Recursive Task Generation for Scalable SDF Graph Execution on Multicore Processors

Research output: Chapter in Book/Conference proceedingConference contributionScientificpeer-review

Abstract

Dataflow modelling is a popular technique for describing parallel algorithms. Using dataflow, algorithm parallelism can be modelled and analysed efficiently at a high level of abstraction. However, challenges arise when translating dataflow semantics into executable code, mainly due to scheduling and synchronization overheads. Invoking task programming models in order to generate efficient code from dataflow representations has been proposed as a promising methodology to optimise the translation process.In this paper, we propose recursive task execution as an optimisation for the dataflow-based code generation process. Our approach is based on extracting synchronous dataflow graph information in order to reduce scheduling overheads and improve load balancing when executing task-based code on multicore processors. We use PREESM dataflow-based prototyping framework to implement and test our concept. Results show that our proposed optimisation enhances code scalability therefore enabling higher application throughput.

Original languageUndefined/Unknown
Title of host publicationProceedings 2020 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing PDP 2020
PublisherIEEE Computer Society Conference Publishing Services (CPS)
Pages
ISBN (Electronic)978-1-7281-6582-0
ISBN (Print)978-1-7281-6583-7
DOIs
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication
EventEuromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) - 2020 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)
Duration: 11 Mar 202013 Mar 2020

Conference

ConferenceEuromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)
Period11/03/2013/03/20

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