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.
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Adalsteinsson, D., Sethian, J.A.: The fast construction of extension velocities in level set methods. J. Comput. Phys. 148, 2–22 (1999)
Sean Baxter. moderngpu 2.0 (2016)
Bayati, B., Chatelain, P., Koumoutsakos, P.: Adaptive mesh refinement for stochastic reaction-diffusion processes. J. Chem. Phys. 230(1), 13–26 (2011)
Bergdorf, M., Cottet, G.-H., Koumoutsakos, P.: Multilevel adaptive particle methods for convection-diffusion equations. Multiscale Model. Simul. 4(1), 328–357 (2005)
Bergdorf, M., Koumoutsakos, P.: A Lagrangian particle-wavelet method. Multiscale Model. Simul. 5(3), 980–995 (2006)
Bergdorf, M., Sbalzarini, I.F., Koumoutsakos, P.: A Lagrangian particle method for reaction-diffusion systems on deforming surfaces. J. Math. Biol. 61, 649–663 (2010)
Brun, E., Guittet, A., Gibou, F.: A local level-set method using a hash table data structure. J. Comput. Phys. 231(6), 2528–2536 (2012)
Gupta, A., Incardona, P., Aydin, A.D., Gumhold, S., Gunther, U., Sbalzarini, I F.: An architecture for interactive in situ visualization and its transparent implementation in OpenFPM. In: In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV’20), pp. 20–26. ACM, New York (2020)
Hoetzlein. R.K.: GVDB: raytracing sparse voxel database structures on the GPU. In: Eurographics/ACM SIGGRAPH Symposium on High Performance Graphics (2016)
Houston, B., Nielsen, M.B., Batty, C., Nilsson, O., Museth, K.: Hierarchical RLE level set: a compact and versatile deformable surface representation. ACM Trans. Graph. 25(1), 151–175 (2006)
Incardona, P., Leo, A., Zaluzhnyi, Y., Ramaswamy, R., Sbalzarini, I.F.: OpenFPM: a scalable open framework for particle and particle-mesh codes on parallel computers. Comput. Phys. Commun. 241, 155–177 (2019)
Kretz, M., Lindenstruth, V.: Vc: A C++ library for explicit vectorization. Softw. Pract. Exper. 42(11), 1409–1430 (2012)
Merrill, D.: CUDA UnBound (CUB) library (2015)
Museth, K.: VDB: high-resolution sparse volumes with dynamic topology. ACM Trans. Graph. 32(3), 27 (2013)
Setaluri, R., Aanjaneya, M., Bauer, S., Sifakis. E.: SPGrid: a sparse paged grid structure applied to adaptive smoke simulation. ACM Trans. Graph. 33(6), 205 (2014)
Zhang, W., et al.: AMReX: a framework for block-structured adaptive mesh refinement. J. Open Source Softw. 4(37), 1370–1370 (2019)
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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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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