Unified locality-sensitive signatures for transactional memory

0Citations
Citations of this article
7Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Transactional memory systems coordinate the execution of concurrent transactions by committing non-conflicting ones. Transaction conflicts are detected by recording on-the-fly the memory locations issued by the threads. Some implementations use two per-thread Bloom filters (signatures), one for reads and another for writes, for that purpose. Signatures summarize sets of memory addresses accessed inside a transaction in bounded hardware. However, fixed-sized hardware introduces the address aliasing problem that results in false positives during the conflict checking process. It is known that the false positive rate increases with the size of the transactions, which has a strong negative impact in the performance of their concurrent execution. In a previous work, authors developed a technique with the aim of reducing the probability of false positives by exploiting spatial locality. In this paper we propose a new technique based on joining the two Bloom filters into a single one and partially sharing the hash function mappings for reads and writes. This unification technique is combined with the locality-sensitive one and it is proved that the false positive rate is further reduced. This paper proves that unified locality-sensitive signatures improve the execution performance of large concurrent transactions in most tested codes compared to separate signatures, without increasing significantly the required hardware area and with a small increment of power consumption. © 2011 Springer-Verlag.

Cite

CITATION STYLE

APA

Quislant, R., Gutierrez, E. D., Plata, O., & Zapata, E. L. (2011). Unified locality-sensitive signatures for transactional memory. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6852 LNCS, pp. 326–337). https://doi.org/10.1007/978-3-642-23400-2_30

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free