An MPI-enabled MapReduce framework for molecular dynamics simulation applications

Computational technologies have been extensively investigated to be applied into many application domains. Since the presence of Hadoop, an implementation of MapReduce framework, scientists have applied it to biological sciences, chemistry, medical sciences, and other areas to efficiently process huge data sets. Although Hadoop is fault-tolerant and processes data in parallel, it does not support MPI in computing. The Map/Reduce tasks in Hadoop have to be serial, which results in inefficient scientific computations wrapped in Map/Reduce tasks. In the real world, many applications require MPI techniques due to their nature. Molecular dynamics simulation is one of them. In our research, we proposed a MPI-enabled MapReduce framework for molecular dynamics simulation applications. The MPI module added into Hadoop enables Hadoop to monitor and manage the resources of a Hadoop cluster so that computations incurred in Map tasks can be performed over available resources on the cluster in a parallel manner. We evaluated the proposed framework against a molecular dynamics simulation algorithm, RESTMD, with the application software CHARMM. The experimental results showed that the MPI-enabled framework improves computing efficiency in molecular dynamics simulation.