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Big Data, Simulations and HPC Convergence

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Big Data Benchmarking (WBDB 2015, WBDB 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10044))

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Abstract

Two major trends in computing systems are the growth in high performance computing (HPC) with in particular an international exascale initiative, and big data with an accompanying cloud infrastructure of dramatic and increasing size and sophistication. In this paper, we study an approach to convergence for software and applications/algorithms and show what hardware architectures it suggests. We start by dividing applications into data plus model components and classifying each component (whether from Big Data or Big Compute) in the same way. This leads to 64 properties divided into 4 views, which are Problem Architecture (Macro pattern); Execution Features (Micro patterns); Data Source and Style; and finally the Processing (runtime) View. We discuss convergence software built around HPC-ABDS (High Performance Computing enhanced Apache Big Data Stack) and show how one can merge Big Data and HPC (Big Simulation) concepts into a single stack and discuss appropriate hardware.

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Acknowledgments

This work was partially supported by NSF CIF21 DIBBS 1443054, NSF OCI 1149432 CAREER. and AFOSR FA9550-13-1-0225 awards. We thank Dennis Gannon for comments on an early draft.

Author information

Authors and Affiliations

  1. School of Informatics and Computing, Indiana University, Bloomington, Indiana, 47408, USA

    Geoffrey Fox, Judy Qiu, Saliya Ekanayake & Supun Kamburugamuve

  2. ECE, Rutgers University, Piscataway, New Jersey, 08854, USA

    Shantenu Jha

Authors
  1. Geoffrey Fox
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  2. Judy Qiu
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  3. Shantenu Jha
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  4. Saliya Ekanayake
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  5. Supun Kamburugamuve
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Corresponding author

Correspondence to Geoffrey Fox .

Editor information

Editors and Affiliations

  1. Technical University of Berlin, Berlin, Germany

    Tilmann Rabl

  2. Cisco Systems, Inc., San Jose, California, USA

    Raghunath Nambiar

  3. University of California at San Diego, La Jolla, California, USA

    Chaitanya Baru

  4. Ampool, Inc., Santa Clara, California, USA

    Milind Bhandarkar

  5. Oracle Corporation, Redwood Shores, California, USA

    Meikel Poess

  6. Indian Institute of Public Health, Hyderabad, India

    Saumyadipta Pyne

Appendix: Convergence Diamonds with 64 Facets

Appendix: Convergence Diamonds with 64 Facets

These are discussed in Sect. 2 and summarized in Fig. 1

Table 1. Convergence Diamonds and their Facets.
Full size table

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Fox, G., Qiu, J., Jha, S., Ekanayake, S., Kamburugamuve, S. (2016). Big Data, Simulations and HPC Convergence. In: Rabl, T., Nambiar, R., Baru, C., Bhandarkar, M., Poess, M., Pyne, S. (eds) Big Data Benchmarking. WBDB WBDB 2015 2015. Lecture Notes in Computer Science(), vol 10044. Springer, Cham. https://doi.org/10.1007/978-3-319-49748-8_1

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  • DOI: https://doi.org/10.1007/978-3-319-49748-8_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49747-1

  • Online ISBN: 978-3-319-49748-8

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