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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

AXC: A new format to perform the SpMV oriented to Intel Xeon Phi architecture in OpenCL

E. Coronado-Barrientos, G. Indalecio, A. Garcia-Loureiro

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Tags: Algorithms, Computer science, Intel Xeon Phi, OpenCL, Sparse matrix

February 10, 2019 by hgpu

AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning

Ameer Haj-Ali, Qijing Huang, William Moses, John Xiang, Ion Stoica, Krste Asanovic, John Wawrzynek

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Tags: Algorithms, Code generation, Compilers, Computer science, Deep learning, HLS, LLVM, Programming Languages

January 20, 2019 by hgpu

HeteroCL: A Multi-Paradigm Programming Infrastructure for Software-Defined Reconfigurable Computing

Yi-Hsiang Lai, Yuze Chi, Yuwei Hu, Jie Wang, Cody Hao Yu, Yuan Zhou, Jason Cong, Zhiru Zhang

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Tags: Algorithms, Compilers, Computer science, FPGA, Heterogeneous systems, HLS, OpenCL, Python

January 6, 2019 by hgpu

ASW: Accelerating Smith-Waterman Algorithm on Coupled CPU-GPU Architecture

Huihui Zou, Shanjiang Tang, Ce Yu, Hao Fu, Yusen Li, Wenjie Tang

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Tags: Algorithms, APU, Bioinformatics, Biology, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 750, OpenCL, Sequence alignment, Smith-Waterman algorithm

December 9, 2018 by hgpu

Optimization of a discontinuous finite element solver with OpenCL and StarPU

Philippe Helluy, Laura Mendoza, Bruno Weber

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Tags: Algorithms, Computer science, Hybrid computing, nVidia, nVidia GeForce GTX 1050 Ti, nVidia Quadro P 6000, OpenCL, Package, Task scheduling

December 9, 2018 by hgpu

A Fast and Simple Approach to Merge and Merge Sort using Wide Vector Instructions

Alex Watkins, Oded Green

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Tags: Algorithms, Computer science, Intel Xeon Phi, Sorting

December 2, 2018 by hgpu

Parallel source code transformation techniques using design patterns

David del Rio Astorga

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Tags: Algorithms, Auto-Tuning, Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 680, Tesla K40, Thesis

December 2, 2018 by hgpu

PanJoin: A Partition-based Adaptive Stream Join

Fei Pan, Hans-Arno Jacobsen

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Tags: Algorithms, Computer science, Databases, FPGA, OpenCL

December 2, 2018 by hgpu

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

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

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Tags: Algorithms, Biology, FPGA, OpenCL, Package, Smith-Waterman algorithm

November 25, 2018 by hgpu

SYCL-DNN: The SYCL-DNN neural network acceleration library

Accelerated Neural Networks on OpenCL Devices Using SYCL-DNN

OpenCL vs: Accelerated Finite-Difference Digital Synthesis

OpenCL vs: Accelerated Finite-Difference Digital Synthesis

SYCL-BLAS: An implementation of BLAS using the SYCL open standard for acceleration on OpenCL devices

Cross-Platform Performance Portability Using Highly Parametrized SYCL Kernels

High Performance Monte Carlo Simulation of Ising Model on TPU Clusters

High Performance Monte Carlo Simulation of Ising Model on TPU Clusters

The Study of the OpenCL Processing Models for the FPGA Device

The Study of the OpenCL Processing Models for the FPGA Devices

TonY: a framework to natively run deep learning frameworks on Apache Hadoop

TonY: An Orchestrator for Distributed Machine Learning Jobs

fairseq: Facebook AI Research Sequence-to-Sequence Toolkit written in Python

fairseq: A Fast, Extensible Toolkit for Sequence Modeling

mARGOt: Dynamic Application Autotuning for Self-Aware Approximate Computing

Dynamic Application Autotuning for Self-Aware Approximate Computing

SkePU2: Autotunable Multi-Backend Skeleton Programming Framework for Multicore CPU and Multi-GPU Systems

Hybrid CPU-GPU execution support in the skeleton programming framework SkePU

HeteroMap: Performance Predictors for Graph Analytics

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

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2019

May

13-15

Boston, USA

7th International Workshop on OpenCL, 2019

2019

May

13

Boston, MA, USA

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

2019

May

14

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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