Multicore Performance Prediction – Comparing Three Recent Approaches in a Case Study

Lüders, Matthias; Arndt, Oliver Jakob; Blume, Holger

doi:10.1007/978-3-030-48340-1_22

Cited by 1 publication

(1 citation statement)

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“…In a case study, which has been published as an extended version in [32], we predicted the performance of a Xilinx Zynq Ultrascale+ EG platform with a virtual prototype in GEM5, an analytic processor model in Exabounds, and our proposed statistical approach. This platform exhibits four ARM Cortex-A53 cores running at 1.2 GHz and 4 GB RAM.…”

Section: Case Studymentioning

confidence: 99%

Multicore Performance Prediction with MPET

Arndt

Lüders

Riggers

et al. 2020

J Sign Process Syst

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Multicore processors serve as target platforms in a broad variety of applications ranging from high-performance computing to embedded mobile computing and automotive applications. But, the required parallel programming opens up a huge design space of parallelization strategies each with potential bottlenecks. Therefore, an early estimation of an application’s performance is a desirable development tool. However, out-of-order execution, superscalar instruction pipelines, as well as communication costs and (shared-) cache effects essentially influence the performance of parallel programs. While offering low modeling effort and good simulation speed, current approximate analytic models provide moderate prediction results so far. Virtual prototyping requires a time-consuming simulation, but produces better accuracy. Furthermore, even existing statistical methods often require detailed knowledge of the hardware for characterization. In this work, we present a concept called Multicore Performance Evaluation Tool (MPET) and its evaluation for a statistical approach for performance prediction based on abstract runtime parameters, which describe an application’s scalability behavior and can be extracted from profiles without user input. These scalability parameters not only include information on the interference of software demands and hardware capabilities, but indicate bottlenecks as well. Depending on the database setup, we achieve a competitive accuracy of 20% mean prediction error (11% median), which we also demonstrate in a case study.

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Section: Case Studymentioning

confidence: 99%