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Parallelizing and optimizing large-scale 3D multi-phase flow simulations on the Tianhe-2 supercomputer

Published: 10 April 2016 Publication History

Abstract

The lattice Boltzmann method LBM is a widely used computational fluid dynamics method for flow problems with complex geometries and various boundary conditions. Large-scale LBM simulations with increasing resolution and extending temporal range require massive high-performance computing HPC resources, thus motivating us to port it onto modern many-core heterogeneous supercomputers like Tianhe-2. Although many-core accelerators such as graphics processing unit and Intel MIC have a dramatic advantage of floating-point performance and power efficiency over CPUs, they also pose a tough challenge to parallelize and optimize computational fluid dynamics codes on large-scale heterogeneous system.

References

[1]
Godenschwager C, Schornbaum F, Bauer M, Köstler H, Rüde U. A framework for hybrid parallel flow simulations with a trillion cells in complex geometries. In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis p. 35. ACM, 2013.
[2]
Biferale L, Mantovani F, Pivanti M, Pozzati F, Sbragaglia M, Scagliarini A, ' Tripiccione R. Optimization of multi-phase compressible lattice Boltzmann codes on massively parallel multi-core systems. Procedia Computer Science 2011; Volume 4: pp.994-1003.
[3]
Bailey P, Myre J, Walsh SD, Lilja DJ, Saar MO. Accelerating lattice Boltzmann fluid flow simulations using graphics processors. In Parallel Processing, 2009. ICPP'09. International Conference on pp. 550-557. IEEE, 2009.
[4]
Fang J, Sips H, Zhang L, Xu C, Che Y, Varbanescu AL. Test-driving intel xeon phi. In Proceedings of the 5th ACM/SPEC International Conference on Performance Engineering pp. 137-148. ACM, 2014.
[5]
Fang J. Towards a Systematic Exploration of the Optimization Space for Many-Core Processors Doctoral dissertation, TU Delft, Delft University of Technology, 2014.
[6]
Xu C, Deng X, Zhang L, Fang J, Wang G, Jiang Y, Cheng X. Collaborating CPU and GPU for large-scale high-order CFD simulations with complex grids on the TianHe-1A supercomputer. Journal of Computational Physics 2014; Volume 278: pp.275-297.
[7]
Official website of openlbmflow. "http://www.lbmflow.com". {10 August 2015}
[8]
Introduction of Tianhe-2 on Top500 website. "http://www.top500.org/system/177999". {10 August 2015}
[9]
Williams S, Waterman A, Patterson D. Roofline: an insightful visual performance model for multicore architectures. Communications of the ACM 2009; Volume 52 Issue 4: pp.65-76.
[10]
Williams S, Carter J, Oliker L, Shalf J, Yelick K. Optimization of a lattice boltzmann computation on state-of-the-art multicore platforms. Journal of Parallel and Distributed Computing 2009; Volume 69 Issue 9: pp.762-777.
[11]
Liu Z, Song A, Xu L, Feng W, Zhou L, Zhang W. A High Scalable Hybrid MPI/OpenMP Parallel Model of Multiple-relaxation-time Lattice Boltzmann Method*. Journal of Computational Information Systems 2014; Volume 10 Issue 20: pp.10147-10157.
[12]
Mountrakis L, Lorenz E, Malaspinas O, Alowayyed S, Chopard B, Hoekstra AG. Parallel performance of an IB-LBM suspension simulation framework. Journal of Computational Science 2015; Volume 9: pp.45-50.
[13]
Pananilath I, Acharya A, Vasista V, Bondhugula U. An Optimizing Code Generator for a Class of Lattice-Boltzmann Computations, 2014.
[14]
Wei C, Zhenghua W, Zongzhe L, Lu Y, Yongxian W. An improved LBM approach for heterogeneous GPU-CPU clusters. In Bio}medical Engineering and Informatics BMEI, 2011 4th International Conference on Vol. 4, pp. 2095-2098. IEEE, 2011.
[15]
Kanoria AA, Damodaran M. Parallel Matlab implementation of the lattice boltzmann method on GPUs, 2014.
[16]
Feichtinger C, Habich J, Köstler H, Rüde U, Aoki T. Performance Modeling and Analysis of Heterogeneous Lattice Boltzmann Simulations on CPU-GPU Clusters. Parallel Computing, 2014.
[17]
Koda Y, Lien FS. The lattice Boltzmann method implemented on the GPU to simulate the turbulent flow over a square cylinder confined in a channel. Flow, Turbulence and Combustion, 2014, 1-18.
[18]
Crimi G, Mantovani F, Pivanti M, Schifano SF, Tripiccione R. Early experience on porting and running a lattice Boltzmann code on the Xeon-Phi co-processor. Procedia Computer Science 2013; Volume 18: pp.551-560.
[19]
Rosales C. Porting to the intel Xeon phi: opportunities and challenges. In Extreme Scaling Workshop XSW, 2013 pp. 1-7. IEEE, 2013.
[20]
McIntosh-Smith S, Curran D. Evaluation of a performance portable lattice Boltzmann code using OpenCL. In Proceedings of the International Workshop on OpenCL 2013 &2014 p. 2. ACM, 2014.
[21]
Bortolotti G, Caberletti M, Crimi G, Ferraro A, Giacomini F, Manzali M, ' Zanella M. Computing on Knights and Kepler Architectures. In Journal of Physics: Conference Series 2014; Volume 513 Issue 5: pp.52032-52038.
[22]
Calore E, Schifano SF, Tripiccione R. A portable OpenCL lattice Boltzmann code for multi-and many-core processor architectures. Procedia Computer Science 2014; Volume 29: pp.40-49.

Cited By

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  • (2017)Performance modeling and optimization of parallel LU-SGS on many-core processors for 3D high-order CFD simulationsThe Journal of Supercomputing10.1007/s11227-016-1943-073:6(2506-2524)Online publication date: 1-Jun-2017
  • (2016)A Review on A High Performance Computing-based Interval Fuzzy Type-2 Model for Web Services' QoS EvaluationProceedings of the 4th International Conference on Information and Network Security10.1145/3026724.3026734(77-82)Online publication date: 28-Dec-2016

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Published In

cover image Concurrency and Computation: Practice & Experience
Concurrency and Computation: Practice & Experience  Volume 28, Issue 5
April 2016
296 pages

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John Wiley and Sons Ltd.

United Kingdom

Publication History

Published: 10 April 2016

Author Tags

  1. LBM
  2. Tianhe-2
  3. heterogeneous system
  4. intel xeon phi
  5. multi-phase flow

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Cited By

View all
  • (2017)Performance modeling and optimization of parallel LU-SGS on many-core processors for 3D high-order CFD simulationsThe Journal of Supercomputing10.1007/s11227-016-1943-073:6(2506-2524)Online publication date: 1-Jun-2017
  • (2016)A Review on A High Performance Computing-based Interval Fuzzy Type-2 Model for Web Services' QoS EvaluationProceedings of the 4th International Conference on Information and Network Security10.1145/3026724.3026734(77-82)Online publication date: 28-Dec-2016

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