A complexity-based classification for multiprocessor synchronization

Faith Ellen; Rati Gelashvili; Nir Shavit; Leqi Zhu

doi:10.1007/s00446-019-00361-3

A complexity-based classification for multiprocessor synchronization

Faith Ellen, Rati Gelashvili^*, Nir Shavit, Leqi Zhu

^*Corresponding author for this work

School of Computer Science

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

For many years, Herlihy’s elegant computability-based Consensus Hierarchy has been our best explanation of the relative power of various objects. Since real multiprocessors allow the different instructions they support to be applied to any memory location, it makes sense to consider combining the instructions supported by different objects, rather than considering collections of different objects. Surprisingly, this causes Herlihy’s computability-based hierarchy to collapse. In this paper, we suggest an alternative: a complexity-based classification of the relative power of sets of multiprocessor synchronization instructions, captured by the minimum number of memory locations of unbounded size that are needed to solve obstruction-free consensus when using different sets of instructions.

Original language	English
Pages (from-to)	125-144
Number of pages	20
Journal	Distributed Computing
Volume	33
Issue number	2
DOIs	https://doi.org/10.1007/s00446-019-00361-3
State	Published - 1 Apr 2020

Funding

Funders	Funder number
Intel Corporation
University of Toronto
Oracle
Massachusetts Institute of Technology
Sun Microsystems
National Science Foundation	CCF-1301926, CCF-1217921, IIS-1447786, 1447786
Natural Sciences and Engineering Research Council of Canada	RGPIN-2015-05080
U.S. Department of Energy	ER26116/DE-SC0008923

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10.1007/s00446-019-00361-3

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abstract = "For many years, Herlihy{\textquoteright}s elegant computability-based Consensus Hierarchy has been our best explanation of the relative power of various objects. Since real multiprocessors allow the different instructions they support to be applied to any memory location, it makes sense to consider combining the instructions supported by different objects, rather than considering collections of different objects. Surprisingly, this causes Herlihy{\textquoteright}s computability-based hierarchy to collapse. In this paper, we suggest an alternative: a complexity-based classification of the relative power of sets of multiprocessor synchronization instructions, captured by the minimum number of memory locations of unbounded size that are needed to solve obstruction-free consensus when using different sets of instructions.",

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A complexity-based classification for multiprocessor synchronization

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