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research
Analytical/ML Mixed Approach for Concurrency Regulation in Software Transactional Memory
Authors
Bruno Ciciani
Pierangelo DI SANZO
Francesco Quaglia
Diego Rughetti
Publication date
1 January 2014
Publisher
'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Cite
Abstract
In this article we exploit a combination of analytical and Machine Learning (ML) techniques in order to build a performance model allowing to dynamically tune the level of concurrency of applications based on Software Transactional Memory (STM). Our mixed approach has the advantage of reducing the training time of pure machine learning methods, and avoiding approximation errors typically affecting pure analytical approaches. Hence it allows very fast construction of highly reliable performance models, which can be promptly and effectively exploited for optimizing actual application runs. We also present a real implementation of a concurrency regulation architecture, based on the mixed modeling approach, which has been integrated with the open source Tiny STM package, together with experimental data related to runs of applications taken from the STAMP benchmark suite demonstrating the effectiveness of our proposal. © 2014 IEEE
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info:doi/10.1109%2Fccgrid.2014...
Last time updated on 22/07/2021
Archivio della ricerca- Università di Roma La Sapienza
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Last time updated on 12/11/2016
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Archivio della Ricerca - Università di Roma 3
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Last time updated on 23/02/2023