Nagging: a scalable fault-tolerant paradigm for distributed search

  • Authors:

  • Alberto Maria Segre;Sean Forman;Giovanni Resta;Andrew Wildenberg

  • Affiliations:

  • Department of Management Sciences, The University of Iowa, Iowa City, IA;Mathematics and Computer Science Department, Saint Joseph's University, Philadelphia, PA;Instituto di Informatica e Telematica, Consiglio Nazionale delle Ricerche, I-56124 Pisa, Italy;Computer Science Department, SUNY Stony Brook, Stony Brook, NY

  • Venue:
  • Artificial Intelligence
  • Year:
  • 2002

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Abstract

This paper describes nagging, a technique for parallelizing search in a heterogeneous distributed computing environment. Nagging exploits the speedup anomaly often observed when parallelizing problems by playing multiple reformulations of the problem or portions of the problem against each other. Nagging is both fault tolerant and robust to long message latencies. In this paper, we show how nagging can be used to parallelize several different algorithms drawn from the artificial intelligence literature, and describe how nagging can be combined with partitioning, the more traditional search parallelization strategy. We present a theoretical analysis of the advantage of nagging with respect to partitioning, and give empirical results obtained on a cluster of 64 processors that demonstrate nagging's effectiveness and scalability as applied to A* search, αβ minimax game tree search, and the Davis-Putnam algorithm.