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Distributed Sparse Block Grids on GPUs

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 12728)

Abstract

We present a design and implementation of distributed sparse block grids that transparently scale from a single CPU to multi-GPU clusters. We support dynamic sparse grids as, e.g., occur in computer graphics with complex deforming geometries and in multi-resolution numerical simulations. We present the data structures and algorithms of our approach, focusing on the optimizations required to render them computationally efficient on CPUs and GPUs alike. We provide a scalable implementation in the OpenFPM software library for HPC. We benchmark our implementation on up to 16 Nvidia GTX 1080 GPUs and up to 64 Nvidia A100 GPUs showing state-of-the-art scalability (68% to 96% parallel efficiency) on three benchmark problems. On a single GPU, our implementation is 14 to 140-fold faster than on a multi-core CPU.

Keywords

  • Sparse grid
  • Block grid
  • CUDA
  • GPU
  • Distributed data

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Acknowledgments

The authors are grateful to the Centre for Information Services and High Performance Computing (ZIH) of TU Dresden and to the Scientific Computing Facility of MPI-CBG for providing their facilities for the benchmarks. This work was supported by the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung, BMBF) under funding codes 01/S18026A-F (competence center for Big Data and AI “ScaDS.AI Dresden/Leipzig”) and 031L0160 (project “SPlaT-DM – computer simulation platform for topology-driven morphogenesis”).

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Correspondence to Ivo F. Sbalzarini .

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Incardona, P., Bianucci, T., Sbalzarini, I.F. (2021). Distributed Sparse Block Grids on GPUs. In: Chamberlain, B.L., Varbanescu, AL., Ltaief, H., Luszczek, P. (eds) High Performance Computing. ISC High Performance 2021. Lecture Notes in Computer Science(), vol 12728. Springer, Cham. https://doi.org/10.1007/978-3-030-78713-4_15

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  • DOI: https://doi.org/10.1007/978-3-030-78713-4_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78712-7

  • Online ISBN: 978-3-030-78713-4

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