HPC Processors Benchmarking Assessment for Global System Science Applications
DOI:
https://doi.org/10.14529/jsfi190202Abstract
The work undertaken in this paper was done in the Centre of Excellence for Global Systems Science (CoeGSS) – an interdisciplinary project funded by the European Commission. CoeGSS project provides a computer-aided decision support in the face of global challenges (e.g. development of energy, water and food supply systems, urbanisation processes and growth of the cities, pandemic control, etc.) and tries to bring together HPC and global systems science. This paper presents a proposition of GSS benchmark which evaluates HPC architectures with respect to GSS applications and seeks for the best HPC system for typical GSS software environments. The outcome of the analysis is defining a benchmark which represents the average GSS environment and its challenges in a good way: spread of smoking habits and development of tobacco industry, development of green cars market and global urbanisation processes. Results of the tests that have been run on a number of recently appeared HPC platforms allow comparing processors’ architectures with respect to different applications using execution times, TDPs3 and TCOs4 as the basic metrics for ranking HPC architectures. Finally, we believe that our analysis of the results conveys a valuable information to the broadened GSS audience which might help to determine the hardware demands for their specific applications, as well as to the HPC community which requires a mature benchmark set reflecting requirements and traits of the GSS applications. Our work can be considered as a step into direction of development of such mature benchmark.
References
EPA: Cmaq: The community multiscale air quality modeling system. https://www.cmascenter.org/cmaq, accessed: 2018-09-21
DTC: The users page on hurricane wrf. https://dtcenter.org/HurrWRF/users, accessed: 2019-05-30
AMD: AMD EPYC 7000 series processors: Leading performance for the Cloud era. https://www.amd.com/system/files/2017-06/AMD-EPYC-Data-Sheet.pdf (2019), accessed: 2019-05-28
Guo, X., Morales, C., Saastad, O.W., Shamakina, A., Rijks, W., Weinberg, V.: Best practice guide – ARM64. http://www.prace-ri.eu/best-practice-guide-arm64/ (2019), accessed: 2019-05-28
Cmaq inputs and test case data. https://www.epa.gov/cmaq/cmaq-inputs-and-test-case-data (2018), accessed: 2018-09-21
Yule, G.G.U.: On the methods of measuring association between two attributes. Journal of the Royal Statistical Society 75(6), 579–652 (1912)
Wolf, S., Fuerst, S., Geiges, A., Steudle, G.A., von Postel, J., Jaeger, C.C.: Electric mobility in view of green growth. https://globalclimateforum.org/wp-content/uploads/2018/12/GCFWorkingPaper3-2017.pdf (2017), accessed: 2019-05-28
Maeda, T., Takemura, S., Furumura, T.: OpenSWPC: An open-source integrated parallel simulation code for modeling seismic wave propagation in 3D heterogeneous viscoelastic media. Earth Planets Space 69(102) (2017), DOI: 10.1186/s40623-017-0687-2
Bryan, G.: Cm1 homepage. http://www2.mmm.ucar.edu/people/bryan/cm1, accessed: 2019-05-30
Maeda, T.: OpenSWPC - an open-source seismic wave propagation code. https://github.com/takuto-maeda/OpenSWPC (2018), accessed: 2018-09-17
Bryan, G.H.: The governing equations for CM1 (2013)
Rubio-Campillo, X.: C++/Python Agent-Based Modelling framework for large-scale distributed simulations. https://github.com/xrubio/pandora, accessed: 2018-09-17
Bicas Caldeira, A., Haug, V., Vetter, S.: IBM power systems S822LC for high performance computing: Introduction and technical overview. https://www.redbooks.ibm.com/redpapers/pdfs/redp5405.pdf (2016), accessed: 2019-05-28
Bicas Caldeira, A., Kahle, M.E., Saverimuthu, G., Vearner, K.: IBM power systems S822LC: Technical overview and introduction. https://www.redbooks.ibm.com/redpapers/pdfs/redp5283.pdf (2015), accessed: 2019-05-28
