Task-based execution of synchronous dataflow graphs for scalable multicore computing

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Publication Details

List of Authors: Georgios Georgakarakos, Sudeep Kanur, Johan Lilius, Karol Desnos
Publication year: 2017
Publisher: IEEE
Book title: Signal Processing Systems (SiPS), 2017 IEEE International Workshop on
ISBN: 978-1-5386-0447-2
eISBN: 978-1-5386-0446-5
ISSN: 2374-7390


Abstract

Dataflow models of computation have early on been acknowledged as an
attractive methodology to describe parallel algorithms, hence they have
become highly relevant for programming in the current multicore
processor era. While several frameworks provide tools to create dataflow
descriptions of algorithms, generating parallel code for programmable
processors is still sub-optimal due to the scheduling overheads and the
semantics gap when expressing parallelism with conventional programming
languages featuring threads. In this paper we propose an optimization of
the parallel code generation process by combining dataflow and task
programming models. We develop a task-based code generator for PREESM, a
dataflow-based prototyping framework, in order to deploy algorithms
described as synchronous dataflow graphs on multicore platforms.
Experimental performance comparison of our task generated code against
typical thread-based code shows that our approach removes significant
scheduling and synchronization overheads while maintaining similar (and
occasionally improving) application throughput.


Last updated on 2019-08-12 at 02:54