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Third, in both cases the order in which nodes are generated in the tree can have a large effect on efficiency. In IDA * it determines the time to search the final or goal threshold, while in minimax search it determines the efficiency of alpha-beta pruning. Finally, iterative-deepening, or successive searches to greater depths, plays an important role in both algorithms. 2 Parallel Heuristic Search The main limitation of search is its computational complexity. Parallel processing can significantly increase the number of nodes evaluated in a given amount of time.

The communication overhead for the parallel versions is always less than 5%, leading to an almost linear relation between the number of processors and number of node evaluations per unit time. This is expected due to the near perfect speedup obtained for brute-force search. This also allows us to estimate speedup by counting the total number of node evaluations in serial, and dividing that by the number of evaluations per processor performed in parallel. On 32 processors, the parallel alpha-beta algorithm evaluates about 3 times as many leaf nodes as the serial version.

If a process is given a terminal node, it returns the value of that node and the processors it was assigned immediately to its parent, and terminates. As soon as the first child process completes the search of its subtree, it terminates, sending a message to its parent with its results, plus the set of processors it was assigned. , depending on the application. This message wakes up the parent process to reallocate the freed processors to the remaining children, and possibly send them new values for alpha or beta, for example.

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