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Massachusetts Institute of Technology

Labeling Schemes for Improving Cilksan Performance

Abstract

dc:description.abstract

While race detection algorithms like SP-bags have provably good theoretical properties, large overheads exist in practice, which urges the need for performance optimization. In this thesis, I propose labeling schemes as a method of circumventing many of the expensive operations in Cilksan, an implementation of the SP-bags algorithm. The proposed labeling schemes give strands of a parallel program labels during the execution of Cilksan, allowing Cilksan to shortcut the processing of certain memory accesses if the label comparison allows. I describe and prove correctness for two labeling schemes, the procedure labeling scheme and the prefix labeling scheme, implement both in Cilksan, and measure their performance. While the results show that the overhead of maintaining labels is too high in my implementation, the labeling schemes manage to circumvent many of the memory access operations, suggesting the merit of a more performant implementation of the same schemes.

Degree

thesis:*
Name thesis:degree_name
Master
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2024

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Holla, Satya
Advisor dc:contributor.advisor
  • Schardl, Tao B.

Rights

dc:rights
Statement dc:rights
  • In Copyright - Educational Use Permitted
  • Copyright retained by author(s)

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/157231
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/157231

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
related terms
citation

Holla, Satya. Labeling Schemes for Improving Cilksan Performance. Massachusetts Institute of Technology, 2024. https://hdl.handle.net/1721.1/157231