Parallel Sequence Alignment in Limited Space

Proceedings of the Twentieth International Conference on Machine Learning, 1995

Volume

Proceedings of the Twentieth International Conference on Machine Learning, 1995

Contents

Sequence comparison with affine gap costs is a problem that is readily parallelizable on simple single-instruction, multiple-data stream (SIMD) parallel processors using only constant space per processing element. Unfortunately, the twin problem of sequence alignment, finding the optimal character-by-character correspondence between two sequences, is more complicated. While the innovative O(n**2)-time and O(n)-space serial algorithm has been parallelized for multiple-instruction, multiple-data stream (MIMD) computers with only a communication-time slowdown, typically O(log n), it is not suitable for hardware-efficient SIMD parallel processors with only local communication. This paper proposes several methods of computing sequence alignments with limited memory per processing element. The algorithms are also well-suited to serial implementation. The simpler algorithms feature, for an arbitrary integer L, a factor of L slowdown in exchange for reducing space requirements from O(n) to O(n**(1/L)) per processing element. Taking this series to the limit, we describe an O(n log n) parallel time algorithm that requires O(log n) space per processing element on O(n) SIMD processing elements with only a mesh or linear interconnection network.