Performance Estimation Based Multicriteria Partitioning Approach for Dynamic Dataflow Programs

Małgorzata Michalska,Marco Mattavelli,Nicolas Zufferey

doi:10.1155/2016/8536432

Abstract

The problem of partitioning a dataflow program onto a target architecture is a difficult challenge for any application design. In general, since the problem is NP-complete, it consists of looking for high quality solutions in terms of maximizing the achievable data throughput. The difficulty is given by the exploration of the design space which results in being extremely large for parallel platforms. The paper describes a heuristic partitioning methodology applicable to dynamic dataflow programs. The methodology is based on two elements: an execution model of the dynamic dataflow program which is used as estimation of the performance for the exploration of the large design space and several partitioning algorithms competing to lead to specific high quality solutions. Experimental results are validated with executions on a virtual platform.

Highlights

An interesting alternative to the classical sequential programming methods for signal processing system implementations is the approach based on dataflow programming
The dynamic scheduling policy used in the experiments is nonpreemptive, which involves that the same actor is continuously scheduled, as long as its firing conditions are fulfilled
Complementary units in this workflow are the profiling of the Transport Triggered Architecture (TTA) architecture and a TTA cycle-accurate simulator [45] that allows a verification of the estimated results in terms of a real execution time obtained on the platform

Summary

Introduction

An interesting alternative to the classical sequential programming methods for signal processing system implementations is the approach based on dataflow programming. Dataflow programs are characterized by a high analyzability and platform independence and by providing an explicit exposition of the potential parallelism. For these reasons, they can be used for exploring a variety of parallel implementation options and to provide an extensive and systematic implementation analysis [1, 2]. Dataflow programs are in general structured as, possibly, hierarchical networks of communicating computational kernels, called actors. A DPN network evolves as a sequence of discrete steps (called firings) corresponding to the executions of actions that may consume and/or produce a finite number of tokens and modify the internal actor state.

Methods

Results

Discussion

Conclusion