Optimizing Load Balance in a Parallel CFD Code for a Large-scale Turbine Simulation on a Vector Supercomputer

Research output: Contribution to journalArticlepeer-review


A turbine for power generation is one of the essential infrastructures in our society. A turbine’s failure causes severe social and economic impacts on our everyday life. Therefore, it is necessary to foresee such failures in advance. However, it is not easy to expect these failures from a real turbine. Hence, it is required to simulate various events occurring in the turbine by numerical simulations of the turbine. A multiphysics CFD code, “Numerical Turbine,” has been developed on vector supercomputer systems for large-scale simulations of unsteady wet steam flows inside a turbine. To solve this problem, the Numerical Turbine code is a block structure code using MPI parallelization, and the calculation space consists of grid blocks of different sizes. Therefore, load imbalance occurs when executing the code in MPI parallelization. This paper creates an estimation model that finds the calculation time from each grid block’s calculation amount and calculation performance. It proposes an OpenMP parallelization method for the load balance of MPI applications. This proposed method reduces the load imbalance by considering the vector performance according to the calculation amount based on the model. Moreover, this proposed method recognizes the need to reduce the load imbalance without pre-execution. The performance evaluation shows that the proposed method improves the load balance from 24.4 % to 9.3 %.

Original languageEnglish
Pages (from-to)114-130
Number of pages17
JournalSupercomputing Frontiers and Innovations
Issue number2
Publication statusPublished - 2021


  • MPI
  • OpenMP
  • hybrid parallelization
  • load balance
  • turbine simulation code
  • vector supercomputer

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications
  • Computational Theory and Mathematics


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