A Framework for Auto-Parallelization and Code Generation: An Integrative Case Study with Legacy FORTRAN Codes

Abstract

GLAF, short for Grid-based Language and Auto-parallelization Framework, is a programming framework that seeks to democratize parallel programming by facilitating better productivity in parallel computing via an intuitive graphical programming interface (GPI) that automatically parallelizes and generates code in many languages. Originally, GLAF addressed program development from scratch via the GPI; but this unduly restricted GLAF's utility to creating new codes only. Thus, this paper extends GLAF by enabling program development from pre-existing kernels of interest, which can then be easily and transparently integrated into existing legacy codes. Specifically, we address the theoretical and practical limitations of integration and interoperability of auto-generated parallel code within existing FORTRAN codes; enhance GLAF to overcome these limitations; and present an integrative case study and evaluation of the enhanced GLAF via the implementation of important kernels in two NASA codes: (1) the Synoptic Surface & Atmospheric Radiation Budget (SARB), part of the Clouds and the Earth's Radiant Energy System (CERES), and (2) the Fully Unstructured Navier-Stokes (FUN3D) suite for computational fluid dynamics.