Sie können Bookmarks mittels Listen verwalten, loggen Sie sich dafür bitte in Ihr SLUB Benutzerkonto ein.
Medientyp:
E-Artikel;
Sonstige Veröffentlichung
Titel:
Multicore Performance Prediction with MPET : Using Scalability Characteristics for Statistical Cross-Architecture Prediction
Beteiligte:
Arndt, Oliver Jakob
[VerfasserIn];
Lüders, Matthias
[VerfasserIn];
Riggers, Christoph
[VerfasserIn];
Blume, Holger
[VerfasserIn]
Erschienen:
Norwell, Mass. : Springer, 2020
Erschienen in:Journal of Signal Processing Systems (formerly: Journal of VLSI signal processing systems for signal, image, and video technology) 92 (2020), Nr. 9 ; Journal of Signal Processing Systems (formerly: Journal of VLSI signal processing systems for signal, image, and video technology)
Anmerkungen:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Beschreibung:
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.