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liquidSVM: A Fast and Versatile SVM package

Ingo Steinwart, Philipp Thomann

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Tags: Computer science, CUDA, Java, nVidia, Package, Python, Spark

February 26, 2017 by hgpu

Is GPGPU CCL worth it? A performance comparison between some GPU and CPU algorithms for solving connected components labeling on binary images

Alex Sundstrom, Victor Ahdel

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Tags: Algorithms, AMD Radeon R9 280X, ATI, Computer science, Image processing, nVidia, nVidia GeForce GTX 960, OpenCL, Performance, Thesis

February 26, 2017 by hgpu

Large-Scale Stochastic Learning using GPUs

Thomas Parnell, Celestine Dunner, Kubilay Atasu, Manolis Sifalakis, Haris Pozidis

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX Titan X, nVidia Quadro M 4000

February 26, 2017 by hgpu

First Experiences Optimizing Smith-Waterman on Intel’s Knights Landing Processor

Enzo Rucci, Carlos Garcia, Guillermo Botella, Armando De Giusti, Marcelo Naiouf, Manuel Prieto-Matias

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Tags: Bioinformatics, Biology, Computer science, Intel Xeon Phi, Sequence alignment, Smith-Waterman algorithm

February 26, 2017 by hgpu

Dynamic Buffer Overflow Detection for GPGPUs

Christopher Erb, Mike Collins, Joseph L. Greathouse

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Tags: AMD FirePro W9100, ATI, Benchmarking, Computer science, OpenCL, Package, Security

February 22, 2017 by hgpu

A 7.663-TOPS 8.2-W Energy-efficient FPGA Accelerator for Binary Convolutional Neural Networks

Yixing Li, Zichuan Liu, Kai Xu, Hao Yu, Fengbo Ren

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Tags: CNN, Computer science, CUDA, Deep learning, Energy-efficient computing, FPGA, Hardware Architecture, Neural networks, nVidia, nVidia GeForce GTX Titan X, Performance

February 22, 2017 by hgpu

Efficient Large-scale Approximate Nearest Neighbor Search on the GPU

Patrick Wieschollek, Oliver Wang, Alexander Sorkine-Hornung, Hendrik P.A. Lensch

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Tags: Computer science, Computer vision, CUDA, Nearest neighbour, nVidia, nVidia GeForce GTX Titan X, Vector quantization

February 22, 2017 by hgpu

Blocking Self-avoiding Walks Stops Cyber-epidemics: A Scalable GPU-based Approach

Hung T. Nguyen, Alberto Cano, Tam Vu, Thang N. Dinh

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Tags: Algorithms, Computer science, CUDA, Network communications, nVidia, nVidia GeForce GTX Titan X

February 22, 2017 by hgpu

Profiling High Level Heterogeneous Programs: Using the SPOC GPGPU framework for OCaml

Mathias Bourgoin, Emmanuel Chailloux, Anastasios Doumoulakis

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Package, Performance

February 18, 2017 by hgpu

LAMMPS’ PPPM Long-Range Solver for the Second Generation Xeon Phi

William McDoniel, Markus Hohnerbach, Rodrigo Canales, Ahmed E. Ismail, Paolo Bientinesi

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Tags: Chemistry, Computer science, Intel Xeon Phi, Molecular dynamics, Performance

February 18, 2017 by hgpu

An Efficient Parallel Data Clustering Algorithm Using Isoperimetric Number of Trees

Ramin Javadi, Saleh Ashkboos

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Tags: Algorithms, Clustering, Computer science, CUDA, nVidia, nVidia GeForce GTX 850 M

February 18, 2017 by hgpu

Trie Compression for GPU Accelerated Multi-Pattern Matching

Xavier Bellekens, Amar Seeam, Christos Tachtatzis, Robert Atkinson

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Tags: Algorithms, Compression, Computer science, CUDA, Data Structures and Algorithms, nVidia, nVidia GeForce GTX 1080

February 18, 2017 by hgpu

Page 30 of 612« First «...10 20...28 293031 32...40 50 60...»Last »

qcgpu-rust: A High Performance, Hardware accelerated, Quantum computer simulator in Rust

Simulating Quantum Computers Using OpenCL

hStreams-benchmark: The benchmark of Intel hStreams

Auto-tuning Streamed Applications on Intel Xeon Phi

Parabix

Multidimensional Parallelization for Streaming Text Processing Applications Based on Parabix Framework

HLS4ML

Fast inference of deep neural networks in FPGAs for particle physics

cytonRL: reinforcement learning, deep Q-network, double DQN, dueling DQN, prioritized experience replay

CytonRL: an Efficient Reinforcement Learning Open-source Toolkit Implemented in C++

DLL: Deep Learning Library (DLL) for C++ (ANNs, CNNs, RBMs, DBNs...)

DLL: A Blazing Fast Deep Neural Network Library

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high performance computing on graphics processing units: hgpu.org

liquidSVM: A Fast and Versatile SVM package

Is GPGPU CCL worth it? A performance comparison between some GPU and CPU algorithms for solving connected components labeling on binary images

Large-Scale Stochastic Learning using GPUs

First Experiences Optimizing Smith-Waterman on Intel’s Knights Landing Processor

Dynamic Buffer Overflow Detection for GPGPUs

A 7.663-TOPS 8.2-W Energy-efficient FPGA Accelerator for Binary Convolutional Neural Networks

Efficient Large-scale Approximate Nearest Neighbor Search on the GPU

Blocking Self-avoiding Walks Stops Cyber-epidemics: A Scalable GPU-based Approach

Profiling High Level Heterogeneous Programs: Using the SPOC GPGPU framework for OCaml

LAMMPS’ PPPM Long-Range Solver for the Second Generation Xeon Phi

An Efficient Parallel Data Clustering Algorithm Using Isoperimetric Number of Trees

Trie Compression for GPU Accelerated Multi-Pattern Matching

Recent source codes

glow: Compiler for Neural Network hardware accelerators

GENESYS: GPU system calls from on ROCm

SR-NMT: Implementation in pytorch of SR-NMT

dlbench: Benchmarking State-of-the-Art Deep Learning Software Tools

qcgpu-rust: A High Performance, Hardware accelerated, Quantum computer simulator in Rust

hStreams-benchmark: The benchmark of Intel hStreams

Parabix

HLS4ML

cytonRL: reinforcement learning, deep Q-network, double DQN, dueling DQN, prioritized experience replay

DLL: Deep Learning Library (DLL) for C++ (ANNs, CNNs, RBMs, DBNs...)

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International Conference on Biomedicine & Pharmacotherapy, 2018

8th International Workshop on Computer Science and Engineering (WCSE’18), 2018

International Conference on Robotics, Artificial Intelligence, Automation and Mechatronics, 2018

The 3rd IEEE International Conference on Image, Vision and Computing (ICIVC), 2018

The 2th International Conference on Robotics and Automation Sciences (ICRAS), 2018