First Experience of Accelerating a Field-Induced Chiral Transition Simulation Using the SX-Aurora TSUBASA

Authors

  • Shinji Yoshida Graduate School of Information Science and Technology, Osaka University
  • Arata Endo Cybermedia Center, Osaka University
  • Hirono Kaneyasu Department of Science, University of Hyogo
  • Susumu Date Cybermedia Center, Osaka University

DOI:

https://doi.org/10.14529/jsfi210203

Abstract

An analysis method based on the Ginzburg-Landau equation for the superconductivity is applied to the field-induced chiral transition simulation (FICT). However, the FICT is time consuming because it takes approximately 10 hours on a single SX-ACE vector processor. Moreover, the FICT must be repeatedly performed with parameters changed to understand the mechanism of the phenomenon. The newly emerged SX-Aurora TSUBASA, the successor of the SX-ACE processor, is expected to provide much higher performance to the programs executed on the SX-ACE as is. However, the SX-Aurora TSUBASA processor has changed its architecture of compute nodes and gives users three different execution models, which leads to users’ concerns and questions in terms of how three execution models should be selectively used. In this paper, we report the first experience of using the SX-Aurora TSUBASA processor for the FICT. Specifically, we have developed three implementations of the FICT corresponding to the three execution models suggested by the SX-Aurora TSUBASA. For acceleration of the FICT, improvement of the vectorization ratio in the program execution and the efficient transfer of data to the general purpose processor as the vector host from the vector processor as the vector engine is explored. The evaluation in this paper shows how acceleration of the FICT is achieved as well as how much effort of users is required.

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Published

2021-09-14

How to Cite

Yoshida, S., Endo, A., Kaneyasu, H., & Date, S. (2021). First Experience of Accelerating a Field-Induced Chiral Transition Simulation Using the SX-Aurora TSUBASA. Supercomputing Frontiers and Innovations, 8(2), 43–58. https://doi.org/10.14529/jsfi210203