Load Scheduling Strategies for Parallel DNA Sequencing Applications

This paper studies a divisible load scheduling strategy with near-optimal processing time leveraging the computational characteristics of parallel DNA sequence alignment algorithms, specifically, the Needleman-Wunsch algorithm. Following the divisible load scheduling theory, an efficient load scheduling strategy is designed in large-scale networks so that the overall processing time of the sequencing tasks is minimized. In this study, the load distribution depends on the length of the sequence and number of processors in the network. Since we consider both of computation and communication overheads, the total processing time is also affected by communication link speed. Several cases have been considered in the study by varying the sequences, communication and computation speeds, and number of processors. Through simulation and numerical analysis, this study demonstrates that for a constant sequence length as the numbers of processors increase in the network the processing time for the job decreases and minimum overall processing time is achieved.