Computing with near data

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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.
Publisher
Association for Computing Machinery, Inc
Issue Date
2019-06-24
Language
English
Citation

14th Joint Conference of International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS 2019 and IFIP Performance Conference 2019, SIGMETRICS/Performance 2019, pp.27 - 28

DOI
10.1145/3309697.3331487
URI
http://hdl.handle.net/10203/269373
Appears in Collection
EE-Conference Papers(학술회의논문)
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