A Shared Memory Based Implementation of Needleman-Wunsch Algorithm Using Skewing Transformation

Abstract

Among various algorithms for protein and nucleotide alignment, Needleman-Wunsch algorithm is widely accepted as it can divide the problem into sub-problems. We present two parallel approaches of Needleman-Wunsch algorithm with single kernel and multi-kernel invocation using skewing transformation which is used for traversing and calculation of dynamic programming matrix. We also compare these with traditional CPU sequential approach which resulted in six fold performance improvement. Furthermore, we present same single kernel ideology on shared memory which resulted in two fold performance improvement over non-shared memory approach.