On domain decomposition with space filling curves for the parallel solution of the coupled Maxwell/Vlasov equations

Space filling Curves (SFCs) are increasingly used for combinatorial scientific computing and in particular for designing fast domain decomposition (partitioning) methods. In the context of parallel particle simulations for solving the system of Maxwell/Vlasov equations with a coupled FE/PIC (Finite Element/Particle-In-Cell) unstructured mesh based solver, one has to deal with a two-constraint partitioning problem. Moreover, this problem has to be solved several times during the simulation. Therefore, a fast and scalable partitioning problem is required. For this purpose, we propose here a new SFC based method which is well adapted to multi-constraint partitioning problems. This method is compared to graph based partitioning methods from the widely used MeTiS tool. Experimental results show that the proposed SFC based method is at least 100 times faster than MeTiS to the disadvantage of edge-cuts that are between 2 to 4 times worse than those achieved by the MeTiS methods

Approximations for the general block distribution of a matrix

AbstractThe general block distribution of a matrix is a rectilinear partition of the matrix into orthogonal blocks such that the maximum sum of the elements within a single block is minimized. This corresponds to partitioning the matrix onto parallel processors so as to minimize processor load while maintaining regular communication patterns. Applications of the problem include various parallel sparse matrix computations, compilers for high-performance languages, particle in cell computations, video and image compression, and simulations associated with a communication network. We analyze the performance guarantee of a natural and practical heuristic based on iterative refinement, which has previously been shown to give good empirical results. When p2 is the number of blocks, we show that the tight performance ratio is θ(p). When the matrix has rows of large cost, the details of the objective function of the algorithm are shown to be important, since a naive implementation can lead to a Ω(p) performance ratio. Extensions to more general cost functions, higher-dimensional arrays, and randomized initial configurations are also considered

Approximations for the General Block Distribution of a Matrix

The general block distribution of a matrix is a rectilinear partition of the matrix into orthogonal blocks such that the maximum sum of the elements within a single block is minimized. This corresponds to partitioning the matrix onto parallel processors so as to minimize processor load while maintaining regular communication patterns. Applications of the problem include various parallel sparse matrix computations, compilers for high-performance languages, particle in cell computations, video and image compression, and simulations associated with a communication network. We analyze the performance guarantee of a natural and practical heuristic based on iterative refinement, which has previously been shown to give good empirical results. When p 2 is the number of blocks, we show that the tight performance ratio is `( p p). When the matrix has rows of large cost, the details of the objective function of the algorithm are shown to be important, since a naive implementation c..