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5Stefan exterior problem is solved for the computation of the boundary nodes displacement in the grid
model. Grid internal nodes motion is computed using an elastic smoothing method. Quality preservation of
the grid model during the computation is the application condition for the approach. Three cell quality criteria
are considered, as well as additional possibilities to preserve grid topology.
A step-by-step computational algorithm is proposed using the example of the heat-transfer problem solu-
tion with the account for the design shape changing; it includes the computation until the stopping criterion is
met, remeshing and proceeding with the computation.
References
1. Deryugin Yu.N., Zelensky D.K., Glazunov V.A. et al. LOGOS multifunctional software package: physical and
mathematical computational models for aero- and hydrodynamics and heat transfer: Preprint. RFNC-VNIIEF. 111-2013.
Sarov: RFNC-VNIIEF, 2013.
Some approaches to the creation of supercomputer technologies for solving compute-intensive problem
B. �. Glinskiy1, D. V. Wins1, Y. A. Zagorulko2, G. B. Zagorulko2, I. M. Kulikov1, A. F. Sapetina1, P. A. Titov1,
I. G. Chernykh1
1Institute of Computational Mathematics and Mathematical Geophysics SB RAS
2A. P. Ershov Institute of Informatics System SB RAS
Email: gbm@sscc.ru
DOI 10.24412/cl-35065-2021-1-01-72
The issues of using supercomputer technologies developed by the authors of the article to solve compute-
intensive problems are discussed. The developed technology of creating algorithmic and software for super-
computers contains three related stages: co-design, by which we understand the adaptation of the problem
statement, the mathematical method, the computational algorithm to the parallel architecture of the super-
computer at all stages of the problem solution; study of the scalability of computational algorithms for the
most promising supercomputers based on simulation modeling; evaluation of the energy efficiency of algo-
rithms for various implementations on a given supercomputer architecture [1]. It is proposed to further devel-
op the proposed approach with the use of intellectual support for solving computationally complex problems
using the ontology of computational methods and algorithms for solving the problem, the ontology of compu-
tational heterogeneous architectures and decision rules [2]. For clarity, illustrations are presented that sche-
matically display the various components of the process of solving a geophysical problem, from stating to im-
plementation on a supercomputer, and also how those are interconnected [3]. An example of field observation
processing for one of the areas of Western Siberia using the developed system is presented [4].
The work was carried out within the framework of the budget project of the ICMMG SB RAS 0251-2021-0005 (sec-
tion 3), as well as with the support of the RFBR grants No 19-07-00085 (sections 2, 3).
References
1. Glinskiy, B., Kulikov, I., Chernykh, I., Snytnikov, A., Sapetina, A., Weins, D. The integrated approach to solving large-
size physical problems on supercomputers (2017) Communications in Computer and Information Science, 793, pp. 278-
289. DOI: 10.1007/978-3-319-71255-0_22.
2. B. Glinskiy, Y. Zagorulko, G. Zagorulko, I. Kulikov, A. Sapetina. The Creation of Intelligent Support Methods for
Solving Mathematical Physics Problems on Supercomputers. Russian Supercomputing Days 2019, Springer International
Publishing 2019, 427-438, DOI 10.1007/978-3-030-36592-9_35.
3. Boris Glinskiy, Anna Sapetina, Valeriy Martynov, Dmitry Weins, Igor Chernykh The Hybrid-Cluster Multilevel
Approach to Solving the Elastic Wave Propagation Problem. // Communications in Computer and Information Science
book series, Springer, (CCIS, V. 753), Pages 261-274.
4. B.M. Glinskiy, G.F. Zhernyak, P. A. Titov. System of intelligent support for solving geophysical problems. Proc. of the
6th Russian Supercomputing Days-2020, Moscow, Sept. 21�22, 2020. P. 11�18. DOI: 10.29003/m1406.RussianSCDays-2020.
Thermal efficiency and computing productivity metrics for data center operations
A. A. Grishina1*, M. Chinnici2, A.-L. Kor3, D. De Chiara4, J.-P. Georges5, E. Rondeau5
1Simula Research Laboratory, Oslo, Norway
2ENEA-R.C. Casaccia, Rome, Italy
3Leeds Beckett University, Leeds, UK
4ENEA-R.C. Portici, Portici (Naples), Italy
5CRAN-CNRS, University of Lorraine, Nancy, France
Email: a.a.grishina17@gmail.com
DOI 10.24412/cl-35065-2021-1-02-93
Data Centers (DCs) provide scalable on-demand computing and networking services to a growing number
of businesses, government, health organizations, and end-users. Naturally, DCs� power demand for cooling and
IT equipment has increased over the last few years [1]. However, some energy utilized in DCs is wasted for
both inefficient cooling and applications that do not run properly on computing clusters [2]. We present a
framework for the investigation of (a) hidden thermal pitfalls using thermal metrics [3] and guidelines [4] as
well as machine learning for assessment of thermal factors affecting individual servers [5] and (b) energy
waste that is caused by ineffective computations and can be translated into carbon waste evaluation with the
help of a proposed metric [6]. The framework has been tested on job scheduling reports and (a) thermal me-
ters of CRESCO4 and CRESCO6 clusters in ENEA Portici DC. The work results in a set of recommendations on
how the productivity assessment could drive a new power and thermal efficiency management strategy.
This research work has been supported and funded by the PERCCOM Erasmus Mundus Program of the European Un-
ion [7].
*The work was conducted while the author followed EMJMD PERCCOM [7] at ENEA Casaccia R.C., Rome, Italy
References
1. A. Shehabi, et al., �United States Data Center Energy Usage Report,� Lawrence Berkeley Natl. Lab. Berkeley, CA,
Tech. Rep., pp. 166, 2016.
2. Chinnici, M.; Capozzoli, A.; Serale, G. Measuring energy e_ciency in data centers. In Pervasive Computing: Next
Generation Platforms for Intelligent Data Collection; Dobre, C., Xhafa, F., Eds.; Morgan Kaufmann: Burlington, MA, USA,
2016; Chapter 10; pp. 299�351. ISBN 9780128037027.
3. Capozzoli, A.; Serale, G.; Liuzzo, L.; Chinnici, M. Thermal metrics for data centers: A critical review. Energy Procedia
2014, 62, 391�400.
4. ASHRAE Technical Committee 9.9, �Thermal Guidelines for Data Processing Environments � Expanded Data Center
Classes and Usage Guidance,� 2011.
5. Grishina A, Chinnici M, Kor A-L, Rondeau E, Georges J-P. A Machine Learning Solution for Data Center Thermal
Characteristics Analysis. Energies. 2020; 13(17):4378. https://doi.org/10.3390/en13174378
6. Grishina, A.; Chinnici, M.; De Chiara, D.; Guarnieri, G.; Kor, A.-L.; Rondeau, E.; Georges, J.-P. DC Energy Data Meas-
urement and Analysis for Productivity and Waste Energy Assessment. In Proceedings of the 2018 IEEE International Con-
ference on Computational Science and Engineering (CSE), Bucharest, Romania, 29�31 October 2018; IEEE: Piscataway, NJ,
USA, 2018; pp. 1�11, ISBN 978-1-5386-7649-3.
