Dynamic load balancing for distributed memory multiprocessors
Journal of Parallel and Distributed Computing
Task allocation onto a hypercube by recursive mincut bipartitioning
Journal of Parallel and Distributed Computing
An improved spectral graph partitioning algorithm for mapping parallel computations
SIAM Journal on Scientific Computing
Parallel Incremental Graph Partitioning
IEEE Transactions on Parallel and Distributed Systems
Parallel dynamic graph partitioning for adaptive unstructured meshes
Journal of Parallel and Distributed Computing - Special issue on dynamic load balancing
Multilevel diffusion schemes for repartitioning of adaptive meshes
Journal of Parallel and Distributed Computing - Special issue on dynamic load balancing
A parallel algorithm for multilevel graph partitioning and sparse matrix ordering
Journal of Parallel and Distributed Computing
Multilevel k-way partitioning scheme for irregular graphs
Journal of Parallel and Distributed Computing
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
ICPP '98 Proceedings of the 1998 International Conference on Parallel Processing
Geometric mesh partitioning: implementation and experiments
IPPS '95 Proceedings of the 9th International Symposium on Parallel Processing
IRREGULAR '98 Proceedings of the 5th International Symposium on Solving Irregularly Structured Problems in Parallel
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The finite element method is widely applied to many domains, such as engineering, atmology, oceanography, biology, etc. The major drawback of the finite element method is that its execution takes a lot of time and memory spaces. Due to the computation-intensiveness and computation-locality properties, we can use the parallel processing method to improve the performance of the finite element method on distributed memory computing environments. However, it is quite difficult to program the finite element method on a distributed memory-computing environment. Therefore, the development of a front-end parallel partial differential equations solver generation system is important. In this paper, we want to develop a front-end parallel partial differential equations solver generation system based on the World Wide Web on a distributed-memory computing environment, such as a PC cluster, a workstation cluster, etc. With the system, users who want to use parallel computers to solver partial differential equations can use web browser to input data and parameters. The system will automatically generate the corresponding parallel codes and execute the codes on the distributed memory-computing environment. The execution result will be shown on the web browser. User can also download the results.