Causal memory: definitions, implementation, and programming


The abstraction of a shared memory is of growing importance in distributed computing systems. Traditional memory consistency ensures that all processes agree on a common order of all operations on memory. Unfortunately, providing these guarantees entails access latencies that prevent scaling to large systems. This paper weakens such guarantees by definingcausal memory, an abstraction that ensures that processes in a system agree on the relative ordering of operations that arecausally related. Because causal memory isweakly consistent, it admits more executions, and hence more concurrency, than either atomic or sequentially consistent memories. This paper provides a formal definition of causal memory and gives an implementation for message-passing systems. In addition, it describes a practical class of programs that, if developed for a strongly consistent memory, run correctly with causal memory.

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Corresponding author

Correspondence to Gil Neiger.

Additional information

Mustaque Ahamad is an Associate Professor in the College of Computing at the Georgia Institute of Technology. He received his M.S. and Ph.D. degrees in Computer Science from the State University of New York at Stony Brook in 1983 and 1985 respectively. His research interests include distributed operating systems, consistency of shared information in large scale distributed systems, and replicated data systems.

James E. Burns received the B.S. degree in mathematics from the California Institute of Technology, the M.B.I.S. degree from Georgia State University, and the M.S. and Ph.D. degrees in information and computer science from the Georgia Institute of Technology. He served on the faculty of Computer Science at Indiana University and the College of Computing at the Georgia Institute of Technology before joining Bellcore in 1993. He is currently a Member of Technical Staff in the Network Control Research Department, where he is studying the telephone control network with special interest in behavior when faults occur. He also has research interests in theoretical issues of distributed and parallel computing especially relating to problems of synchronization and fault tolerance.

This work was supported in part by the National Science Foundation under grants CCR-8619886, CCR-8909663, CCR-9106627, and CCR-9301454. Parts of this paper appeared in S. Toueg, P.G. Spirakis, and L. Kirousis, editors,Proceedings of the Fifth International Workshop on Distributed Algorithms, volume 579 ofLecture Notes on Computer Science, pages 9–30, Springer-Verlag, October 1991

The photograph of Professor J.E. Burns was published in Volume 8, No. 2, 1994 on page 59

This author's contributions were made while he was a graduate student at the Georgia Institute of Technology. No photograph and biographical information is available for P.W. Hutto

Gil Neiger was born on February 19, 1957 in New York, New York. In June 1979, he received an A.B. in Mathematics and Psycholinguistics from Brown University in Providence, Rhode Island. In February 1985, he spent two weeks picking cotton in Nicaragua in a brigade of international volunteers. In January 1986, he received an M.S. in Computer Science from Cornell University in Ithaca, New York and, in August 1988, he received a Ph.D. in Computer Science, also from Cornell University. On August 20, 1988, Dr. Neiger married Hilary Lombard in Lansing, New York. He is currently a Staff Software Engineer at Intel's Software Technology Lab in Hillsboro, Oregon. Dr. Neiger is a member of the editorial boards of theChicago Journal of Theoretical Computer Science and theJournal of Parallel and Distributed Computing.

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Ahamad, M., Neiger, G., Burns, J.E. et al. Causal memory: definitions, implementation, and programming. Distrib Comput 9, 37–49 (1995).

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Key Words

  • Memory consistency
  • Causal memory
  • Sequential consistency
  • Distributed shared memory