Open AccessHigh performance computing for machine learning
Author(s) -
Arpad Kerestely
Publication year - 2020
Publication title -
bulletin of the "transilvania" university of braşov. series iii, mathematics, informatics, physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.203
H-Index - 5
eISSN - 2065-216X
pISSN - 2065-2151
DOI - 10.31926/but.mif.2020.13.62.2.26
Subject(s) - computer science , big data , machine learning , artificial intelligence , volume (thermodynamics) , space (punctuation) , data science , data mining , operating system , physics , quantum mechanics
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.