Reduced Function Set Abstraction (RFSA)

In this paper we present RFSA, a Reduced Function Set Abstraction based on an existing parallel programming model as a potential programming model for Data-Intensive Supercomputing (DISC) applications. Our RFSA uses either a select or a merge procedure to reduce the number of function calls in existing programming models. In the current version, we build RFSA on top of Message Passing Interface (MPI/MPI-IO) Programming Model. MPI/MPI-IO provides high performance compute and I/O function calls to the scientists/engineers writing parallel programs; who are required to use the most appropriate optimization of a specific function, hence limits the programmer productivity. Therefore we propose a set of reduced functions with an automatic selection algorithm to decide what specific MPI/MPI-IO function to use. We implement a selection algorithm for I/O functions like read, write, etc and also merge a set of functions for datatypes and file views. By running different parallel I/O benchmarks on both medium-scale clusters and NERSC supercomputers, we show that RFSA reduces the number of function calls without any performance penalties.