A Performance Study of Load Balancing Strategies for Approximate String Matching on an MPI Heterogeneous System Environment

In this paper, we present three parallel approximate string matching methods on a parallel architecture with heterogeneous workstations to gain supercomputer power at low cost. The first method is the static master-worker with uniform distribution strategy, the second one is the dynamic master-worker with allocation of subtexts and the third one is the dynamic master-worker with allocation of text pointers. Further, we propose a hybrid parallel method that combines the advantages of static and dynamic parallel methods in order to reduce the load imbalance and communication overhead. This hybrid method is based on the following optimal distribution strategy: the text collection is distributed proportional to workstation’s speed. We evaluated the performance of four methods with clusters 1, 2, 4, 6 and 8 heterogeneous workstations. The experimental results demonstrate that the dynamic allocation of text pointers and hybrid methods achieve better performance than the two original ones.