Benchmarking Machine Learning: How Fast Can Your Algorithms Go?

No Thumbnail Available

Links to Files

https://arxiv.org/abs/2101.03219

Permanent Link

http://hdl.handle.net/11603/21724

Collections

UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection
UMBC Student Collection

Author/Creator

Author/Creator ORCID

Date

2021-01-08

Type of Work

conference papers and proceedings preprints
Text

Department

Program

Citation of Original Publication

Ning, Zeyu et al.; Benchmarking Machine Learning: How Fast Can Your Algorithms Go?; Machine Learning, 8 Jan, 2021; https://arxiv.org/abs/2101.03219

Rights

This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.

Subjects

methods of speeding up machine learning
vector caches
parallel execution

Abstract

This paper is focused on evaluating the effect of some different techniques in machine learning speed-up, including vector caches, parallel execution, and so on. The following content will include some review of the previous approaches and our own experimental results.