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ACCOLADES: A Scalable Workflow Framework for Large-Scale Simulation and Analyses of Automotive Engines

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High Performance Computing (ISC High Performance 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9137))

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Abstract

Analysis and optimization of simulation-generated data have myriads of scientific and industrial applications. Fuel consumption and emissions over the entire drive cycle of a large fleet of vehicles is an example of such an application and the focus of this study. Temporal variation of fuel consumption and emissions in an automotive engine are functions of over twenty variables. Determining relationships between fuel consumption or emissions and the dependent variables plays a crucial role in designing an automotive engine. This paper describes the development of ACCOLADES (Advanced Concurrent COmputing for LArge-scale Dynamic Engine Simulations), a scalable workflow framework that exploits the task parallelism inherent in such analyses by using large-scale computing. Excellent weak scaling is observed on 4,096 cores of both an Intel Sandy Bridge-based cluster and a Blue-Gene/Q supercomputer.

This material is based upon work supported by the U.S. Department of Energy, Office of Science, under Contract DE-AC02-06CH11357.

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Acknowledgments

We gratefully acknowledge the computing resources provided by the Argonne Leadership Computing Facility and the Laboratory Computing Resource Center at Argonne National Laboratory.

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Authors and Affiliations

  1. Argonne National Laboratory, Argonne, IL, 60439, USA

    Shashi M. Aithal & Stefan M. Wild

Authors
  1. Shashi M. Aithal
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  2. Stefan M. Wild
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Corresponding author

Correspondence to Shashi M. Aithal .

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Editors and Affiliations

  1. Deutsches Klimarechenzentrum (DKRZ), Hamburg, Germany

    Julian M. Kunkel

  2. Deutsches Klimarechenzentrum (DKRZ), Hamburg, Germany

    Thomas Ludwig

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Aithal, S.M., Wild, S.M. (2015). ACCOLADES: A Scalable Workflow Framework for Large-Scale Simulation and Analyses of Automotive Engines. In: Kunkel, J., Ludwig, T. (eds) High Performance Computing. ISC High Performance 2015. Lecture Notes in Computer Science(), vol 9137. Springer, Cham. https://doi.org/10.1007/978-3-319-20119-1_7

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  • DOI: https://doi.org/10.1007/978-3-319-20119-1_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20118-4

  • Online ISBN: 978-3-319-20119-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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