Abstract¶
Today’s supercomputers have hundreds of thousands of compute cores and this number is likely to grow. Many of today’s algorithms will have to be rethought to take advantage of such large systems. New algorithms must provide fine grained parallelism and excellent scalability. Python offers good support for numerical libraries and offers bindings to MPI that can be used to develop parallel algorithms for distributed memory machines.
PySMMP provides bindings to the protein simulation package SMMP. Combined with mpi4py, PySMMP can be used to perform parallel tempering simulations of small proteins on the supercomputers JUGENE and JuRoPA. In this paper, the performance of the Fortran implementation of parallel tempering in SMMP is compared with the Python implementation in PySMMP. Both codes use the same Fortran code for the calculation of the energy.
The performance of the implementations is comparable on both machines, but some challenges remain before the Python implementation can replace the Fortran implementation for all production runs.
