An evaluation of coarse grain dataflow code generation strategies

Abstract

Presents top-down and bottom-up methods for generating coarse grain dataflow or multithreaded code, and evaluates their effectiveness. The top-down technique generates clusters directly from the intermediate data dependence graph used for compiler optimizations. Bottom-up techniques coalesce fine-grain dataflow code into clusters. We measure the resulting number of clusters executed, cluster size, and number of inputs per cluster, for Livermore and Purdue benchmarks. The top-down method executes less clusters and instructions, but incurs a higher number of matches per cluster, which exemplifies the need for efficient matching of more than two inputs per cluster.