Three-Dimensional Fluid Code with XcalableMP

In order to adapt parallel computers to general convenient tools for computational scientists, a high-level and easy-to-use portable parallel programming paradigm is mandatory. XcalableMP, which is proposed by the XcalableMP Specification Working Group, is a directive-based language extension for Fortran and C to easily describe parallelization in programs for distributed memory parallel computers. The Omni XcalableMP compiler, which is provided as a reference XcalableMP compiler, is currently implemented as a source-to-source translator. It converts XcalableMP programs to standard MPI programs, which can be easily compiled by the native Fortran compiler and executed on most of parallel computers. A three-dimensional Eulerian fluid code written in Fortran is parallelized by XcalableMP using two different programming models with the ordinary domain decomposition method, and its performances are measured on the K computer. Programs converted by the Omni XcalableMP compiler prevent native Fortran compiler optimizations and show lower performance than that of hand-coded MPI programs. Finally almost the same performances are obtained by using specific compiler options of the native Fortran compiler in the case of a global-view programming model, but performance degradation is not improved by specifying any native compiler options when the code is parallelized by a local-view programming model.

MULTISCALE BOUNDARY ELEMENT METHOD FOR LAPLACE EQUATION

In this paper, the multiscale boundary element method is applied to solve the Laplace equation numerically. The new technique is the coupling of the multiscale technique and the boundary element method in order to speed up the computation. A numerical example is given to illustrate the efficiency of the proposed method. The computed numerical solutions by the proposed method will be compared with the solutions obtained by the conventional boundary element method with the help of Fortran compiler. By comparison, results show that the new technique use less iterations to arrive at the solutions.