Modern numerical process simulators are becoming increasingly complicated in both physical models and domain shape. Grid generation is difficult for these simulators because of the inherent transient nature of the problems being solved. This paper addresses adaptive grid refinement for use in solving diffusion problems. It additionally investigates higher order approximations to the discretized...

For the parallel computation of partial differential equations, one key is the grid partitioning. It requires that each process owns the same amount of computations, and also, the partitioning quality should be proper to reduce the communications among processes. When calculating the partial differential equations using adaptive finite element methods, the grid and the basis functions adjust in...

Grid computing systems offer large scale computing resources and can help carry out computation intensive jobs with improved efficiency and reduced business costs. Due to the heterogeneity of the computation and communication resources in these grid systems, efficient allocation of user jobs to resources is essential for reducing the execution time and costs. In this paper, we study an adaptive...

A computational grid is a highly dynamic and distributed environment. Unlike tightly-coupled parallel computing environment, high performance computing on the grid is complicated by the heterogeneous computational performances of each node, possible node unavailability, unpredictable node behavior, and unreliable network connectivity. Compared to a static scheduling, an adaptive scheduling mech...