High performance computing for machine learning

Authors

  • Arpad Kerestely Transilvania University of Brasov, Romania

DOI:

https://doi.org/10.31926/but.mif.2020.13.62.2.26

Keywords:

distributed, data parallelism, model parallelism, Hadoop, Spark, machine learning, data science, data mining, high performance computing

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 oer, 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 with the aid of faster and better Machine Learning algorithms. This paper aims to unveil how one benets from another, what research has achieved so far and where is it heading.

Author Biography

Arpad Kerestely, Transilvania University of Brasov, Romania

Faculty of Mathematics and Informatics

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Published

2021-01-22

Issue

Section

INFORMATICS