High Performance Computing: From Deep Learning to Data Engineering

被引：1

作者：

Fox, Geoffrey ^{[1
]}

机构：

[1] Indiana Univ, Bloomington, IN 47405 USA

来源：

2020 IEEE 34TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM WORKSHOPS (IPDPSW 2020) | 2020年

关键词：

D O I：

10.1109/IPDPSW50202.2020.00164

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

We describe how High-Performance Computing (HPC) can be used to enhance Big Data and Machine Learning (ML) systems (HPC for ML) but also how machine learning can be used to enhance system execution (ML for HPC). We review the different aspects of data engineering needed to process large scale data and how it is implemented in the Twister2 system combined with PyTorch and TensorFlow. We discuss the different forms of parallelism seen in deep learning with a focus on pipelined parallelism over layers

引用

页码：988 / 988

页数：1

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