BAMSE: A balanced mapping space exploration algorithm for GALS-based manycore platforms

Abstract

We study the problem of mapping concurrent tasks of an application modeled as a data flow graph onto processors of a GALS-based manycore platform. We propose a mapping algorithm called BAMSE, which exploits the characteristics of streaming applications and the specifications of the target architecture to optimize the mapping solution. Different configuration parameters embedded into the algorithm enable one to strike a balance between scalability of the approach and the quality of generated solutions. Experiments with several real life applications show that our algorithm outperforms hand-optimized manual mappings up to 65% in terms of longest inter-processor communication link, and as high as 19% with respect to total length of the links, when the two criteria are used as primary and secondary optimization objectives, respectively. Additionally, our algorithm delivers superior mappings compared to ILP generated solutions after 10 days of solver runtime.