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COMBS: First Open-Source Based Benchmark Suite for Multi-physics Simulation Relevant HPC Research

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Algorithms and Architectures for Parallel Processing (ICA3PP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12452))

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Abstract

Recent scientific computing increasingly relies on multi-scale multi-physics simulations to enhance predictive capabilities by replacing a suite of stand-alone simulation codes that independently simulate various physical phenomena. Inevitably, multi-physics simulation demands high performance computing (HPC) through advanced hardware and software accelerating due to its intensive computing workload and run-time communication needs. Thus, its research has become a hotspot across different disciplines. However, it is observed that most benchmarks used in the evaluation of corresponding work are through commercial or in-house codes. Then, the lack of accessible open-source multi-physics benchmark suites has presented a challenge in uniformly evaluating simulation performance across related disciplines. This work proposes the first open-source based benchmark suite with 12 selected benchmarks for research in multi-physics simulation, the Clarkson Open-Source Multi-physics Benchmark Suite (COMBS). Multiple metrics have been gathered for these benchmarks, such as instructions per second and memory usage. Also provided are build and benchmark scripts to improve usability. Additionally, their source codes and installation guides are available for downloading through a github repository built by the authors. The selected benchmarks are from key applications of multi-physics simulation and highly cited publications. It is believed that this benchmark suite will facilitate to harness the full potential of HPC research in the field of multi-physics simulation.

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References

  1. Liu, Y., Nishimura, M., Seydaliev, M., Piro, M.: Backbone: a multi-physics framework for coupling nuclear codes based on CORBA and MPI. Nucl. Eng. Radiat. Sci. (2017)

    Google Scholar 

  2. Gouja, I., Avramova, M., Rubin, A.: Development and optimization of coupling interfaces between reactor core neutronics and thermal-hydraulic codes. In: The International Conference on Advances in Reactor Physics to Power the Nuclear Renaissance (2010)

    Google Scholar 

  3. Gomez-Torres, A.M., Sanchez-Espinoza, V., Ivanov, K., Macian-Juan, R.: DYNSUB: a high fidelity coupled code system for the evaluation of local safety parameters-part I: development, implementation and verification. Ann. Nucl. Energy 48, 108–122 (2012)

    Article  Google Scholar 

  4. Sanchez, V., Al-Hamry, A.: Development of a coupling scheme between MCNP and COBRA-TF for the prediction of the pin power of a PWR fuel assembly. In: The International Conference on Mathematics, Computational Methods and Reactor Physics (2009)

    Google Scholar 

  5. Chen, Z., Chen, X.-N., Rineiski, A., Zhao, P., Chen, H.: Coupling a CFD code with neutron kinetics and pin thermal models for nuclear reactor safety analyses. Ann. Nucl. Energy 83, 41–49 (2015)

    Article  Google Scholar 

  6. Tao, X., Liu, Yu., Liu, T., Li, G., Aydemir, N.: Multiphysics modelling of background dose by systemic targeted alpha therapy. Med. Imaging Radiat. Sci. (2018). https://doi.org/10.1016/j.jmir.2018.06.002

    Article  Google Scholar 

  7. Xu, T., Liu, T., Li, G., Dugal, C., Li, Y.: Microdosimetric and biokinetic modelling of alpha-immuno-conjugate transport in endothelial cells. J. Med. Imaging Radiat. Sci. 50, S1–S2 (2019)

    Article  Google Scholar 

  8. Xu, T., et al.: Technical note: the development of a multi-physics simulation tool to estimate the background dose by systemic targeted alpha therapy. Med. Phys. (2020)

    Google Scholar 

  9. Xiao, H.: A multi-physics approach to the co-design of 3D multi-core processors. Ph.D. dissertation (2018)

    Google Scholar 

  10. Errera, M., et al.: Multi-physics coupling approaches for aerospace numerical simulations. J. Aerosp. Lab (2011)

    Google Scholar 

  11. Schmidt, R., Hooper, R., Belcourt, N., Pawlowski, R.: MOOSE: a parallel computational framework for coupled systems of nonlinear equations. Nucl. Eng. Des. 239(10), 1768–1778 (2009)

    Article  Google Scholar 

  12. Schmidt, R., Belcourt, N., Hooper, R., Pawlowski, R.: An introduction to lime 1.0 and its use in coupling codes for multiphysics simulations. Sandia Report, SAND2011-8524 (2011)

