Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Online tuning of parallelism degree in parallel nesting transactional memory
KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS. INESC-ID/Instituto Superior Tecnico, Universidade de Lisboa, Portugal.
Show others and affiliations
2018 (English)In: Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 474-483, article id 8425201Conference paper, Published paper (Refereed)
Abstract [en]

This paper addresses the problem of self-Tuning the parallelism degree in Transactional Memory (TM) systems that support parallel nesting (PN-TM). This problem has been long investigated for TMs not supporting nesting, but, to the best of our knowledge, has never been studied in the context of PN-TMs. Indeed, the problem complexity is inherently exacerbated in PN-TMs, since these require to identify the optimal parallelism degree not only for top-level transactions but also for nested sub-Transactions. The increase of the problem dimensionality raises new challenges (e.g., increase of the search space, and proneness to suffer from local maxima), which are unsatisfactorily addressed by self-Tuning solutions conceived for flat nesting TMs. We tackle these challenges by proposing AUTOPN, an on-line self-Tuning system that combines model-driven learning techniques with localized search heuristics in order to pursue a twofold goal: i) enhance convergence speed by identifying the most promising region of the search space via model-driven techniques, while ii) increasing robustness against modeling errors, via a final local search phase aimed at refining the model's prediction. We further address the problem of tuning the duration of the monitoring windows used to collect feedback on the system's performance, by introducing novel, domain-specific, mechanisms aimed to strike an optimal trade-off between latency and accuracy of the self-Tuning process. We integrated AUTOPN with a state of the art PN-TM (JVSTM) and evaluated it via an extensive experimental study. The results of this study highlight that AUTOPN can achieve gains of up to 45× in terms of increased accuracy and 4× faster convergence speed, when compared with several on-line optimization techniques (gradient descent, simulated annealing and genetic algorithm), some of which were already successfully used in the context of flat nesting TMs.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 474-483, article id 8425201
Series
International Parallel and Distributed Processing Symposium IPDPS, ISSN 1530-2075
Keywords [en]
Adaptive System, Performance Tuning, Transactional Memory
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-234099DOI: 10.1109/IPDPS.2018.00057ISI: 000444710900047Scopus ID: 2-s2.0-85052197116ISBN: 9781538643686 (print)OAI: oai:DiVA.org:kth-234099DiVA, id: diva2:1245550
Conference
32nd IEEE International Parallel and Distributed Processing Symposium, IPDPS 2018, Vancouver, Canada, 21 May 2018 through 25 May 2018
Note

QC 20180905

Available from: 2018-09-05 Created: 2018-09-05 Last updated: 2018-10-09Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Zeng, Jingna
By organisation
Software and Computer systems, SCS
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 61 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf