Streaming BDD Manipulation

ÐBinary Decision Diagrams (BDDs) are now commonly used for handling Boolean functions because of their excellent efficiency in terms of time and space. However, the conventional BDD manipulation algorithm is strongly based on the hash table technique, so it always encounters the memory overflow problem when handling large-scale BDD data. This paper proposes a new streaming BDD manipulation method that never causes memory overflow or swap out. This method allows us to handle very large-scale BDD stream data beyond the memory limitation. Our method can be characterized as follows: 1) it gives a continuous tradeoff curve between memory usage and stream data length, 2) valid solutions for a partial Boolean space can be obtained if we break the process before finishing, and 3) easily accelerated by pipelined multiprocessing. An experimental result demonstrates that we can succeed in finding a number of solutions to a SAT problem using commodity PC with a 64 MB memory, where as the conventional BDD manipulator would have required a 100GB memory. BDD manipulation has been considered as an intensively memory-consuming procedure, but now we can also utilize the hard disk and network resources as well. The method leads to a new approach to BDD manipulation.