Multicore performance prediction with MPET

Using scalability characteristics for statistical cross-architecture prediction

authored by: Oliver Jakob Arndt, Matthias Lüders, Christoph Riggers, Holger Blume
Abstract: 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.
Organisation(s): Institute of Microelectronic Systems
Type: Article
Journal: Journal of Signal Processing Systems
Volume: 92
Pages: 981-998
No. of pages: 18
ISSN: 1939-8018
Publication date: 09.2020
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Control and Systems Engineering, Theoretical Computer Science, Signal Processing, Information Systems, Modelling and Simulation, Hardware and Architecture
Electronic version(s): https://doi.org/10.1007/s11265-020-01563-w (Access: Open)
https://doi.org/10.1007/s11265-021-01691-x (Access: Open)