Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/9860
Title: Computing with Near Data [Article]
Authors: Tang, X.
Kandemir, M.T.
Zhao, H.
Jung, M.
Karakoy, M.
Keywords: data movement
manycore systems
recomputation
Program compilers
Cumulative cost
Data access
Data movements
Extra computations
Recomputation
Storage elements
Digital storage
Issue Date: 2019
Publisher: Association for Computing Machinery
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 is held by the owner/author(s).
URI: https://doi.org/10.1145/3309697.3331487
https://hdl.handle.net/20.500.11851/9860
ISSN: 0163-5999
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Show full item record

CORE Recommender

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.