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A Theoretical Model for Global Optimization of Parallel Algorithms

Author

Listed:
  • Julian Miller

    (Chair for High Performance Computing, IT Center, RWTH Aachen University, 52074 Aachen, Germany)

  • Lukas Trümper

    (Chair for High Performance Computing, IT Center, RWTH Aachen University, 52074 Aachen, Germany
    Huddly AS, Karenslyst Allé 51, 0279 Oslo, Norway)

  • Christian Terboven

    (Chair for High Performance Computing, IT Center, RWTH Aachen University, 52074 Aachen, Germany)

  • Matthias S. Müller

    (Chair for High Performance Computing, IT Center, RWTH Aachen University, 52074 Aachen, Germany)

Abstract

With the quickly evolving hardware landscape of high-performance computing (HPC) and its increasing specialization, the implementation of efficient software applications becomes more challenging. This is especially prevalent for domain scientists and may hinder the advances in large-scale simulation software. One idea to overcome these challenges is through software abstraction. We present a parallel algorithm model that allows for global optimization of their synchronization and dataflow and optimal mapping to complex and heterogeneous architectures. The presented model strictly separates the structure of an algorithm from its executed functions. It utilizes a hierarchical decomposition of parallel design patterns as well-established building blocks for algorithmic structures and captures them in an abstract pattern tree (APT) . A data-centric flow graph is constructed based on the APT, which acts as an intermediate representation for rich and automated structural transformations. We demonstrate the applicability of this model to three representative algorithms and show runtime speedups between 1.83 and 2.45 on a typical heterogeneous CPU/GPU architecture.

Suggested Citation

  • Julian Miller & Lukas Trümper & Christian Terboven & Matthias S. Müller, 2021. "A Theoretical Model for Global Optimization of Parallel Algorithms," Mathematics, MDPI, vol. 9(14), pages 1-14, July.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:14:p:1685-:d:596252
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    Citations

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    Cited by:

    1. Laith Abualigah & Ali Diabat & Raed Abu Zitar, 2022. "Orthogonal Learning Rosenbrock’s Direct Rotation with the Gazelle Optimization Algorithm for Global Optimization," Mathematics, MDPI, vol. 10(23), pages 1-42, November.

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