Exploiting fine-grain parallelism on dataflow architectures

Although dataflow computers have many attractive features, skepticism exists concerning their efficiency in handling arrays (vectors) in high performance scientific computation. This paper outlines an efficient implementation scheme for arrays in applicative languages (such as VAL and SISAL) based on the principles of dataflow software pipelining. It illustrates how the fine-grain parallelism of dataflow approach can effectively handle large amount of data structured in applicative array operations. This is done through dataflow software pipelining between pairs of code blocks which act as producer-consumer of array values. To make effective use of the pipelined code mapping scheme, a compiler needs information concerning the overall program structure as well as the structure of each code block. An applicative language provides a basis for such analysis.

The program transformation techniques described here are developed primarily for the computationally intensive part of a scientific numerical program, which is usually formed by one or a few clusters of acyclic connected code blocks. Each code block defines an array value from several input arrays. We outline how mapping decisions of arrays can be based on a global analysis of attributes of the code blocks. We emphasize the role of overall program structure and the strategy of global optimization of the machine code structure. The structure of a proposed dataflow compiler based on the scheme described in this paper is outlined.