@Article{segre02a,
  author =	 {Alberto Maria Segre and Sean Forman and Giovanni
                  Resta and Andrew Wildenberg},
  title =	 {Nagging: A scalable fault-tolerant paradigm for
                  distributed search},
  journal =	 {Artificial Intelligence},
  year =	 2002,
  volume =	 140,
  number =	 {1--1},
  pages =	 {71--106},
  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, alphabeta minimax game tree search,
                  and the Davis-Putnam algorithm.},
  url = 	 {http://jmvidal.cse.sc.edu/library/segre02a.pdf},
  cluster = 	 {12440606795010574236},
  doi = 	 {10.1016/S0004-3702(02)00228-X}
}