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Numerical Representation of Directed Acyclic Graphs for Efficient Dataflow Embedded Resource Allocation

Published: 08 October 2019 Publication History

Abstract

Stream processing applications running on Heterogeneous Multi-Processor Systems on Chips (HMPSoCs) require efficient resource allocation and management, both at compile-time and at runtime. To cope with modern adaptive applications whose behavior can not be exhaustively predicted at compile-time, runtime managers must be able to take resource allocation decisions on-the-fly, with a minimum overhead on application performance.
Resource allocation algorithms often rely on an internal modeling of an application. Directed Acyclic Graph (DAGs) are the most commonly used models for capturing control and data dependencies between tasks. DAGs are notably often used as an intermediate representation for deploying applications modeled with a dataflow Model of Computation (MoC) on HMPSoCs. Building such intermediate representation at runtime for massively parallel applications is costly both in terms of computation and memory overhead.
In this paper, an intermediate representation of DAGs for resource allocation is presented. This new representation shows improved performance for run-time analysis of dataflow graphs with less overhead in both computation time and memory footprint. The performances of the proposed representation are evaluated on a set of computer vision and machine learning applications.

References

[1]
Matin Abadi et al. 2016. TensorFlow: A system for large-scale machine learning. 265--283.
[2]
Florian Arrestier, Karol Desnos, Maxime Pelcat, Julien Heulot, Eduardo Juarez, and Daniel Menard. 2018. Delays and states in dataflow models of computation. In Proceedings of the 18th International Conference on Embedded Computer Systems Architectures, Modeling, and Simulation - SAMOS’18. ACM Press, Pythagorion, Greece, 47--54.
[3]
Cédric Augonnet, Samuel Thibault, Raymond Namyst, and Pierre-André Wacrenier. 2009. StarPU: A unified platform for task scheduling on heterogeneous multicore architectures. (2009), 16.
[4]
Bishnupriya Bhattacharya and Shuvra S. Bhattacharyya. 2001. Parameterized dataflow modeling for DSP systems. IEEE Transactions on Signal Processing 49, 10 (2001), 2408--2421.
[5]
Shuvra S. Bhattacharyya, Edward A. Lee, and Praveen K. Murphy. 1996. Software Synthesis from Dataflow Graphs. Kluwer Academic Publishers, Norwell, MA, USA.
[6]
G. Bilsen, M. Engels, R. Lauwereins, and J. Peperstraete. 1996. Cycle-static dataflow. IEEE Transactions on Signal Processing 44, 2 (Feb. 1996), 397--408.
[7]
Morteza Damavandpeyma, Sander Stuijk, Twan Basten, Marc Geilen, and Henk Corporaal. 2013. Schedule-extended synchronous dataflow graphs. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 32, 10 (Oct. 2013), 1495--1508.
[8]
Hamza Deroui, Karol Desnos, Jean-François Nezan, and Alix Munier-Kordon. 2017. Relaxed subgraph execution model for the throughput evaluation of IBSDF graphs. In International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS).
[9]
Karol Desnos, Maxime Pelcat, Jean-François Nezan, Shuvra S. Bhattacharyya, and Slaheddine Aridhi. 2013. Pimm: Parameterized and interfaced dataflow meta-model for mpsocs runtime reconfiguration. In Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS XIII), 2013 International Conference on. IEEE, 41--48.
[10]
Pascal Fradet, Alain Girault, and Peter Poplavko. 2012. SPDF: A schedulable parametric data-flow MoC. In Proceedings of the Conference on Design, Automation and Test in Europe. EDA Consortium, 769--774.
[11]
Thierry Gautier, Joao VF Lima, Nicolas Maillard, and Bruno Raffin. 2013. Xkaapi: A runtime system for data-flow task programming on heterogeneous architectures. In Parallel 8 Distributed Processing (IPDPS), 2013 IEEE 27th International Symposium on. IEEE, 1299--1308.
[12]
Kronos Group. 2013. The OpenVX API for hardware acceleration. In http://www.khronos.org/openvx.
[13]
Julien Heulot, Maxime Pelcat, Karol Desnos, Jean-François Nezan, and Slaheddine Aridhi. 2014. Spider: A synchronous parameterized and interfaced dataflow-based rtos for multicore dsps. In Education and Research Conference (EDERC), 2014 6th European Embedded Design in. IEEE, 167--171.
[14]
J. Keinert, C. Haubelt, and J. Teich. 2006. Modeling and analysis of windowed synchronous algorithms. In 2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, Vol. 3. IEEE, Toulouse, France, III--892--III--895.
[15]
Y.-K. Kwok. 1997. High-performance algorithms of compile-time scheduling of parallel processors. Ph.D. Dissertation. Hong Kong University of Science and Technology. Advisor(s) Ahmad, Ishfaq.
[16]
Edward A. Lee and David G. Messerschmitt. 1987. Synchronous data flow. Proc. IEEE 75, 9 (1987), 1235--1245.
[17]
Edward A. Lee and Thomas M. Parks. 1995. Dataflow process networks. Proc. IEEE 83, 5 (1995), 773--801.
[18]
Maxime Pelcat, Karol Desnos, Julien Heulot, Clément Guy, Jean-François Nezan, and Slaheddine Aridhi. 2014. Preesm: A dataflow-based rapid prototyping framework for simplifying multicore dsp programming. In Education and Research Conference (EDERC), 2014 6th European Embedded Design in. IEEE, 36--40.
[19]
Jonathan Piat, Shuvra S. Bhattacharyya, and Mickaël Raulet. 2009. Interface-based hierarchy for synchronous data-flow graphs. In Signal Processing Systems, 2009. SiPS 2009. IEEE Workshop on. IEEE, 145--150.
[20]
José Luis Pino, Shuvra S. Bhattacharyya, and Edward A. Lee. 1995. A Hierarchical Multiprocessor Scheduling Framework for Synchronous Dataflow Graphs. Electronics Research Laboratory, College of Engineering, University of California.
[21]
Sebastian Ritz, Matthias Pankert, V. Zivojinovic, and Heinrich Meyr. 1993. Optimum vectorization of scalable synchronous dataflow graphs. In Application-Specific Array Processors, 1993. Proceedings., International Conference on. IEEE, 285--296.
[22]
Jiahao Wu, Timothy Blattner, Walid Keyrouz, and Shuvra S. Bhattacharyya. 2018. A design tool for high performance image processing on multicore platforms. In 2018 Design, Automation 8 Test in Europe Conference 8 Exhibition (DATE). IEEE, Dresden, Germany, 1304--1309.
[23]
George F. Zaki, William Plishker, Shuvra S. Bhattacharyya, and Frank Fruth. 2012. Partial expansion graphs: Exposing parallelism and dynamic scheduling opportunities for DSP applications. In 2012 IEEE 23rd International Conference on Application-Specific Systems, Architectures and Processors. IEEE, Delft, Netherlands, 86--93.
[24]
George F. Zaki, William Plishker, Shuvra S. Bhattacharyya, and Frank Fruth. 2017. Implementation, scheduling, and adaptation of partial expansion graphs on multicore platforms. Journal of Signal Processing Systems 87, 1 (April 2017), 107--125.

