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Workload-aware Automatic Parallelization for Multi-GPU DNN Training

Sungho Shin, Youngmin Jo, Jungwook Choi, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wonyong Sung

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Tags: Artificial intelligence, Computer science, Deep learning, Neural networks, nVidia, nVidia DGX-1, nVidia GeForce GTX Titan XP, nVidia Tesla GP100

November 11, 2018 by hgpu

Watershed analysis algorithm implementation in CUDA and OpenACC

Parallelizing Multiple Flow Accumulation Algorithm using CUDA and OpenACC

OpenCL Performance Prediction using Architecture-Independent Features

Characterizing and Predicting Scientific Workloads for Heterogeneous Computing Systems

PySPH: a Python-based SPH framework

PySPH: a Python-based framework for smoothed particle hydrodynamics

Efficient Interleaved Batch Matrix Solvers for CUDA

Efficient Interleaved Batch Matrix Solvers for CUDA

ArborX: Performance-portable geometric search library

ArborX: A Performance Portable Search Library

FlowSeq: Generative Flow based Sequence-to-Sequence Toolkit

FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow

Compositional Deep Learning in Futhark

Compositional Deep Learning in Futhark

MLPerf Benchmark Suite

Demystifying the MLPerf Benchmark Suite

stdgpu: Efficient STL-like Data Structures on the GPU

stdgpu: Efficient STL-like Data Structures on the GPU

ikra-cpp: C++ Library for Object-oriented Programming with Structure of Arrays Layout

Memory-Efficient Object-Oriented Programming on GPUs

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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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