    Google Scholar 

  13. SALOME official webpage (2019)

    Google Scholar 

  14. Ko, S.-H., Kim, N., Kim, J., Thota, A., Jha, S.: Efficient runtime environment for coupled multi-physics simulations: dynamic resource allocation and load-balancing. In: 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (2010)

    Google Scholar 

  15. Sfika, N., Korfiati, A., Alexakos, C., Likothanassis, S., Daloukas, K., Tsompanopoulou, P.: Dynamic cloud resources allocation on multidomain/multiphysics problems. In: 3rd International Conference on Future Internet of Things and Cloud (2015)

    Google Scholar 

  16. Hermann, E., Raffin, B., Faure, F., Gautier, T., Allard, J.: Multi-GPU and multi-CPU parallelization for interactive physics simulations. In: D’Ambra, P., Guarracino, M., Talia, D. (eds.) Euro-Par 2010. LNCS, vol. 6272, pp. 235–246. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15291-7_23

    Chapter  Google Scholar 

  17. CORAL-2 Benchmarks (2019). https://asc.llnl.gov/coral-2-benchmarks/

  18. Hyman, J.M., Nicolaenko, B.: The Kuramoto-Sivashinsky equation: a bridge between PDE’s and dynamical systems. Physica D: Nonlinear Phenomena 18, 113–126 (1986)

    Article  MathSciNet  Google Scholar 

  19. 2D Heat Benchmark Source Codes (2011)

    Google Scholar 

  20. Horak, V., Gruber, P.: Multi-physics coupling approaches for aerospace numerical simulations. Parallel Numerics (2005)

    Google Scholar 

  21. Hundsdorfer, W.H., Verwer, J.G.: Numerical solution of time-dependent advection-diffusion-reaction equations. Parallel Numerics (2011)

    Google Scholar 

  22. Advection-Diffusion Equation Benchmark Source Codes (2017). https://github.com/antoine-levitt/benchmark_heat

  23. Fidibench Benchmark Source Codes (2019). https://github.com/pletzer/fidibench

  24. HPCG Benchmark Website (2019). http://www.hpcg-benchmark.org/

  25. KS-PDE Benchmark Source Codes (2018). https://github.com/johnfgibson/julia-pde-benchmark

  26. TAMIR BENDORY VEIT ELSER, TI-YEN LAN. Benchmark problems for phase retrieval (2017)

    Google Scholar 

  27. Phase Retrieval Benchmark Source Codes (2019). https://github.com/veitelser/phase-retrieval-benchmarks

  28. Sombrero Benchmark Source Codes (2019). https://github.com/sa2c/sombrero

  29. OpenMPI (2019). https://www.open-mpi.org/

  30. OpenMP (2019). https://www.openmp.org/

  31. COMBS Github (2020). https://github.com/dowlinah/COMBS

  32. Nethercote, N., Seward, J.: Valgrind: a framework for heavyweight dynamic binary instrumentation. ACM SIGPLAN Not. 42(6), 89–100 (2007)

    Article  Google Scholar 

  33. Massif: a heap profiler (2020). https://valgrind.org/docs/manual/ms-manual.html

  34. Callgrind: a call-graph generating cache and branch prediction profiler (2020). http://valgrind.org/docs/manual/cl-manual.html

  35. Bienia, C., Kumar, S., Singh, J.P., Li, K.: The parsec benchmark suite: characterization and architectural implications. In: Proceedings of the 17th International Conference on Parallel Architectures and Compilation Techniques, pp. 72–81 (2008)

    Google Scholar 

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Acknowledgments

This work is partially funded by NSF Award OAC-1852102.

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Correspondence to Yu Liu .

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1 Artifact Description Appendix

1 Artifact Description Appendix

A Github website [31] has been designed to include the source codes of all benchmarks and the installation guide (tested on Ubuntu Linux 18.04). Users can download the source codes of all benchmarks through the command below, and then follow the installation guide of each benchmark to install and test them.

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Dowling, A., Swiatowicz, F., Liu, Y., Tolnai, A.J., Engel, F.H. (2020). COMBS: First Open-Source Based Benchmark Suite for Multi-physics Simulation Relevant HPC Research. In: Qiu, M. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2020. Lecture Notes in Computer Science(), vol 12452. Springer, Cham. https://doi.org/10.1007/978-3-030-60245-1_1

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