Token Coherence: Decoupling Performance and Correctness

Author
Hill, Mark D
Wood, David A
Contributor
Abstract

Many future shared-memory multiprocessor servers will both target commercial workloads and use highly-integrated "glueless" designs. Implementing low-latency cache coherence in these systems is difficult, because traditional approaches either add indirection for common cache-to-cache misses (directory protocols) or require a totally-ordered interconnect (traditional snooping protocols). Unfortunately, totally-ordered interconnects are difficult to implement in glueless designs. An ideal coherence protocol would avoid indirections and interconnect ordering; however, such an approach introduces numerous protocol races that are difficult to resolve. We propose a new coherence framework to enable such protocols by separating performance from correctness. A performance protocol can optimize for the common case (i.e., absence of races) and rely on the underlying correctness substrate to resolve races, provide safety, and prevent starvation. We call the combination Token Coherence, since it explicitly exchanges and counts tokens to control coherence permissions. This paper develops TokenB, a specific Token Coherence performance protocol that allows a glueless multiprocessor to both exploit a low-latency unordered interconnect (like directory protocols) and avoid indirection (like snooping protocols). Simulations using commercial workloads show that our new protocol can significantly outperform traditional snooping and directory protocols.

Advisor
Date of presentation
2003-06-01
Conference name
Departmental Papers (CIS)
Conference dates
2023-05-17T00:09:41.000
Conference location
Date Range for Data Collection (Start Date)
Date Range for Data Collection (End Date)
Digital Object Identifier
Series name and number
Volume number
Issue number
Publisher
Publisher DOI
Journal Issue
Comments
Copyright 2003 IEEE. Reprinted from Proceedings of the 30th Annual International Symposium on Computer Architecture (ISCA’03), pages 182-193. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. At the time of publication, author Milo M.K. Martin was affiliated with the University of Michigan. Currently, November 2006, he is a faculty member in the Department of Computer and Information Science ath the University of Pennsylvania.
Recommended citation
Collection