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Using Nesting to Push the Limits of Transactional Data Structure Libraries

Authors Gal Assa, Hagar Meir, Guy Golan-Gueta, Idit Keidar, Alexander Spiegelman



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Author Details

Gal Assa
  • Technion - Israel Institute of Technology, Haifa, Israel
Hagar Meir
  • IBM Research, Haifa, Israel
Guy Golan-Gueta
  • Independent researcher, Israel
Idit Keidar
  • Technion - Israel Institute of Technology, Haifa, Israel
Alexander Spiegelman
  • Novi Research, USA

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Gal Assa, Hagar Meir, Guy Golan-Gueta, Idit Keidar, and Alexander Spiegelman. Using Nesting to Push the Limits of Transactional Data Structure Libraries. In 25th International Conference on Principles of Distributed Systems (OPODIS 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 217, pp. 30:1-30:17, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.OPODIS.2021.30

Abstract

Transactional data structure libraries (TDSL) combine the ease-of-programming of transactions with the high performance and scalability of custom-tailored concurrent data structures. They can be very efficient thanks to their ability to exploit data structure semantics in order to reduce overhead, aborts, and wasted work compared to general-purpose software transactional memory. However, TDSLs were not previously used for complex use-cases involving long transactions and a variety of data structures. In this paper, we boost the performance and usability of a TDSL, towards allowing it to support complex applications. A key idea is nesting. Nested transactions create checkpoints within a longer transaction, so as to limit the scope of abort, without changing the semantics of the original transaction. We build a Java TDSL with built-in support for nested transactions over a number of data structures. We conduct a case study of a complex network intrusion detection system that invests a significant amount of work to process each packet. Our study shows that our library outperforms publicly available STMs twofold without nesting, and by up to 16x when nesting is used.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Concurrent algorithms
Keywords
  • Transactional Libraries
  • Nesting

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