Advanced Code Generation for High Performance Fortran

Abstract

For data-parallel languages such as High Performance Fortran to achieve wide acceptance, parallelizing compilers must be able to provide consistently high performance for a broad spectrum of scientific applications. Although compilation of regular data-parallel applications for message-passing systems have been widely studied, current state-of-the-art compilers implement only a small number of key optimizations, and the implementations generally focus on optimizing programs using a “case-based” approach. For these reasons, current compilers are unable to provide consistently high levels of performance. In this paper, we describe techniques developed in the Rice dHPF compiler to address key code generation challenges that arise in achieving high performance for regular applications on message-passing systems. We focus on techniques required to implement advanced optimizations and to achieve consistently high performance with existing optimizations. Many of the core communication analysis and code generation algorithms in dHPF are expressed in terms of abstract equations manipulating integer sets. This approach enables general and yet simple implementations of sophisticated optimizations, making it more practical to include a comprehensive set of optimizations in data-parallel compilers. It also enables the compiler to support much more aggressive computation partitioning algorithms than in previous compilers. We therefore believe this approach can provide higher and more consistent levels of performance than are available today.