The main scientific and technical problems of using hybrid HPC clusters in materials science

The article discusses the use of hybrid HPC clusters for the execution of software designed to calculate the electronic structure and atomic scale materials modeling. Modern software systems, which are designed to solve the problems of materials science, use the capabilities of various hardware computing accelerators to increase productivity. The use of such computing technologies requires the adaptation of application program code to hybrid computing architectures, which include classic central processing units (CPUs) and specialized graphics accelerators (GPUs).
The use of large computing hybrid systems requires the development of methods for ensuring the workloading of such computing systems that will allow efficient use of computing resources and avoid equipment downtime.
First of all, these methods should allow parallel execution of user applications using computational accelerators. However, in practice, software environments designed to solve application problems cannot be deployed in the same computing environment due to software incompatibility. In order to overcome this limitation and ensure the parallel execution of diverse types of materials science tasks, the creation of individual task execution environments based on virtualization technologies and cloud technologies.
The continuation of virtualization technologies and the provision of cloud services is the construction of digital platforms. The article proposes the use of a digital platform for hosting scientific materials science services that provide calculations using various application software systems. Digital platforms make it possible to provide a unified user interface to scientific materials science services. The platform provides opportunities for finding the necessary scientific services, transferring source data and results between users, the platform and hybrid high-performance clusters.

high-performance computing cluster, hybrid architecture, graphics accelerator, electronic structure calculations, quantum-mechanical molecular dynamics, VASP, Quantum ESPRESSO

1. Abramov S. M., Lilitko E. P. Current state and development prospects of high-end HPC system. Information Technologies and Computation Systems, 2013, no. 2, pp. 6—22. (In Russ.)

2. Zhuravlev A. A., Reviznikov D. L., Abgaryan K. K. The method of discrete elements with an atomic structure. In: Proceedings of the XXI international conference on computational mechanics and modern applied software systems (VMSPSPS’2019). Moscow, 2019, pp. 59—61. (In Russ.)

3. Mikurova A. V., Skvortsov V. S. A generalized prediction model of inhibition of neuraminidase of influenza virus of various strains. Biochem. Moscow Suppl. Ser. B, 2018, vol. 12, pp. 322—329. DOI: 10.1134/S1990750818040054

4. Mikurova A. V., Skvortsov V. S., Raevsky O. A. Computational evaluation of selectivity of inhibition of muscarinic receptors M1-M4. Biomedical Chemistry: Research and Methods, 2018, vol. 1, no. 3, p. e00072. (In Russ.). DOI: 10.18097/BMCRM00072

5. Vouzis P. D., Sahinidis N. V. GPU-BLAST: using graphics processors to accelerate protein sequence alignment. Bioinformatics, 2011, vol. 27, no. 2, pp. 182—188. DOI: 10.1093/bioinformatics/btq644

6. Gorchakov A. Yu., Malkova V. U. Comparison of Intel Core-i7, Intel Xeon, Intel Xeon Phi and IBM Power 8 processors using the example of initial data recovery. International Journal of Open Information Technologies, 2018, vol. 6, no. 4, pp. 12—17. (In Russ.)

7. Volkov S., Sukhoroslov O. V. Simplifying the use of clouds for scientific computing with Everest. Procedia Computer Science, 2017, vol. 119, pp. 112—120. DOI: 10.1016/j.procs.2017.11.167

8. Volovich K. I., Zatsarinnyy A. A., Kondrashev V. A., Shabanov A. P. Scientific research as a cloud service. Sistemy i Sredstva Inform., 2017, vol. 27, no. 1, p. 73—84. (In Russ.). DOI: 10.14357/08696527170105

9. Gorchakov A. Yu. Using OPENMP to implement the multithreaded method of uneven coatings // Advanced Information Technologies (PIT 2018). Proceedings of the International Scientific and Technical Conference. Samara: Izdatel’stvo Samarskogo nauchnogo tsentra RAN, 2018. 613—616. (In Russ.)

10. Volovich K. I., Denisov S. A., Malkovsky S. I. Formation of an individual modeling environment in a hybrid high-performance computing complex. In: Proc. of the I Intern. Conf. «Mathematical Modeling in Materials Science of Electronic Components. MMMEK-2019». Moscow: MAKS-Press, 2019, pp. 21—24. (In Russ.)

11. Afanasyev I., Voevodin V. The comparison of large-scale graph processing algorithms implementation methods for Intel KNL and NVIDIA GPU. In: Supercomputing. RuSCDays 2017. Communications in Computer and Information Science. Springer, Cham, 2017, pp. 80—94. DOI: 10.1007/978-3-319-71255-0_7

12. Ding F., Mey D., Wienke S., Zhang R., Li L. A Study on today’s cloud environments for HPC applications. In: Third International Conference, CLOSER 2013. Cloud Computing and Services Science. Berlin (Germany): Springer, 2014, pp. 114—127. DOI: 10.1007/978-3-319-11561-0_8

13. Zatsarinny A. A., Gorshenin A. K., Kondrashev V. A., Volovich K. I., Denisov S. A. Toward high performance solutions as services of research digital platform. Procedia Computer Science, 2019, vol. 150, pp. 622—627. DOI: 10.1016/j.procs.2019.02.078

14. Zatsarinny A. A., Gorshenin A. K., Volovich K. I., Kolin K. K., Kondrashev V. A., Stepanov P. V. Management of scientific services as the basis of the national digital platform «Science and Education». Strategicheskie prioritety, 2017, no. 2, pp. 103—113. (In Russ.)

15. Kondrashev V. A., Volovich K. I. Service management of a digital platform on the example of high-performance computing services. In: Materials of the International Scientific Conference. Voronezh, 2018. (In Russ.)

16. Kartsev A., Malkovsky S. I., Volovich K. I., Sorokin A. A. Research of the performance and scalability of the Quantum ESPRESSO package in the study of low-dimensional systems on hybrid computing systems. In: Proc. of the I Intern. Conf. «Mathematical Modeling in Materials Science of Electronic Components. MMMEK-2019». Moscow: MAKS-Press, 2019, pp. 18—21. (In Russ.)

17. Berriman G. B., Deelman E., Juve G., Rynge M., Vöckler J.-S. The application of cloud computing to scientific workflows: a study of cost and performance. Phil. Trans. R. Soc. A, 2013, vol. 371, no. 1983. DOI: 10.1098/rsta.2012.0066

18. Yakobovskiy M. V., Bondarenko A. A., Vyrodov A. V., Grigoriev S. K., Kornilina M. A., Plotnikov A. I., Polyakov S. V., Popov I. V., Puzyrkov D. V., Soukov S. A. Cloud service for solving multiscale nanotechnology problems of on clusters and supercomputers. Izvestiya YuFU. Tekhnicheskie nauki, 2016, no. 12, pp. 103—114. (In Russ.). DOI: 10.18522/2311-3103-2016-12-103114

19. Gorchakov A. Yu., Posypkin M. A. Comparison of variants of multithreading realization of method of branches and borders for multi-core systems. Modern Information Technologies and IT-Education, 2018, vol. 14, no. 1, pp. 138—148. (In Russ.). DOI: 10.25559/SITITO.14.201801.138-148

20. Regulations on the of the Center for Shared Use «Informatics». URL: http://www.frccsc.ru/ckp (accessed: 02.12.2019). (In Russ.)