Cloud computing became a routine tool for scientists in many domains. The JINR cloud infrastructure provides JINR users computational resources for performing various scientific calculations. In order to speed up achievements of scientific results the JINR cloud service for parallel applications was developed. It consists of several components and implements a flexible and modular architecture which allows to utilize both more applications and various types of resources, as computational backends. Besides this architecture increases the utilization of cloud idle resources.
An example of using the Cloud&HybriLIT resources in the scientific computing is the study of superconducting processes in the stacked long Josephson junctions (LJJ). LJJ systems are undergone the intensive research because of a perspective of practical applications in nano-electronics and quantum computing. Respective mathematical model is described by a system of the sine-Gordon type partial differential equations [1,2] where the spatial derivatives are approximated with help of standard finite difference formulas and the resulting system of ODEs is numerically solved by means of the 4th order Runge-Kutta procedure. Parallel MPI-implementation of the numerical algorithm was developed in [3,4]. Preliminary results of numerical experiments of LJJs calculations at the JINR cloud infrastructure are presented in . In this contribution, we generalize the experience on application of the Cloud&HybriLIT resources for the high performance computing of physical characteristics in the LJJ system.
The second example is provisioning the HybriLIT cloud service for training and testing the deep recurrent neural networks (RNN) specially designed for BM@N track reconstruction . Trained RNN can process 6500 simulated track-candidates in one second on the single Nvidia Tesla M60 with 97.5% recognition efficiency.
The work is supported by RFBR under grant 15-29-01217.
- I. R. Rahmonov, Y. M. Shukrinov, and A. Irie, JETP Letters 99, 632 (2014);
- I. R. Rahmonov, Yu. M. Shukrinov, P. Kh. Atanasova, E. V. Zemlyanaya, and M. V. Bashashin,JETP 124 131 (2017);
- M.V. Bashashin, E.V. Zemlyanaya, I.R. Rahmonov, Yu.M. Shukrinov, P.Kh. Atanasova, A.V. Volokhova Numerical approach and parallel implementation for computer simulation of stacked long Josephson Junctions // Computer Research and Modeling, T.8, № 4, 2016, P.593–604;
- E. V. Zemlyanaya, M. V. Bashashin, I. R. Rahmonov, Yu. M. Shukrinov, P. Kh. Atanasova, and A. V. Volokhova. Model of stacked long Josephson junctions: Parallel algorithm and numerical results in case of weak coupling. AIP Conference Proceedings 1773, 110018(1-9) (2016);
- Aleksandrov E.I., Bashashin M.V., Belyakov D.V., Volohova A.V., Zemlyanaya E.V., Zuev M.I., Kutovskiy N.A., Matveev M.A., Nechaevskiy A.V., Ososkov G.A., Podgainy D.V., Rahmonov I.R., Streltsova O.I., Trofimov V.V., Shukrinov Yu.M. Investigation of efficiency of MPI-calculations on cloud and heterogeneous infrastructures of MICC JINR. Materials of the All-Russian Conference with International Participation "Information and Telecommunication Technologies and Mathematical Modeling of High-Tech Systems" (April 24-28, 2017, Moscow), PFUR, 2017, 206-208;
- D.Baranov, S.Mitsyn, G.Ososkov, P.Goncharov, A.Tsytrinov, Novel approach to the particle track reconstruction based on deep learning methods, Proceedings of the XXVI International Symposium on Nuclear Electronics & Computing (NEC’2017), рр 37-45.