Applying dynamic balancing to improve the performance of MPI parallel genomics applications

Genomics applications are becoming more and more important in the field of bioinformatics, as they allow researchers to extract meaningful information from the huge amount of data generated by the new sequencing technologies. The analysis of these data is a very time consuming task and, therefore, the use of High Performance Computing (HPC) and parallel processing techniques is essential. Although the structure of these applications can be easily adapted to parallel systems by distributing the data to be processed among the available processors, load imbalance is a usual cause of performance degradation. In this paper we propose a dynamic load balancing method based on MPI RMA one-sided communications to minimize the synchronization among processes and the overhead due to communications while improving the workload balance. The strategy is applied, as a case study, to ParRADMeth, an MPI/OpenMP parallel application for the identification of Differential Methylated Regions (DMRs). Results show that the new version of the tool outperforms the previous one in all cases, achieving high performance and scalability. For example, our approach is up to 243 times faster than the sequential version and 1.74 times faster than the previous parallel version when processing a real dataset on a cluster with 8 nodes, each one with 32 CPU cores.