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A general guide to applying machine learning to computer architecture

Author
Nemirovsky, D.; Arkose, T.; Markovic, N.; Nemirovsky, M.; Unsal, O.; Cristal, A.; Valero, M.
Type of activity
Journal article
Journal
Supercomputing frontiers and innovations
Date of publication
2018
Volume
5
Number
1
First page
95
Last page
115
DOI
https://doi.org/10.14529/jsfi180106 Open in new window
Repository
http://hdl.handle.net/2117/117079 Open in new window
URL
http://superfri.org/superfri/article/view/165/262 Open in new window
Abstract
The resurgence of machine learning since the late 1990s has been enabled by significant advances in computing performance and the growth of big data. The ability of these algorithms to detect complex patterns in data which are extremely difficult to achieve manually, helps to produce effective predictive models. Whilst computer architects have been accelerating the performance of machine learning algorithms with GPUs and custom hardware, there have been few implementations leveraging these algor...
Citation
Nemirovsky, D., Arkose, T., Markovic, N., Nemirovsky, M., Unsal, O., Cristal, A., Valero, M. A general guide to applying machine learning to computer architecture. "Supercomputing frontiers and innovations", 2018, vol. 5, núm. 1, p. 95-115.
Keywords
Computer architecture, Data science, Parameter engineering, Performance prediction, Scheduling
Group of research
CAP - High Performace Computing Group

Participants