A local relaxation method for solving PDEs on mesh-connected arrays
SIAM Journal on Scientific and Statistical Computing
Dynamic load balancing for distributed memory multiprocessors
Journal of Parallel and Distributed Computing
Load balancing and Poisson equation in a graph
Concurrency: Practice and Experience
Optimal parameters for load balancing using the diffusion method in k-ary n-cube networks
Information Processing Letters
An improved spectral graph partitioning algorithm for mapping parallel computations
SIAM Journal on Scientific Computing
Efficient schemes for nearest neighbor load balancing
Parallel Computing - Special issue on parallelization techniques for numerical modelling
Load Balancing in Parallel Computers: Theory and Practice
Load Balancing in Parallel Computers: Theory and Practice
Accelerated diffusion algorithms for dynamic load balancing
Information Processing Letters
Toward Optimal Diffusion Matrices
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Iterative Load Balancing Schemes for Air Pollution Models
LSSC '01 Proceedings of the Third International Conference on Large-Scale Scientific Computing-Revised Papers
Engineering Diffusive Load Balancing Algorithms Using Experiments
IRREGULAR '97 Proceedings of the 4th International Symposium on Solving Irregularly Structured Problems in Parallel
Fast diffusion load balancing algorithms on torus graphs
Euro-Par'06 Proceedings of the 12th international conference on Parallel Processing
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In this paper, we simulate the performance of a load balancing scheme. In particular, we study the application of the Extrapolated Diffusion(EDF) method for the efficient parallelization of a simple atmospheric model. It involves the numerical solution of the steady state Navier-Stokes(NS) equations in the horizontal plane and random load values, corresponding to the physics computations, in the vertical plane. For the numerical solution of NS equations, we use the local Modified Successive Overrelaxation (LMSOR) method with local parameters thus avoiding the additional cost caused by the global communication of the involved parameter ω in the classical SOR method.We have implemented an efficient domain decomposition technique by using a larger number of processors in the areas of the domain with heavier work load. With our balancing scheme, a gain of approximately 45% in execution time is achieved, in certain cases.