Computing with near data

Xulong Tang, Mahmut Kandemir, Hui Zhao, Myoungsoo Jung, Mustafa Karakoy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The cost of moving data between compute elements and storage elements plays a signiicant role in shaping the overall performance of applications.We present a compiler-driven approach to reducing data movement costs. Our approach, referred to as Computing with Near Data (CND), is built upon a concept called ?recomputationž, in which a costly data access is replaced by a few less costly data accesses plus some extra computation, if the cumulative cost of the latter is less than that of the costly data access. Experimental result reveals that i) the average recomputability across our benchmarks is 51.1%, ii) our compiler-driven strategy is able to exploit 79.3% of the recomputation opportunities presented by our workloads, and iii) our enhancements increase the value of the recomputability metric signiicantly.

Original languageEnglish (US)
Title of host publicationSIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems
PublisherAssociation for Computing Machinery, Inc
Pages27-28
Number of pages2
ISBN (Electronic)9781450366786
DOIs
StatePublished - Jun 20 2019
Event14th Joint Conference of International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2019 and IFIP Performance Conference 2019, SIGMETRICS/Performance 2019 - Phoenix, United States
Duration: Jun 24 2019Jun 28 2019

Publication series

NameSIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems

Conference

Conference14th Joint Conference of International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2019 and IFIP Performance Conference 2019, SIGMETRICS/Performance 2019
CountryUnited States
CityPhoenix
Period6/24/196/28/19

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All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Computer Networks and Communications
  • Computational Theory and Mathematics

Cite this

Tang, X., Kandemir, M., Zhao, H., Jung, M., & Karakoy, M. (2019). Computing with near data. In SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems (pp. 27-28). (SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems). Association for Computing Machinery, Inc. https://doi.org/10.1145/3309697.3331487
Tang, Xulong ; Kandemir, Mahmut ; Zhao, Hui ; Jung, Myoungsoo ; Karakoy, Mustafa. / Computing with near data. SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems. Association for Computing Machinery, Inc, 2019. pp. 27-28 (SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems).
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title = "Computing with near data",
abstract = "The cost of moving data between compute elements and storage elements plays a signiicant role in shaping the overall performance of applications.We present a compiler-driven approach to reducing data movement costs. Our approach, referred to as Computing with Near Data (CND), is built upon a concept called ?recomputationž, in which a costly data access is replaced by a few less costly data accesses plus some extra computation, if the cumulative cost of the latter is less than that of the costly data access. Experimental result reveals that i) the average recomputability across our benchmarks is 51.1{\%}, ii) our compiler-driven strategy is able to exploit 79.3{\%} of the recomputation opportunities presented by our workloads, and iii) our enhancements increase the value of the recomputability metric signiicantly.",
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Tang, X, Kandemir, M, Zhao, H, Jung, M & Karakoy, M 2019, Computing with near data. in SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems. SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems, Association for Computing Machinery, Inc, pp. 27-28, 14th Joint Conference of International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2019 and IFIP Performance Conference 2019, SIGMETRICS/Performance 2019, Phoenix, United States, 6/24/19. https://doi.org/10.1145/3309697.3331487

Computing with near data. / Tang, Xulong; Kandemir, Mahmut; Zhao, Hui; Jung, Myoungsoo; Karakoy, Mustafa.

SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems. Association for Computing Machinery, Inc, 2019. p. 27-28 (SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Jung, Myoungsoo

AU - Karakoy, Mustafa

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N2 - The cost of moving data between compute elements and storage elements plays a signiicant role in shaping the overall performance of applications.We present a compiler-driven approach to reducing data movement costs. Our approach, referred to as Computing with Near Data (CND), is built upon a concept called ?recomputationž, in which a costly data access is replaced by a few less costly data accesses plus some extra computation, if the cumulative cost of the latter is less than that of the costly data access. Experimental result reveals that i) the average recomputability across our benchmarks is 51.1%, ii) our compiler-driven strategy is able to exploit 79.3% of the recomputation opportunities presented by our workloads, and iii) our enhancements increase the value of the recomputability metric signiicantly.

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Tang X, Kandemir M, Zhao H, Jung M, Karakoy M. Computing with near data. In SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems. Association for Computing Machinery, Inc. 2019. p. 27-28. (SIGMETRICS Performance 2019 - Abstracts of the 2019 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems). https://doi.org/10.1145/3309697.3331487