Cited By

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  • (2023)A Note on Grid-Type Directed Acyclic Graph for Important Property of Resource Allocation ProblemProceedings of the 6th International Conference on Combinatorics, Graph Theory, and Network Topology (ICCGANT 2022)10.2991/978-94-6463-138-8_15(170-176)Online publication date: 26-Apr-2023
  • (2023)Dataflow Models of Computation for Programming Heterogeneous MulticoresHandbook of Computer Architecture10.1007/978-981-15-6401-7_45-2(1-40)Online publication date: 28-Sep-2023
  • (2022)Dataflow Models of Computation for Programming Heterogeneous MulticoresHandbook of Computer Architecture10.1007/978-981-15-6401-7_45-1(1-40)Online publication date: 28-Jan-2022

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  1. Numerical Representation of Directed Acyclic Graphs for Efficient Dataflow Embedded Resource Allocation

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      Published In

      cover image ACM Transactions on Embedded Computing Systems
      ACM Transactions on Embedded Computing Systems  Volume 18, Issue 5s
      Special Issue ESWEEK 2019, CASES 2019, CODES+ISSS 2019 and EMSOFT 2019
      October 2019
      1423 pages
      ISSN:1539-9087
      EISSN:1558-3465
      DOI:10.1145/3365919
      Issue’s Table of Contents
      Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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      Publication History

      Published: 08 October 2019
      Accepted: 01 July 2019
      Revised: 01 June 2019
      Received: 01 April 2019
      Published in TECS Volume 18, Issue 5s

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      Author Tags

      1. Dataflow
      2. numerical modeling
      3. resource allocation

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      • (2023)A Note on Grid-Type Directed Acyclic Graph for Important Property of Resource Allocation ProblemProceedings of the 6th International Conference on Combinatorics, Graph Theory, and Network Topology (ICCGANT 2022)10.2991/978-94-6463-138-8_15(170-176)Online publication date: 26-Apr-2023
      • (2023)Dataflow Models of Computation for Programming Heterogeneous MulticoresHandbook of Computer Architecture10.1007/978-981-15-6401-7_45-2(1-40)Online publication date: 28-Sep-2023
      • (2022)Dataflow Models of Computation for Programming Heterogeneous MulticoresHandbook of Computer Architecture10.1007/978-981-15-6401-7_45-1(1-40)Online publication date: 28-Jan-2022

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