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SLOPE: Structural Locality-Aware Programming Model for Composing Array Data Analysis

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High Performance Computing (ISC High Performance 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11501))

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Abstract

MapReduce brought on the Big Data revolution. However, its impact on scientific data analyses has been limited because of fundamental limitations in its data and programming models. Scientific data is typically stored as multidimensional arrays, while MapReduce is based on key-value (KV) pairs. Applying MapReduce to analyze array-based scientific data requires a conversion of arrays to KV pairs. This conversion incurs a large storage overhead and loses structural information embedded in the array. For example, analysis operations, such as convolution, are defined on the neighbors of an array element. Accessing these neighbors is straightforward using array indexes, but requires complex and expensive operations like self-join in the KV data model. In this work, we introduce a novel ‘structural locality’-aware programming model (SLOPE) to compose data analysis directly on multidimensional arrays. We also develop a parallel execution engine for SLOPE to transparently partition the data, to cache intermediate results, to support in-place modification, and to recover from failures. Our evaluations with real applications show that SLOPE is over ninety thousand times faster than Apache Spark and is \(38\%\) faster than TensorFlow.

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Notes

  1. 1.

    https://bitbucket.org/arrayudf/.

  2. 2.

    https://www.nersc.gov/.

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Acknowledgment

This effort was supported by the U.S. Department of Energy (DOE), Office of Science, Office of Advanced Scientific Computing Research under contract number DE-AC02-05CH11231 (program manager Dr. Laura Biven). This research used resources of the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility.

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Authors and Affiliations

  1. Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA, 94720, USA

    Bin Dong, Kesheng Wu, Suren Byna & Houjun Tang

Authors
  1. Bin Dong
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  2. Kesheng Wu
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  3. Suren Byna
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  4. Houjun Tang
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Corresponding author

Correspondence to Bin Dong .

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Editors and Affiliations

  1. University of Edinburgh, Edinburgh, UK

    Michèle Weiland

  2. Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany

    Guido Juckeland

  3. Technical University of Munich, Munich, Germany

    Carsten Trinitis

  4. Ohio State University, Columbus, USA

    Ponnuswamy Sadayappan

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Dong, B., Wu, K., Byna, S., Tang, H. (2019). SLOPE: Structural Locality-Aware Programming Model for Composing Array Data Analysis. In: Weiland, M., Juckeland, G., Trinitis, C., Sadayappan, P. (eds) High Performance Computing. ISC High Performance 2019. Lecture Notes in Computer Science(), vol 11501. Springer, Cham. https://doi.org/10.1007/978-3-030-20656-7_4

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  • DOI: https://doi.org/10.1007/978-3-030-20656-7_4

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