Transactional Memory Scheduling Using Machine Learning Techniques

Current shared memory multi-core systems require powerful software and hardware techniques to support the performance parallel computation and consistency simultaneously. The use of transactional memory results in significant improvement of performance by avoiding thread synchronization and locks ov...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	2016 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP) S. 718 - 725
Hauptverfasser:	Assiri, Basem, Busch, Costas
Format:	Tagungsbericht Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.02.2016
Schlagworte:	Accuracy Algorithms Computer programs Decisions Fairness Values Hidden Markov Model Hidden Markov models History K-Nearest Neighbor Lazy Snapshot Algorithm Locks Machine learning Machine learning algorithms Scheduling Support Vector Machine Support vector machines Synchronism Throughput Training Transactional Memory
ISSN:	2377-5750
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!