDA, Molecular dynamics, nVidia, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX Titan X

January 20, 2018 by hgpu

Scalable and massively parallel Monte Carlo photon transport simulations for heterogeneous computing platforms

Leiming Yu, Fanny Nina-Paravecino, David Kaeli, Qianqian Fang

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Tags: Algorithms, AMD R9 Nano, AMD Radeon RX 480, ATI, Computational Physics, Heterogeneous systems, nVidia, nVidia GeForce GTX 1050 Ti, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX 590, nVidia GeForce GTX 980 Ti, nVidia GeForce GTX Titan X, OpenCL, Package, Physics

November 12, 2017 by hgpu

GooFit 2.0

Henry Schreiner, Christoph Hasse, Bradley Hittle, Himadri Pandey, Michael Sokoloff, Karen Tomko

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Tags: Computer science, CUDA, Mathematical Software, MPI, nVidia, nVidia GeForce GTX 1050 Ti, nVidia GeForce GTX 1080 Ti, Package, Probability, Tesla K40, Tesla P100

October 29, 2017 by hgpu

DeepProf: Performance Analysis for Deep Learning Applications via Mining GPU Execution Patterns

Jiazhen Gu, Huan Liu, Yangfan Zhou, Xin Wang

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080 Ti, Performance, Suffix trees

July 14, 2017 by hgpu

CLort: High Throughput and Low Energy Network Intrusion Detection on IoT Devices with Embedded GPUs

SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks

SWIFOLD: Smith-Waterman implementation on FPGA with OpenCL for long DNA sequences

Spatter: Benchmark for measuring the performance of sparse and irregular memory access

Spatter: A Benchmark Suite for Evaluating Sparse Access Patterns

StePS: Stereographically Projected Cosmological Simulations

StePS: A Multi-GPU Cosmological N-body Code for Compactified Simulations

AMGCL:C++ library for solving large sparse linear systems with algebraic multigrid method

AMGCL: an Efficient, Flexible, and Extensible Algebraic Multigrid Implementation

PAstudy

Double-precision FPUs in High-Performance Computing: an Embarrassment of Riches?

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Rome, Italy

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

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Rome, Italy

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Miami, Florida, USA

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26-28

Vancouver, Canada

3rd International Conference on Vision, Image and Signal Processing (ICVISP), 2019

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high performance computing on graphics processing units: hgpu.org

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

Go game move prediction using convolutional neural network

BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism

Fast and Flexible GPU Accelerated Binding Free Energy Calculations within the AMBER Molecular Dynamics Package

Scalable and massively parallel Monte Carlo photon transport simulations for heterogeneous computing platforms

GooFit 2.0

DeepProf: Performance Analysis for Deep Learning Applications via Mining GPU Execution Patterns

Recent source codes

schnaps: a finite element solver

GPU-benchmarks

Truchas: 3D Multiphysics Simulation of Metal Casting and Processing

CLort: High Throughput and Low Energy Network Intrusion Detection on IoT Devices with Embedded GPUs

SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks

SWIFOLD: Smith-Waterman implementation on FPGA with OpenCL for long DNA sequences

Spatter: Benchmark for measuring the performance of sparse and irregular memory access

StePS: Stereographically Projected Cosmological Simulations

AMGCL:C++ library for solving large sparse linear systems with algebraic multigrid method

PAstudy

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Featured events

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

3rd World Conference on Clinical Research and Biomarkers, 2019

3rd International Conference on Vision, Image and Signal Processing (ICVISP), 2019