Title:
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REVIEWING DATA ACCESS PATTERNS
AND COMPUTATIONAL REDUNDANCY FOR MACHINE
LEARNING ALGORITHMS |
Author(s):
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Imen Chakroun, Tom Vander Aa and Tom Ashby |
ISBN:
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978-989-8533-92-0 |
Editors:
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Ajith P. Abraham and Jörg Roth |
Year:
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2019 |
Edition:
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Single |
Keywords:
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Increasing Data Locality, Data Redundancy and Reuse, Machine Learning |
Type:
|
Full Paper |
First Page:
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31 |
Last Page:
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38 |
Language:
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English |
Cover:
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Full Contents:
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click to dowload
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Paper Abstract:
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Machine learning (ML) is probably the first and foremost used technique to deal with the size and complexity of the new
generation of data. In this paper, we analyze one of the means to increase the performances of ML algorithms which is
exploiting data locality. Data locality and access patterns are often at the heart of performance issues in computing systems
due to the use of certain hardware techniques to improve performance. Altering the access patterns to increase locality can
dramatically increase performance of a given algorithm. Besides, repeated data access can be seen as redundancy in data
movement. Similarly, there can also be redundancy in the repetition of calculations. This work also identifies some of the
opportunities for avoiding these redundancies by directly reusing computation results. We document the possibilities of
such reuse in some selected machine learning algorithms and give initial indicative results from our first experiments on
data access improvement and algorithm redesign. |
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