Distributed-memory systems (DMMPs) are powerful tools for solving large-scale scientific and engineering problems. However, these machines are difficult to program since the data must be distributed across the processors and message-passing operations must be inserted for communicating non-local data. In this paper, we discuss the automatic parallelization of Fortran programs for DMMPs, based on the programming paradigms associated with Vienna Fortran and High Performance Fortran. After introducing the state of the art, as represented by currently implemented systems, we will identify a number of limitations of this technology. In addition to insufficient functionality for handling many real applications, a major deficiency of current systems is the lack of intelligence in selecting good transformation strategies. We argue that a knowledgebased approach to compiling will contribute to more powerful and intelligent automatic parallelization systems in the future.
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