Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/5629
Title: Computing with near data
Authors: Tang, Xulong
Kandemir, Mahmut Taylan
Zhao, H.
Jung, M.
Karaköy, Mustafa
Keywords: Data movement
Manycore systems
Recomputation
Issue Date: 2019
Publisher: Association for Computing Machinery, Inc
Source: 14th Joint Conference of International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2019 and IFIP Performance Conference 2019, SIGMETRICS/Performance 2019, 24 June 2019 through 28 June 2019, , 149007
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 ?recomputation?, 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. © 2019 Copyright held by the owner/author(s).
URI: https://doi.org/10.1145/3309697.3331487
https://hdl.handle.net/20.500.11851/5629
ISBN: 9781450366786
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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