Tabu Search for Partitioning Dynamic Dataflow Programs

Abstract

An important challenge of dataflow programming is the problem of partitioning dataflow components onto a target architecture. A common objective function associated to this problem is to find the maximum data processing throughput. This NP-complete problem is very difficult to solve with high quality close-to-optimal solutions for the very large size of the design space and the possibly large variability of input data. This paper introduces four variants of the tabu search metaheuristic expressly developed for partitioning components of a dataflow program. The approach relies on the use of a simulation tool, capable of estimating the performance for any partitioning configuration exploiting a model of the target architecture and the profiling results. The partitioning solutions generated with tabu search are validated for consistency and high accuracy with experimental platform executions.