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A Rule-Based Quasi-Static Scheduling Approach for Static Islands in Dynamic Dataflow Graphs

By Joachim Falk, Christian Zebelein, Christian Haubelt and Jürgen Teich

Abstract

In this article, an efficient rule-based clustering algorithm for static dataflow subgraphs in a dynamic dataflow graph is presented. The clustered static dataflow actors are quasi-statically scheduled, insucha way that the global performance in terms of latency and throughput is improved compared to a dynamically scheduled execution, while avoiding the introduction of deadlocks as generated by naive static scheduling approaches. The presented clustering algorithm outperforms previously published approaches by a faster computation and more compact representation of the derived quasi-static schedule. This is achieved by a rulebased approach, which avoids an explicit enumeration of the state space. A formal proof of the correctness of the presented clustering approach is given. Experimental results show significant improvements in both, performance and code size, compared to a state-of-the-art clustering algorithm

Topics: Categories and Subject Descriptors, D.1.3 [Programming Techniques, Concurrent Programming— Parallel programming General Terms, Algorithms Additional Key Words and Phrases, Data flow analysis, actor-oriented design, clustering, scheduling ACM Reference Format
Year: 2013
OAI identifier: oai:CiteSeerX.psu:10.1.1.353.310
Provided by: CiteSeerX
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