Chemel, C., Fisher, B., Kong, X., Francis, X., Sokhi, R., Good, N., Collins, W., Folberth, G.: Application of chemical transport model CMAQ to policy decisions regarding PM2.5 in the UK. Atmospheric Environment 82, 410 – 417 (2014), DOI: 10.1016/j.atmosenv.2013.10.001
Biswas, M.K., Bernardet, L., Ginis, I., Kwon, Y., Liu, Q., Marchok, T., Sheinin, D., Tallapragada, V., Thomas, B., Tong, M., Trahan, S., Wang, W., Yablonsky, R., Zhang, X.: Hurricane weather research and forecasting (HWRF) model: 2017 scientific documentation (2018), DOI: 10.5065/D6MK6BPR, accessed: 2019-05-28
Geiges, A., Wolf, S., Steudle, G., Fuerst, S.: Report of framework for prototyping of parallel agent based modelling systems. http://coegss.eu/wp-content/uploads/2018/11/D3.8.pdf (2018), accessed: 2019-05-28
Swarup, S., Marathe, M.V.: Generating synthetic populations for social modeling. http://people.virginia.edu/˜ss7rs/synthetic_population_tutorial_2/slides.php (2017), accessed: 2019-05-28
CoeGSS-Project: Agent-based modelling framework for python. https://github.com/CoeGSS-Project/abm4py, accessed: 2019-05-30
Newburn, C.J., Abdurachmanov, D., Kaplan, L., McIntosh-Smith, S., McLean, M., Sumimoto, S., Van Hensbergen, E., Vergara Larrea, V.: The ARM software ecosystem: Are we there yet? https://arm-hpc.gitlab.io/presentations/SC17-Arm-Ecosystem.pdf (2017), accessed: 2019-05-28
Epstein, J.M., Pankajakshan, R., Hammond, R.A.: Combining computational fluid dynamics and agent-based modeling: A new approach to evacuation planning. PLOS ONE 6(5), 1–5 (2011), DOI: 10.1371/journal.pone.0020139
Hoefler, T., Belli, R.: Scientific benchmarking of parallel computing systems: Twelve ways to tell the masses when reporting performance results. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. pp. 73:1–73:12. SC ’15, ACM, New York, NY, USA (2015), DOI: 10.1145/2807591.2807644
Gienger, M., Gogolenko, S., Geiges, A., Kaliszan, D., Petruczynik, S., Januszewski, R., Wolniewicz, P.: Second report on provided testbed components for running services and pilots. http://coegss.eu/wp-content/uploads/2018/11/D5.8.pdf (2018), accessed: 2019-05-28
Gienger, M., Petruczynik, S., Januszewski, R., Kaliszan, D., Gogolenko, S., Fuerst, S., Palka, M., Ubaldi, E.: First report on provided testbed components for running services and pilots. http://coegss.eu/wp-content/uploads/2018/02/D5.7.pdf (2017), accessed: 2019-05-28
Wikichip: Hi1616 - HiSilicon. https://en.wikichip.org/wiki/hisilicon/hi16xx/hi1616, accessed: 2019-05-28
Richmond, P.: FLAME GPU: Technical report and user guide. https://media. readthedocs.org/pdf/flamegpu/latest/flamegpu.pdf (2018), accessed: 2019-05-28
Richmond, P., Romano, D.: Template-Driven Agent-Based Modeling and Simulation with CUDA, pp. 313–324. GPU Computing Gems Emerald Edition, Elsevier (2011), DOI: 10.1016/b978-0-12-384988-5.00021-8
Family 8335+03 IBM power system S822LC for high performance computing. https://ibm.co/2cMMb8B (2018), accessed: 2018-09-18
Gogolenko, S.: Software for agent based social simulation with raster inputs in distributed HPC environments. https://fs.hlrs.de/projects/teraflop/26thWorkshop_talks/WSSP26-24_Gogolenko.pdf (2017), accessed: 2019-05-28
Suleimenova, D., Bell, D., Groen, D.: A generalized simulation development approach for predicting refugee destinations. Scientific Reports 7(1), 13377 (2017), DOI: 10.1038/s41598-017-13828-9
Intel R Xeon R Gold 6140 processor specification. https://ark.intel.com/products/120485/Intel-Xeon-Gold-6140-Processor-24_75M-Cache-2_30-GHz (2018), accessed: 2018-09-18
Downloads
Published
How to Cite
Issue
License
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-Non Commercial 3.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.