Interlanguage parallel scripting for distributed-memory scientific computing

Scripting languages such as Python and R have been widely adopted as tools for the development of scientific software because of the expressiveness of the languages and their available libraries. However, deploying scripted applications on large-scale parallel computer systems such as the IBM Blue Gene/Q or Cray XE6 is a challenge because of issues including operating system limitations, interoperability challenges, and parallel filesystem overheads due to the small file system accesses common in scripted approaches. We present a new approach to these problems in which the Swift scripting system is used to integrate high-level scripts written in Python, R, and Tcl with native code developed in C, C++, and Fortran, by linking Swift to the library interfaces to the script interpreters. We present a technique to efficiently launch scripted applications on supercomputers, and we demonstrate high performance, such as invoking 14M Python interpreters per second on Blue Waters.