High performance computing for machine learning

Q4 Mathematics
Arpad Kerestely
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引用次数: 1

Abstract

Efficient High Performance Computing for Machine Learning has become a necessity in the past few years. Data is growing exponentially in domains like healthcare, government, economics and with the development of IoT, smartphones and gadgets. This big volume of data, needs a storage space which no traditional computing system can offer, and needs to be fed to Machine Learning algorithms so useful information can be extracted out of it. The larger the dataset that is fed to a Machine Learning algorithm the more precise the results will be, but also the time to compute those results will increase. Thus, the need for Efficient High Performance computing in the aid of faster and better Machine Learning algorithms. This paper aims to unveil how one benefits from another, what research has achieved so far and where is it heading.
用于机器学习的高性能计算
在过去的几年里,高效的高性能计算已经成为机器学习的必需品。随着物联网、智能手机和电子设备的发展,医疗保健、政府、经济等领域的数据呈指数级增长。如此庞大的数据量,需要传统计算系统无法提供的存储空间,并且需要将其输入机器学习算法,以便从中提取有用的信息。提供给机器学习算法的数据集越大,结果就越精确,但计算这些结果的时间也会增加。因此,需要在更快更好的机器学习算法的帮助下进行高效的高性能计算。本文旨在揭示一个人如何受益于另一个人,迄今为止取得了哪些研究成果,以及研究的方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
0.30
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