Using Differential Execution Analysis to Identify Thread Interference

Understanding the performance of a multi-threaded application is difficult. The threads interfere when they access the same shared resource, which slows down their execution. Unfortunately, current profiling tools report the hardware components or the synchronization primitives that saturate, but th...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on parallel and distributed systems Jg. 30; H. 12; S. 2866 - 2878
Hauptverfasser: Bouksiaa, Mohamed Said Mosli, Trahay, Francois, Lescouet, Alexis, Voron, Gauthier, Dulong, Remi, Guermouche, Amina, Brunet, Elisabeth, Thomas, Gael
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.12.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Schlagworte:
bottleneck detection
Computer Science
Distributed, Parallel, and Cluster Computing
Energy storage
Generators
Interference
multithreading
Operating Systems
Performance analysis
Power system stability
Real-time systems
Renewable energy sources
Supply and demand
Synchronism
Multithreading
Performance analysis
Bottleneck detection
ISSN:1045-9219, 1558-2183
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Understanding the performance of a multi-threaded application is difficult. The threads interfere when they access the same shared resource, which slows down their execution. Unfortunately, current profiling tools report the hardware components or the synchronization primitives that saturate, but they cannot tell if the saturation is the cause of a performance bottleneck. In this paper, we propose a holistic metric able to pinpoint the blocks of code that suffer interference the most, regardless of the interference cause. Our metric uses performance variation as a universal indicator of interference problems. With an evaluation of 27 applications we show that our metric can identify interference problems caused by six different kinds of interference in nine applications. We are able to easily remove seven of the bottlenecks, which leads to a performance improvement of up to nine times.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2019.2927481