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An Analysis of Variation Between Cores For Intel Xeon Phi Knights Corner And Xeon Phi Knights Landing

Jamar Robinson

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Tags: Computer science, Intel Xeon Phi, Performance, Thesis

June 25, 2017 by hgpu

Smith-Waterman Acceleration in Multi-GPUs: A Performance per Watt Analysis

Jesus Perez Serrano, Edans Flavius De Oliveira Sandes, Alba Cristina Magalhaes Alves de Melo, Manuel Ujaldon

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Tags: Bioinformatics, Biology, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 980, Performance, Smith-Waterman algorithm

June 10, 2017 by hgpu

Speedup and Parallelization Models for Energy-Efficient Many-Core Systems Using Performance Counters

Mohammed A. Noaman Al-hayanni, Rishad Shafik, Ashur Rafiev, Fei Xia, Alex Yakovlev

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Tags: Computer science, Energy-efficient computing, Intel Xeon Phi, Performance

June 5, 2017 by hgpu

Towards Enhancing Performance, Programmability, and Portability in Heterogeneous Computing

Konstantinos Krommydas

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Tags: ATI, ATI Radeon HD 6550, ATI Radeon HD 7660, ATI Radeon HD 7970, Code generation, Compilers, Computer science, FPGA, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Package, Performance, Tesla K20, Thesis

May 9, 2017 by hgpu

TinyDL: Just-In-Time Deep Learning Solution For Constrained Embedded Systems

Bita Darvish Rouhani, Azalia Mirhoseini, Farinaz Koushanfar

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Tags: ARM, Computer science, CUDA, Deep learning, nVidia, nVidia Jetson TK1, Performance

May 2, 2017 by hgpu

Deep Learning in the Automotive Industry: Applications and Tools

Andre Luckow, Matthew Cook, Nathan Ashcraft, Edwin Weill, Emil Djerekarov, Bennie Vorster

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Tags: Cloud, Computer science, Computer vision, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, nVidia GRID K520, Performance

May 2, 2017 by hgpu

Accelerating Discrete Wavelet Transforms on Parallel Architectures

David Barina, Michal Kula, Michal Matysek, Pavel Zemcik

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Tags: Algorithms, ATI, ATI Radeon HD 6970, Discrete Wavelet Transform, Image processing, nVidia, nVidia GeForce GTX Titan X, OpenCL, OpenGL, Performance, Pixel shaders

April 30, 2017 by hgpu

Benchmarking OpenCL, OpenACC, OpenMP, and CUDA: programming productivity, performance, and energy consumption

Suejb Memeti, Lu Li, Sabri Pllana, Joanna Kolodziej, Christoph Kessler

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Tags: Benchmarking, Computer science, CUDA, Energy-efficient computing, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX Titan X, OpenACC, OpenCL, OpenMP, Performance

April 20, 2017 by hgpu

Parallel Multi Channel Convolution using General Matrix Multiplication

Aravind Vasudevan, Andrew Anderson, David Gregg

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Tags: Algorithms, ARM, Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, Neural networks, nVidia, nVidia Tegra TX1, Performance

April 17, 2017 by hgpu

Optimizing Communication by Compression for Multi-GPU Scalable Breadth-First Searches

Julian Romera

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Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, OpenMPI, Performance, Tesla K80, Tesla M2090, Thesis

April 7, 2017 by hgpu

FPGA-based Tsunami Simulation: Performance Comparison with GPUs, and Roofline Model for Scalability Analysis

Kohei Nagasu, Kentaro Sano, Fumiya Kono, Naohito Nakasato

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Tags: AMD Radeon R9 280X, ATI, Earth and Space Sciences, FPGA, nVidia, OpenCL, Performance, Tesla K20

April 3, 2017 by hgpu

APUNet: Revitalizing GPU as Packet Processing Accelerator

Younghwan Go, Muhammad Jamshed, YoungGyoun Moon, Changho Hwang, KyoungSoo Park

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Tags: Algorithms, AMD, APU, ATI, Computer science, nVidia, nVidia GeForce GTX 980, OpenCL, Performance

March 28, 2017 by hgpu

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DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications

OpenAdSCL

Scalar collapse in AdS with an OpenCL open source code

Rgtsvm: Support Vector Machines on a GPU in R

Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras

gloo: Collective communications library with various primitives for multi-machine training

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU

CELES: CUDA-accelerated electromagnetic scattering by large ensembles of spheres

CELES: CUDA-accelerated simulation of electromagnetic scattering by large ensembles of spheres

Boda: A C++ Framework for Efficient Experiments in Computer Vision [WIP]

Implementing Efficient, Portable Computations for Machine Learning

Parallel and in-process compilation of individuals for genetic programming on GPU

ESPRESO: Highly Parallel Solvers for Engineering Applications

Intel Xeon Phi acceleration of Hybrid Total FETI solver

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11-13

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7-9

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2nd International Conference on Communication and Information Systems (ICCIS), 2017

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high performance computing on graphics processing units: hgpu.org

An Analysis of Variation Between Cores For Intel Xeon Phi Knights Corner And Xeon Phi Knights Landing

Smith-Waterman Acceleration in Multi-GPUs: A Performance per Watt Analysis

Speedup and Parallelization Models for Energy-Efficient Many-Core Systems Using Performance Counters

Towards Enhancing Performance, Programmability, and Portability in Heterogeneous Computing

TinyDL: Just-In-Time Deep Learning Solution For Constrained Embedded Systems

Accelerating Discrete Wavelet Transforms on Parallel Architectures

Benchmarking OpenCL, OpenACC, OpenMP, and CUDA: programming productivity, performance, and energy consumption

Parallel Multi Channel Convolution using General Matrix Multiplication

Optimizing Communication by Compression for Multi-GPU Scalable Breadth-First Searches

FPGA-based Tsunami Simulation: Performance Comparison with GPUs, and Roofline Model for Scalability Analysis

APUNet: Revitalizing GPU as Packet Processing Accelerator

Recent source codes

DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications

OpenAdSCL

Rgtsvm: Support Vector Machines on a GPU in R

Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras

gloo: Collective communications library with various primitives for multi-machine training

MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU

CELES: CUDA-accelerated electromagnetic scattering by large ensembles of spheres

Boda: A C++ Framework for Efficient Experiments in Computer Vision [WIP]

Parallel and in-process compilation of individuals for genetic programming on GPU

ESPRESO: Highly Parallel Solvers for Engineering Applications

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4th International Conference on Biomedical and Bioinformatics Engineering (ICBBE), 2017

9th International Conference on Signal Processing Systems (ICSPS), 2017

The 5th International conference on Control, Mechatronics and Automation (ICCMA), 2017

8th International Conference on Biology, Environment and Chemistry (ICBEC), 2017

2nd International Conference on Communication and Information Systems (ICCIS), 2017