ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2017, Vol. 54 ›› Issue (4): 844-854.doi: 10.7544/issn1000-1239.2017.20150923

Previous Articles     Next Articles

Two Level Parallel Data Read Acceleration Method for Visualization in Scientific Computing

Shi Liu1,2,3, Xiao Li2,3, Cao Liqiang2,3, Mo Zeyao2,3   

  1. 1(China Ship Research and Development Academy, Beijing 100101); 2(Institute of Applied Physics and Computational Mathematics, Beijing 100088); 3(CAEP Software Center for High Performance Numerical Simulation, Beijing 100088)
  • Online:2017-04-01

Abstract: In order to match the overall computing capability of super computer, the super computer’s storage subsystem usually has good I/O performance scalability, which causes that, applications’ I/O access concurrency under the best performance of the storage subsystem and the total compute core number (tens of thousands to several millions) of super computer are usually in the same order of magnitude; however, the process number (equals to the I/O access concurrency) used in visualization in scientific computing (ViSC) applications is usually relatively small (experientially set to 1% of used computing process number, typically several to hundreds). Therefore, the best I/O performance of the storage subsystem cannot be achieved. In this paper we propose a two level parallel data read-based acceleration method for ViSC applications. Multi threads parallel data accessing is introduced into the visualization process; the I/O access concurrency of the super computer’s storage subsystem has been enhanced and visualization applications’ data read rate has been promoted through the two level parallel read, i.e. the parallelism among multi processes and the parallelism among multi threads inner process. The test results show that, under various visualization process scales, the peak data read rate using two parallel mode is higher than that using single parallel mode by 33.5%-269.5%, while the mean data read rate using two parallel mode is higher than that using single parallel mode by 26.6%-232.2%; according to the diverse scientific computing applications and various process scales, based on two level parallel data read method, the overall peak running speed of visualization applications can be accelerated by 19.5%-225.7%, and the mean speed can be accelerated by 15.8%-197.6%.

Key words: two level parallel data read, visualization in scientific computing, data access pattern, storage subsystem, I/O performance characteristics

CLC Number: