Load Balancing for Parallel Computations with the Finite Element Method

Authors

  • José Luis González García GWDG - Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen, Göttingen, Lower Saxony.
  • Ramin Yahyapour GWDG - Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen, Göttingen, Lower Saxony
  • Andrei Tchernykh CICESE Research Center

DOI:

https://doi.org/10.13053/cys-17-3-1534

Keywords:

Load balancing, FEM, HPC efficiency.

Abstract

In this paper, we give an overview of efforts to improve current techniques of load-balancing and efficiency of finite element method (FEM) computations on large-scale parallel machines and introduce a multilevel load balancer to improve the local load imbalance. FEM is used to numerically approximate solutions of partial differential equations (PDEs) as well as integral equations. The PDEs domain is discretized into a mesh of information and usually solved using iterative methods. Distributing the mesh among the processors in a parallel computer, also known as the mesh-partitioning problem, was shown to be NP-complete. Many efforts are focused on graph-partitioning to parallelize and distribute the mesh of information. Data partitioning is important to efficiently execute applications in distributed systems. To address this problem, a variety of general-purpose libraries and techniques have been developed providing great effectiveness. But the load-balancing problem is not yet well solved. Today’s large simulations require new techniques to scale on clusters of thousands of processors and to be resource aware due the increasing use of heterogeneous computing architectures as found in many-core computer systems. Existing libraries and algorithms need to be enhanced to support more complex applications and hardware architectures. We present trends in this field and discuss new ideas and approaches that take into account the new emerging requirements.

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Published

2013-10-01

Issue

Vol. 17 No. 3 (2013): 17(3) July-September 2013

Section

Articles

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