Interactive Supercomputing’s Star-P Platform

The thesis of this extended abstract is simple. High productivity comes from high level infrastructures. To measure this, we introduce a methodology that goes beyond the tradition of timing software in serial and tuned parallel modes. We perform a classroom productivity study involving 29 students who have written a homework exercise in a low level language (MPI message passing) and a high level language (Star-P with MATLAB client). Our conclusions indicate what perhaps should be of little surprise: (1) the high level language is always far easier on the students than the low level language. (2) The early versions of the high level language perform inadequately compared to the tuned low level language, but later versions substantially catch up. Asymptotically, the analogy must hold that message passing is to high level language parallel programming as assembler is to high level environments such as MATLAB, Mathematica, Maple, or even Python. We follow the Kepner method that correctly realizes that traditional speedup numbers without some discussion of the human cost of reaching these numbers can fail to reflect the true human productivity cost of high performance computing. Traditional data compares low level message passing with serial computation. With the benefit of a high level language system in place, in our case Star-P running with MATLAB client, and with the benefit of a large data pool: 29 students, each running the same code ten times on three evolutions of the same platform, we can methodically demonstrate the productivity gains. To date we are not aware of any high level system as extensive and interoperable as Star-P, nor are we aware of an experiment of this kind performed with this volume of data