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FusionStitching: Deep Fusion and Code Generation for Tensorflow Computations on GPUs

Guoping Long, Jun Yang, Kai Zhu, Wei Lin

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Tags: Code generation, Computer science, CUDA, Deep learning, Machine learning, nVidia, TensorFlow

November 18, 2018 by hgpu

A Hybrid GPU-FPGA-based Computing Platform for Machine Learning

Xu Liu, Hibat Allah Ounifi, Abdelouahed Gherbi, Yves Lemieux, Wubin Li

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Tags: Algorithms, Benchmarking, CNN, Computer science, FPGA, Hybrid computing, Machine learning, nVidia, nVidia GeForce GTX Titan XP, OpenCL

November 11, 2018 by hgpu

Automatic Mapping for OpenCL-Programs on CPU/GPU Heterogeneous Platforms

Konrad Moren, Diana Gohringer

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Tags: AMD Radeon R9 290, ATI, Compilers, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 780 Ti, OpenCL

October 28, 2018 by hgpu

The Ocean Tensor Package

Ewout van den Berg

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, Package

October 28, 2018 by hgpu

Towards Efficient Large-Scale Graph Neural Network Computing

Lingxiao Ma, Zhi Yang, Youshan Miao, Jilong Xue, Ming Wu, Lidong Zhou, Yafei Dai

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Tags: Computer science, CUDA, Deep learning, Graph theory, Machine learning, Neural networks, nVidia, Tesla P100

October 28, 2018 by hgpu

TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

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Tags: Compilers, Computer science, Deep learning, FPGA, Machine learning, nVidia, nVidia GeForce GTX Titan X, Optimization, Package, performance portability

October 13, 2018 by hgpu

On Reinforcement Learning for Full-length Game of StarCraft

Zhen-Jia Pang, Ruo-Ze Liu, Zhou-Yu Meng, Yi Zhang, Yang Yu, Tong Lu

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Tags: AI, Algorithms, Artificial intelligence, Computer science, Deep learning, Games, Machine learning, nVidia, Tesla K40

October 6, 2018 by hgpu

Scalability Analysis of Synchronous Data-Parallel Artificial Neural Network (ANN) Learners

Chang Sun

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Tags: Computer science, Machine learning, Neural networks, nVidia, OpenCL, Tesla P100, Thesis

September 23, 2018 by hgpu

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

Jack Turner, Jose Cano, Valentin Radu, Elliot J. Crowley, Michael O'Boyle, Amos Storkey

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Tags: ARM, Compression, Computer science, Deep learning, Machine learning, Neural networks, OpenCL, OpenMP, Package

September 16, 2018 by hgpu

A deep learning approach to autonomous lunar landing

Ilaria Bloise, Marcello Orlandelli

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Tags: Algorithms, Artificial intelligence, Computer science, Deep learning, LSTM, Machine learning, Neural networks, nVidia, RNN, Tesla K20, Thesis

September 16, 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

CosmoFlow: Using Deep Learning to Learn the Universe at Scale

Amrita Mathuriya, Deborah Bard, Peter Mendygral, Lawrence Meadows, James Arnemann, Lei Shao, Siyu He, Tuomas Karna, Daina Moise, Simon J. Pennycook, Kristyn Maschoff, Jason Sewall, Nalini Kumar, Shirley Ho, Mike Ringenburg, Prabhat, Victor Lee

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Tags: Astrophysics, Cosmology, CUDA, Deep learning, Intel Xeon Phi, Machine learning, nVidia, Package, TensorFlow, Tesla P100

August 19, 2018 by hgpu

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?

hpx: The C++ Standard Library for Parallelism and Concurrency

Integration of CUDA Processing within the C++ library for parallelism and concurrency (HPX)

OpenDwarfs: a benchmark suite consisting of different computation/communication idioms, i.e., dwarfs, for state-of-art multicore CPUs, GPUs, Intel MICs and Altera FPGAs

OpenCL Performance Prediction using Architecture-Independent Features

Rodinia Benchmark Suite ported for OpenCL-capable Intel FPGAs

High Performance Computing with FPGAs and OpenCL

The Ocean Tensor Package

The Ocean Tensor Package

Tornado

Using Compiler Snippets to Exploit Parallelism on Heterogeneous Hardware: A Java Reduction Case Study

pocl: Portable Computing Language

Exploiting Task Parallelism with OpenCL: A Case Study

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2019

February

25-28

Shanghai, China

International Conference on Image, Video and Signal Processing (IVSP), 2019

2018

November

20-22

Khalifa University, Abu Dhabi, UAE

International Joint Conference on Signals, Systems and Computers (CSSC), 2018

2018

December

13-15

Wellington, New Zealand

International Joint Conference on Robotics, Automation and Mechatronics (JCRAM), 2018

2018

December

10-12

Wellington, New Zealand

International Joint Conference on Robotics and Artificial Intelligence (JCRAI), 2018

2018

December

13-15

Wellington, New Zealand

International Joint Conference on Computer Vision and Pattern Recognition (CCVPR), 2018

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