Synthesis of Parallel Software from Heterogeneous Dataflow Models

Dataflow process networks (DPNs) are intrinsically data-driven, i.e., node actions are not synchronized among each other and may fire whenever sufficient input operands arrived at a node. While the general model of computation (MoC) of DPNs does not impose further restrictions, many different subcla...

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Bibliographic Details
Published in:SN computer science Vol. 3; no. 3; p. 249
Main Authors: Rafique, Omair, Schneider, Klaus
Format: Journal Article
Language:English
Published: Singapore Springer Nature Singapore 01.05.2022
Springer Nature B.V
Subjects:
Behavior
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Data Structures and Information Theory
Design
Embedded systems
Heterogeneity
Information Systems and Communication Service
Methods
Model-Driven Engineering and Software Development
Original Research
Pattern Recognition and Graphics
Semantics
Software
Software Engineering/Programming and Operating Systems
Synthesis
Vision
Heterogeneity
Automatic synthesis
Dataflow process networks
ISSN:2662-995X, 2661-8907
Online Access:Get full text
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Summary:Dataflow process networks (DPNs) are intrinsically data-driven, i.e., node actions are not synchronized among each other and may fire whenever sufficient input operands arrived at a node. While the general model of computation (MoC) of DPNs does not impose further restrictions, many different subclasses of DPNs representing different dataflow MoCs have been considered over time. These classes mainly differ in the kinds of behaviors of the processes. A DPN may be heterogeneous in that different processes in the network belong to different classes of DPNs. A heterogeneous DPN can therefore be effectively used to model and to implement different components of a system with different kinds of processes and, therefore, different dataflow MoCs. This paper presents a model-based design based on different dataflow MoCs including their heterogeneous combinations. In particular, it covers the automatic software synthesis of systems from DPN models. The main objective is to validate, evaluate and compare the artifacts exhibited by different dataflow MoCs at the implementation level of systems under the supervision of a common design tool. Moreover, this work also offers an efficient synthesis method that targets and exploits heterogeneity in DPNs by generating implementations based on the kinds of behaviors of the processes. The proposed synthesis method provides a tool chain including different specialized code generators for specific dataflow MoCs, and a runtime system that finally maps models using a combination of different dataflow MoCs on cross-vendor target hardware.
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ISSN:2662-995X
2661-8907
DOI:10.1007/s42979-022-01102-3