TY - GEN
T1 - Memory Space Recycling
AU - Ryoo, Jihyun
AU - Kandemir, Mahmut Taylan
AU - Karakoy, Mustafa
N1 - Funding Information:
The material presented in this paper is based upon work supported by the National Science Foundation under Grant Numbers 2119236, 2122155, 2028929, 1931531, and 1763681. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of NSF.
Publisher Copyright:
© 2022 Owner/Author.
PY - 2022/6/6
Y1 - 2022/6/6
N2 - Many program codes from different application domains process very large amounts of data, making their data locality/cache memory behavior critical for high performance. Prior work has addressed the data locality optimization problem in the context of both single-core and multi-core systems. Another dimension of optimization, which can be as equally important/beneficial as improving data access pattern is to reduce the data volume (total number of addresses) accessed by the program code. In this work, we explore the idea of rewriting an application program code to reduce its memory space footprint. The main idea behind this approach is to reuse/recycle, for a given data element, a memory location that has originally been assigned to another data element, provided that the lifetimes of these two data elements do not overlap with each other. We present a detailed experimental evaluation of our proposed memory space recycling strategy. The experimental results show that our proposed approach brings, respectively, 33.2%, 48.6%, 46.5%, 31.8%, and 27.9% average improvements in these metrics, in the case of single-threaded applications. With the multi-threaded versions of the same applications, the achieved improvements are 39.5%, 55.5%, 53.4%, 26.2%, and 22.2%, in the same order.
AB - Many program codes from different application domains process very large amounts of data, making their data locality/cache memory behavior critical for high performance. Prior work has addressed the data locality optimization problem in the context of both single-core and multi-core systems. Another dimension of optimization, which can be as equally important/beneficial as improving data access pattern is to reduce the data volume (total number of addresses) accessed by the program code. In this work, we explore the idea of rewriting an application program code to reduce its memory space footprint. The main idea behind this approach is to reuse/recycle, for a given data element, a memory location that has originally been assigned to another data element, provided that the lifetimes of these two data elements do not overlap with each other. We present a detailed experimental evaluation of our proposed memory space recycling strategy. The experimental results show that our proposed approach brings, respectively, 33.2%, 48.6%, 46.5%, 31.8%, and 27.9% average improvements in these metrics, in the case of single-threaded applications. With the multi-threaded versions of the same applications, the achieved improvements are 39.5%, 55.5%, 53.4%, 26.2%, and 22.2%, in the same order.
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U2 - 10.1145/3489048.3522654
DO - 10.1145/3489048.3522654
M3 - Conference contribution
AN - SCOPUS:85132177757
T3 - SIGMETRICS/PERFORMANCE 2022 - Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems
SP - 35
EP - 36
BT - SIGMETRICS/PERFORMANCE 2022 - Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems
PB - Association for Computing Machinery, Inc
T2 - 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems, SIGMETRICS/PERFORMANCE 2022
Y2 - 6 June 2022 through 10 June 2022
ER -