scholarly journals Massively parallel computation of accurate densities for N-body dark matter simulations using the phase-space-element method

2017 ◽  
Vol 20 ◽  
pp. 68-76
Author(s):  
R. Kaehler
Keyword(s):  
Dark Matter ◽  
Phase Space ◽  
Parallel Computation ◽  
Massively Parallel ◽  
Element Method

Parallel finite-element method using domain decomposition and Parareal for transient motor starting analysis

2019 ◽  
Vol 38 (5) ◽  
pp. 1507-1520 ◽  
Author(s):  
Yasuhito Takahashi ◽  
Koji Fujiwara ◽  
Takeshi Iwashita ◽  
Hiroshi Nakashima
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Parallel Computing ◽  
Domain Decomposition ◽  
Parallel Computation ◽  
Domain Decomposition Method ◽  
Space Time ◽  
Content Type ◽  
Parallel Performance ◽  
Element Method

Purpose This paper aims to propose a parallel-in-space-time finite-element method (FEM) for transient motor starting analyses. Although the domain decomposition method (DDM) is suitable for solving large-scale problems and the parallel-in-time (PinT) integration method such as Parareal and time domain parallel FEM (TDPFEM) is effective for problems with a large number of time steps, their parallel performances get saturated as the number of processes increases. To overcome the difficulty, the hybrid approach in which both the DDM and PinT integration methods are used is investigated in a highly parallel computing environment. Design/methodology/approach First, the parallel performances of the DDM, Parareal and TDPFEM were compared because the scalability of these methods in highly parallel computation has not been deeply discussed. Then, the combination of the DDM and Parareal was investigated as a parallel-in-space-time FEM. The effectiveness of the developed method was demonstrated in transient starting analyses of induction motors. Findings The combination of Parareal with the DDM can improve the parallel performance in the case where the parallel performance of the DDM, TDPFEM or Parareal is saturated in highly parallel computation. In the case where the number of unknowns is large and the number of available processes is limited, the use of DDM is the most effective from the standpoint of computational cost. Originality/value This paper newly develops the parallel-in-space-time FEM and demonstrates its effectiveness in nonlinear magnetoquasistatic field analyses of electric machines. This finding is significantly important because a new direction of parallel computing techniques and great potential for its further development are clarified.

scholarly journals NIHAO project II: halo shape, phase-space density and velocity distribution of dark matter in galaxy formation simulations

2016 ◽  
Vol 462 (1) ◽  
pp. 663-680 ◽  
Author(s):  
Iryna Butsky ◽  
Andrea V. Macciò ◽  
Aaron A. Dutton ◽  
Liang Wang ◽  
Aura Obreja ◽  
...  
Keyword(s):  
Dark Matter ◽  
Phase Space ◽  
Velocity Distribution ◽  
Galaxy Formation ◽  
Phase Space Density ◽  
Space Density

Boundary Element Parallel Computation for 3D Elastostatics Using CUDA

2011 ◽  
Author(s):  
Yingjun Wang ◽  
Qifu Wang ◽  
Gang Wang ◽  
Yunbao Huang ◽  
Yixiong Wei
Keyword(s):  
Boundary Element ◽  
Parallel Computation ◽  
Computer Aided Design ◽  
Graphics Processing Unit ◽  
Computation Method ◽  
Processing Unit ◽  
Element Analysis ◽  
Integral Methods ◽  
Cad Models ◽  
Element Method

Finite Element Method (FEM) is pervasively used in most of 3D elastostatic numerical simulations, in which Computer Aided Design (CAD) models need to be converted into mesh models first and then enriched with semantic data (e.g. material parameters, boundary conditions). The interaction between CAD models and FEM models stated above is very intensive. Boundary Element Method (BEM) has been used gradually instead of FEM in recent years because of its advantage in meshing. BEM can reduce the dimensionality of the problem by one so that the complexity in mesh generation can be decreased greatly. In this paper, we present a Boundary Element parallel computation method for 3D elastostatics. The parallel computation runs on Graphics Processing Unit (GPU) using Computing Unified Device Architecture (CUDA). Three major components are included in such method: (1) BEM theory in 3D elastostatics and the boundary element coefficient integral methods, (2) the parallel BEM algorithm using CUDA, and (3) comparison the parallel BEM using CUDA with conventional BEM and FEM respectively by examples. The dimension reduction characteristics of BEM can dispose the 3D elastostatic problem by 2D meshes, therefore we develop a new faceting function to make the ACIS facet meshes suitable for Boundary Element Analysis (BEA). The examples show that the GPU parallel algorithm in this paper can accelerate BEM computation about 40 times.

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