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arXiv:2001.03000 (cs)

Title:Guidelines for enhancing data locality in selected machine learning algorithms

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Abstract: To deal with the complexity of the new bigger and more complex generation of data, machine learning (ML) techniques are probably the first and foremost used. For ML algorithms to produce results in a reasonable amount of time, they need to be implemented efficiently. In this paper, we analyze one of the means to increase the performances of machine learning 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 start by motivating why and how a more efficient implementation can be achieved by exploiting reuse in the memory hierarchy of modern instruction set processors. Next we document the possibilities of such reuse in some selected machine learning algorithms.
Comments: European Commission Project: EPEEC - European joint Effort toward a Highly Productive Programming Environment for Heterogeneous Exascale Computing (EC-H2020-80151) This an extended version of arXiv:1904.11203
Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML)
Journal reference: Intelligent Data Analysis, vol. 23, no. 5, pp. 1003-1020, 2019
DOI: 10.3233/IDA-184287
Cite as: arXiv:2001.03000 [cs.LG]
  (or arXiv:2001.03000v1 [cs.LG] for this version)

Submission history

From: Tom Vander Aa [view email]
[v1] Thu, 9 Jan 2020 14:16:40 UTC (319 KB)
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