Concurrent operations on priority queues

Abstract

Among the fastest priority queue implementations, skew heaps have properties that make them particularly suited to concurrent manipulation of shared queues. A concurrent version of the top down implementation of skew heaps can be produced from previously published sequential versions using almost mechanical transformations. This implementation requires O(log n) time to enqueue or dequeue an item, but it allows new operations to begin after only O(1) time on a MIMD machine. Thus, there is potential for significant concurrency when multiple processes share a queue. Applications to problems in graph theory and simulation are discussed in this article.

References

1. 1 Bayer, R. and Schkolnick, M. Concurrency of operations on B-trees. Acta Informatica 9, I (1977), 1-21.
2. 2 BenAri, M. Principles of Concurrent Programming. Prentice Hall, Englewood Cliffs, N.J., 1982.
3. 3 Biswas, J. and Browne, J.C. Simultaneous update of priority structures. In Proceedings of the 1987 International Conference on Parallel Processing. S.K. Sahni, Ed. (University Park, Pa., Aug. 17-21, 1987), pp. 124-131.
4. 4 Brown, M.R. The Analysis of a Practical and Nearly Optimal Priority Queue. Tech. Rep. STAN-CS-77-600, Computer Science Dept., Stanford Univ., 1977. (An abridged version appears as Implementation and analysis of bionomial queue algorithms. SIAM J. Computing 7, 3 (Aug. 1978) 298-319.)
5. 5 Ford, R.F. and Calhoun, J. Concurrency control mechanisms and the serializability of concurrent tree algorithms. In Proceedings of the 3rd ACM SIGACT-SIGMOD Symposium on Principles of Database Systems (Waterloo, Ontario, Apr. 1984).
6. 6 Jones, D.W. An empirical comparison of priority-queue and eventset implementations. Commun. ACM 29, 4 (Apr. 1986), 300-311.
7. 7 Jones, D.W. Concurrent simulation: An alternative to distributed simulation. In Proceedings of the 1986 Winter Simulation Conference. J. Wilson, J. Henriksen, S. Roberts, Eds. (Washington, D.C., Dec. 8-10, 1986). pp. 417-423.
8. 8 Kingston, J.H. Analysis of tree algorithms for the simulation event list. Acta Informatica 22, I (Apr. 1985), 15-33.
9. 9 Knuth, D.E. The Art of Computer Programming. Volume III, Sorting and Searching. Addison-Wesiey, Reading, Mass., 1973, 150-153.
10. 10 Lehman, P. and Yao, S. Efficient locking for concurrent operations on B-trees. ACM Trans. Database Syst. 6, 4 (Dec. 1981), 650-670.
11. 11 Nix, R. An Evaluation of Pagodas. Tech. Rep. 164, Computer Science Dept., Yale Univ., Conn. (no date).
12. 12 Quinn, M.J. and Deo, N. Parallel Graph Algorithms. Comp. Surv. 16, 3 (Sept. 1984) 319-348.
13. 13 Sleator, D.D. and Tarjan. R.E. Self adjusting binary trees. In Proceedings of the 15th ACM Symposium on Theory of Computing (Boston, Mass., April 25-27, 1983}. ACM, New York, 235-246.
14. 14 Sleator, D.D. and Tarjan, R.E. Self adjusting heaps. SIAM J. Comput. 15, 1 (Feb. 1986), 52-59.
15. 15 Vuillemin. J. A data structure for manipulating priority queues. Commun. ACM 21, 4 (Apr. 1978), 309-315.

Index Terms

• Computer systems organization

  • Architectures

    • Parallel architectures

      • Multiple instruction, multiple data

• General and reference

  • Cross-computing tools and techniques

    • Performance

• Mathematics of computing

  • Probability and statistics

    • Statistical paradigms

      • Queueing theory

• Software and its engineering

  • Software organization and properties

    • Contextual software domains

      • Operating systems

        • Process management

          • Concurrency control

• Theory of computation

  • Design and analysis of algorithms

    • Approximation algorithms analysis

      • Scheduling algorithms

    • Online algorithms

      • Online learning algorithms

        • Scheduling algorithms

  • Theory and algorithms for application domains

    • Machine learning theory

      • Reinforcement learning

        • Sequential decision making