An agent-based solution for dynamic multi-node wavefront balancing in biological sequence comparison

Many parallel and distributed strategies were created to reduce the execution time of bioinformatics algorithms. One well-known bioinformatics algorithm is the Smith–Waterman, that may be parallelized using the wavefront method. When the wavefront is distributed across many heterogeneous nodes, it must be balanced to create a synchronous data flow. This is a very challenging problem if the nodes have variable computational power. This paper presents an agent-based solution for parallel biological sequence comparison applications that use the multi-node wavefront method. In our approach, autonomous agents are able to identify unbalanced computations and dynamically rebalance the load among the nodes. Two strategies were developed to the balancer agent in order to identify if the computations are balanced, one using global information and other using only local information. The global strategy demands a huge amount of data transfers, incurring in more communication, whereas the local strategy can decide about the balancing status using only local information. The results show that the balancing gains of strategies are very close. Thus, the local strategy is preferred, since it can be implemented in real wavefront balancers with almost the same benefits as the global strategy.