Using Machine Learning Techniques to Detect Parallel Patterns of Multi-threaded Applications

Multicore hardware and software are becoming increasingly more complex. The programmability problem of multicore software has led to the use of parallel patterns. Parallel patterns reduce the effort and time required to develop multicore software by effectively capturing its thread communication and...

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Veröffentlicht in:International journal of parallel programming Jg. 44; H. 4; S. 867 - 900
Hauptverfasser: Deniz, Etem, Sen, Alper
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.08.2016
Springer Nature B.V
Schlagworte:
Accuracy
Analysis
Benchmarks
Central processing units
Communication
Computer programming
Computer programs
Computer Science
CPUs
Data retrieval
Decision trees
Design
Designers
Experiments
Feature selection
Hardware
Information sharing
Machine learning
Optimization
Parallel processing
Pattern recognition
Principal components analysis
Processor Architectures
Software
Software Engineering/Programming and Operating Systems
Studies
Theory of Computation
Pattern detection
Multicore software
Parallel programming
Parallel patterns
Multi-threaded applications
ISSN:0885-7458, 1573-7640
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Multicore hardware and software are becoming increasingly more complex. The programmability problem of multicore software has led to the use of parallel patterns. Parallel patterns reduce the effort and time required to develop multicore software by effectively capturing its thread communication and data sharing characteristics. Hence, detecting the parallel pattern used in a multi-threaded application is crucial for performance improvements and enables many architectural optimizations; however, this topic has not been widely studied. We apply machine learning techniques in a novel approach to automatically detect parallel patterns and compare these techniques in terms of accuracy and speed. We experimentally validate the detection ability of our techniques on benchmarks including PARSEC and Rodinia. Our experiments show that the k-nearest neighbor, decision trees, and naive Bayes classifier are the most accurate techniques. Overall, decision trees are the fastest technique with the lowest characterization overhead producing the best combination of detection results. We also show the usefulness of the proposed techniques on synthetic benchmark generation.
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ISSN:0885-7458
1573-7640
DOI:10.1007/s10766-015-0396-z