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Static Partitioning of Spreadsheets for Parallel Execution

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Book cover Practical Aspects of Declarative Languages (PADL 2019)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11372))

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Abstract

Spreadsheets are popular tools for end-user development and complex modelling but can suffer from poor performance. While end-users are usually domain experts they are seldom IT professionals that can leverage today’s abundant multicore architectures to offset such poor performance. We present an iterative, greedy algorithm for automatically partitioning spreadsheets into load-balanced, acyclic groups of cells that can be scheduled to run on shared-memory multicore processors. A big-step cost semantics for the spreadsheet formula language is used to estimate work and guide partitioning. The algorithm does not require end-users to modify the spreadsheet in any way. We implement three extensions to the algorithm for further accelerating computation; two of which recognise common cell structures known as cell arrays that naturally express a degree of parallelism. To the best of our knowledge, no such automatic algorithm has previously been proposed for partitioning spreadsheets. We report a maximum 24-fold speed-up on 48 logical cores.

Supported by the Independent Research Fund Denmark.

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Notes

  1. 1.

    Originally referred to as the convexity constraint in [17] as it relates to convex subgraphs.

  2. 2.

    Available unmodified at https://gerrit.libreoffice.org/gitweb?p=benchmark.git;a=tree.

  3. 3.

    Raw data available at https://github.com/popular-parallel-programming/p3-results/tree/master/static-partitioning.

  4. 4.

    We used the Windows Performance Monitor to monitor performance characteristics and WinDbg to inspect the managed heap.

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Acknowledgements

The author would like to thank Peter Sestoft and Florian Biermann for valuable insight and discussions during the development of this work, as well as Peter Sestoft and Holger Stadel Borum for proofreading.

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Authors and Affiliations

  1. Computer Science Department, IT University of Copenhagen, Copenhagen, Denmark

    Alexander Asp Bock

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  1. Alexander Asp Bock
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Correspondence to Alexander Asp Bock .

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Editors and Affiliations

  1. Universidade Nova de Lisboa, Lisbon, Portugal

    José Júlio Alferes

  2. Chalmers University of Technology, Gothenburg, Sweden

    Moa Johansson

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Bock, A.A. (2019). Static Partitioning of Spreadsheets for Parallel Execution. In: Alferes, J., Johansson, M. (eds) Practical Aspects of Declarative Languages. PADL 2019. Lecture Notes in Computer Science(), vol 11372. Springer, Cham. https://doi.org/10.1007/978-3-030-05998-9_14

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  • DOI: https://doi.org/10.1007/978-3-030-05998-9_14

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