In this paper, we design a multiprocessor strategy that exploits the computational characteristics of the algorithms used for biological sequence comparison proposed in the literature. We employ divisible load theory (DLT) that is suitable for handling large scale processing on network based systems. For the first time in the domain of DLT, the problem of aligning biological sequences is attempted. The objective is to minimize the total processing time of the alignment process. In designing our strategy, DLT facilitates a clever partitioning of the entire computation process involved in such a way that the overall time consumed for aligning the sequences is a minimum. The partitioning takes into account the computation speeds of the nodes and the underlying communication network. Since this is a real-life application, the post-processing phase becomes important, and hence we consider propagating the results back in order to generate an exact alignment. We consider several cases in our analysis such as deriving closed-form solutions for the processing time for heterogeneous, homogeneous, and networks with slow links. Further, we attempt to employ a multiinstallment strategy to distribute the tasks such that a higher degree of parallelism can be achieved. For slow networks, our strategy recommends near-optimal solutions. We derive an important condition to identify such cases and propose two heuristic strategies. Also, our strategy can be extended for multisequence alignment by utilizing a clustering strategy such as the Berger-Munson algorithm proposed in the literature. Finally, we use real-life DNA samples of house mouse mitochondrion (Mus Musculus Mitochondrion, NC_001569) consisting of 16,295 residues and the DNA of human mitochondrion (Homo Sapiens Mitochondrion, NC_001807) consisting of 16,571 residues, obtainable from the GenBank, in our rigorous simulation experiments to illustrate all the theoretical findings.

The problem of distributing and processing a divisible load in a heterogeneous linear net-work of processors with arbitrary processors release times is considered. A divisible load is very large in size and has computationally intensive CPU requirements. Further, it has the property that the load can be partitioned arbitrarily into any number of portions and can be scheduled onto processors independently for computation. The load is assumed to arrive at one of the farthest end processors, referred to as boundary processors, for processing. The processors in the network are assumed to have non-zero release times, i.e., the time instants from which the processors are available for processing the divisible load. Our objective is to design a load distribution strategy by taking into account the release times of the processors in such a way that the entire processing time of the load is a minimum. We consider two generic cases in which all processors have identical release times and when all processors have arbitrary release times. We adopt both the single and multi-installment strategies pro-posed in the divisible load scheduling literature in our design of load distribution strategies, wherever necessary, to achieve a minimum processing time. Finally, when optimal strate-gies cannot be realized, we propose two heuristic strategies, one for the identical case, and the other for non-identical release times case, respectively. Several conditions are derived to determine whether or not optimal load distribution exists and illustrative examples are provided for the ease of understanding.