Storing a Sparse Table with 0(1) Worst Case Access Time
Journal of the ACM (JACM)
Distributing Hot-Spot Addressing in Large-Scale Multiprocessors
IEEE Transactions on Computers
An optimal parallel dictionary
SPAA '89 Proceedings of the first annual ACM symposium on Parallel algorithms and architectures
A complexity theory of efficient parallel algorithms
Theoretical Computer Science - Special issue: Fifteenth international colloquium on automata, languages and programming, Tampere, Finland, July 1988
How to distribute a dictionary in a complete network
STOC '90 Proceedings of the twenty-second annual ACM symposium on Theory of computing
Low contention load balancing on large-scale multiprocessors
SPAA '92 Proceedings of the fourth annual ACM symposium on Parallel algorithms and architectures
LogP: towards a realistic model of parallel computation
PPOPP '93 Proceedings of the fourth ACM SIGPLAN symposium on Principles and practice of parallel programming
Contention in shared memory algorithms
Journal of the ACM (JACM)
Journal of the ACM (JACM)
A New Universal Class of Hash Functions and Dynamic Hashing in Real Time
ICALP '90 Proceedings of the 17th International Colloquium on Automata, Languages and Programming
Parallelism in random access machines
STOC '78 Proceedings of the tenth annual ACM symposium on Theory of computing
Journal of Algorithms
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We consider the problem of minimizing contention in static (read-only) dictionary data structures, where contention is measured with respect to a fixed query distribution by the maximum expected number of probes to any given cell. The query distribution is known by the algorithm that constructs the data structure but not by the algorithm that queries it. Assume that the dictionary has n items. When all queries in the dictionary are equiprobable, and all queries not in the dictionary are equiprobable, we show how to construct a data structure in O(n) space where queries require O(1) probes and the contention is O(1/n). Asymptotically, all of these quantities are optimal. For arbitrary query distributions, we construct a data structure in O(n) space where each query requires O(logn/loglogn) probes and the contention is O(logn/(nloglogn)). The lack of knowledge of the query distribution by the query algorithm prevents perfect load leveling in this case: for a large class of algorithms, we present a lower bound, based on VC-dimension, that shows that for a wide range of data structure problems, achieving contention even within a polylogarithmic factor of optimal requires a cell-probe complexity of @W(loglogn).