Trading Computation for Communication: A Taxonomy of Data Recomputation Techniques

Ismail Akturk, Ulya R. Karpuzcu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

A critical challenge for modern system design is meeting the overwhelming performance, storage, and communication bandwidth demand of emerging applications within a tightly bound power budget. As both the time and power, hence the energy, spent in data communication by far exceeds the energy spent in actual data generation (i.e., computation), (re)computing data can easily become cheaper than storing and retrieving (pre)computed data. Therefore, trading computation for communication can improve energy efficiency by minimizing the energy overhead incurred by data storage, retrieval, and communication. This paper provides a taxonomy for the computation versus communication trade-off accompanied by a quantitative characterization.

Original languageEnglish (US)
Article number8543839
Pages (from-to)496-506
Number of pages11
JournalIEEE Transactions on Emerging Topics in Computing
Volume9
Issue number1
DOIs
StatePublished - Jan 1 2021

Bibliographical note

Funding Information:
This work was supported by US National Science Foundation CAREER CCF-1553042.

Publisher Copyright:
© 2013 IEEE.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Data recomputation
  • amnesic execution
  • communication reduction
  • energy efficiency
  • load value prediction

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