Transactional Memory (TM) is an emerging paradigm that promises to ease the development of parallel applications. Due to its inherently speculative nature, however, TM can suffer of performance degradations in presence of conflict intensive workloads.A key technique to tackle this issue consists in dynamically regulating the number of concurrent threads, which allows for selecting the concurrency level that best fits the intrinsic parallelism of specific applications. In this area, several self-tuning approaches have been proposed for Software-based implementations of TM (STM). In this paper we investigate the effectiveness of these techniques when applied to Hardware TM (HTM), a theme that is particularly relevant and timely given the recent integration of hardware supports for TM in next generation of mainstream Intel processors. Our study, conducted on Intel's implementation of HTM, identifies several issues associated with the employment of techniques originally conceived for STM. Motivated by these findings, we propose an innovative machine learning based technique explicitly designed to take into account peculiarities of HTM systems, and demonstrate its advantages, in terms of higher accuracy and shorter learning times, using the STAMP benchmark suite. © 2014 Springer International Publishing Switzerland.
Automatic tuning of the parallelism degree in hardware transactional memory / Rughetti, Diego; Romano, Paolo; Quaglia, Francesco; Ciciani, Bruno. - 8632:(2014), pp. 475-486. (Intervento presentato al convegno 20th International Conference on Parallel Processing, Euro-Par 2014 tenutosi a Porto; Portugal) [10.1007/978-3-319-09873-9-40].
Automatic tuning of the parallelism degree in hardware transactional memory
RUGHETTI, DIEGO
;ROMANO, PAOLO;QUAGLIA, Francesco;CICIANI, Bruno
2014
Abstract
Transactional Memory (TM) is an emerging paradigm that promises to ease the development of parallel applications. Due to its inherently speculative nature, however, TM can suffer of performance degradations in presence of conflict intensive workloads.A key technique to tackle this issue consists in dynamically regulating the number of concurrent threads, which allows for selecting the concurrency level that best fits the intrinsic parallelism of specific applications. In this area, several self-tuning approaches have been proposed for Software-based implementations of TM (STM). In this paper we investigate the effectiveness of these techniques when applied to Hardware TM (HTM), a theme that is particularly relevant and timely given the recent integration of hardware supports for TM in next generation of mainstream Intel processors. Our study, conducted on Intel's implementation of HTM, identifies several issues associated with the employment of techniques originally conceived for STM. Motivated by these findings, we propose an innovative machine learning based technique explicitly designed to take into account peculiarities of HTM systems, and demonstrate its advantages, in terms of higher accuracy and shorter learning times, using the STAMP benchmark suite. © 2014 Springer International Publishing Switzerland.File | Dimensione | Formato | |
---|---|---|---|
Rughetti_Automatic-Tuning_2014.pdf
solo gestori archivio
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
418.89 kB
Formato
Adobe PDF
|
418.89 kB | Adobe PDF | Contatta l'autore |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.