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Tasking framework for adaptive speculative parallel mesh generation

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Abstract

Handling the ever-increasing complexity of mesh generation codes along with the intricacies of newer hardware often results in codes that are both difficult to comprehend and maintain. Different facets of codes such as thread management and load balancing are often intertwined, resulting in efficient but highly complex software. In this work, we present a framework which aids in establishing a core principle, deemed separation of concerns, where functionality is separated from performance aspects of various mesh operations. In particular, thread management and scheduling decisions are elevated into a generic and reusable tasking framework. The results indicate that our approach can successfully abstract the load balancing aspects of two case studies, while providing access to a plethora of different execution back-ends. One would expect, this new flexibility to lead to some additional cost. However, for the configurations studied in this work, we observed up to \(13\%\) speedup for some meshing operations and up to \(5.8\%\) speedup over the entire application runtime compared to hand-optimized code. Moreover, we show that by using different task creation strategies, the overhead compared to straight-forward task execution models can be improved dramatically by as much as \(1200\%\) without compromises in portability and functionality.

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Notes

  1. Recently, Intel®Threading Building Blocks was renamed to Intel®oneAPI Threading Building Blocks (oneTBB) to highlight that the tool is part of the oneAPI ecosystem.

  2. \(h=\log _2(n/grainsize)\) is the depth of a perfect binary tree with n/grainsize terminal nodes. \(2^{h+1} -1\) is the number of nodes for a perfect binary tree with depth h.

  3. https://www.khronos.org/sycl (Accessed 27th April 2021).

  4. https://kokkos.org (Accessed 27th April 2021).

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Acknowledgements

We would like to thank the reviewers for providing helpful comments on earlier drafts of the manuscript. This research was sponsored in part by the NASA Transformational Tools and Technologies Project (NNX15AU39A) of the Transformative Aeronautics Concepts Program under the Aeronautics Research Mission Directorate, NSF grant no. CCF-1439079, the Richard T. Cheng Endowment, the Modeling and Simulation fellowship of Old Dominion University and the Dominion Scholar fellowship of Old Dominion University. Experiments were supported by the Research Computing clusters at Old Dominion University. The authors would like to thank Kevin Garner for the corrections of the English text in the manuscript.

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  1. Christos Tsolakis, Polykarpos Thomadakis, Nikos Chrisochoides Center for Real-Time Computing, Old Dominion University, Norfolk, VA, USA

    Christos Tsolakis, Polykarpos Thomadakis & Nikos Chrisochoides

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Tsolakis, C., Thomadakis, P. & Chrisochoides, N. Tasking framework for adaptive speculative parallel mesh generation. J Supercomput 78, 1–32 (2022). https://doi.org/10.1007/s11227-021-04158-9

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