Paper Info
Title
Nesting and composition in transactional data structure libraries.
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 work, we boost the performance and usability of a TDSL, 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 nesting in 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 TL2 twofold without nesting, and by up to 16x when nesting is used. Finally, we discuss cross-library nesting, namely dynamic composition of transactions from multiple libraries.
Year
DOI
Venue
2020
10.1145/3332466.3374514
PPoPP '20: 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming San Diego California February, 2020
Field
DocType
ISBN
Computer science,Transaction data,Distributed computing
Conference
978-1-4503-6818-6
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Gal Assa100.34
Hagar Meir232.49
Guy Golan-Gueta316011.80
Idit Keidar41892155.01
Alexander Spiegelman591.57