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Model-based simulation for SMT cores

Published: 06 June 2022 Publication History

Abstract

Studies that evaluate new architectural designs of virtual memory typically employ a "model-based" methodology that relies on simulations of the translation lookaside buffer (TLB) coupled with empirical performance models. We observe that this methodology is limited in that each simulated thread of execution has its own dedicated TLB, whereas modern processors share a single TLB among multiple threads through simultaneous multithreading (SMT). Existing model-based research is thus unable to explore virtual memory designs in SMT context. We address this problem by developing a simulator that realistically combines memory reference streams of multiple SMT threads into one TLB. Our simulator is 99% accurate across four Intel processor generations running a wide range of memory-intensive workloads. Our validation efforts lead us to discover a previously undocumented eviction policy in Broadwell TLBs.

Reference

[1]
Mohammad Agbarya, Idan Yaniv, Jayneel Gandhi, and Dan Tsafrir. 2020. Predicting execution times with partial simulations in virtual memory research: why and how. In IEEE/ACM International Symposium on Microarchitecture (MICRO). 456--470.

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cover image ACM Conferences
SYSTOR '22: Proceedings of the 15th ACM International Conference on Systems and Storage
June 2022
163 pages
ISBN:9781450393805
DOI:10.1145/3534056
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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  • Technion: Israel Institute of Technology
  • USENIX Assoc: USENIX Assoc

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Association for Computing Machinery

New York, NY, United States

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Published: 06 June 2022

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SYSTOR '22
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SYSTOR '22 Paper Acceptance Rate 12 of 41 submissions, 29%;
Overall Acceptance Rate 108 of 323 submissions, 33%

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