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Conference Paper

Distributed Sparse Block Grids on GPUs.

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Incardona,  Pietro
Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society;

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Sbalzarini,  Ivo F.
Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society;

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Citation

Incardona, P., Bianucci, T., & Sbalzarini, I. F. (2021). Distributed Sparse Block Grids on GPUs. In High Performance Computing: 36th International Conference, ISC High Performance 2021, Virtual Event, June 24 – July 2, 2021, Proceedings (pp. 272-290). Cham: Springer International Publishing.


Cite as: https://hdl.handle.net/21.11116/0000-0008-DA58-0
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.