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RPPM: Rapid Performance Prediction of Multithreaded Workloads on Multicore Processors

S. D. Pestel,S. V. D. Steen,Shoaib Akram,L. Eeckhout

Published 2019 in IEEE International Symposium on Performance Analysis of Systems and Software

ABSTRACT

Analytical performance modeling is a useful complement to detailed cycle-level simulation to quickly explore the design space in an early design stage. Mechanistic analytical modeling is particularly interesting as it provides deep insight and does not require expensive offline profiling as empirical modeling. Previous work in mechanistic analytical modeling, unfortunately, is limited to single-threaded applications running on single-core processors. This work proposes RPPM, a mechanistic analytical performance model for multi-threaded applications on multicore hardware. RPPM collects microarchitecture-independent characteristics of a multi-threaded workload to predict performance on a previously unseen multicore architecture. The profile needs to be collected only once to predict a range of processor architectures. We evaluate RPPM's accuracy against simulation and report a performance prediction error of 11.2% on average (23% max). We demonstrate RPPM's usefulness for conducting design space exploration experiments as well as for analyzing parallel application performance.

PUBLICATION RECORD

  • Publication year

    2019

  • Venue

    IEEE International Symposium on Performance Analysis of Systems and Software

  • Publication date

    2019-03-01

  • Fields of study

    Computer Science, Engineering

  • Identifiers
    DOI 10.1109/ISPASS.2019.00038
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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