This whitepaper describes the load-balancing performance issues that are observed and tackled during the petascaling of a space plasma simulation code developed at the Finnish Meteorological Institute (FMI). It models the communication pattern as a hypergraph, and partitions the computational grid using the parallel hypergraph partitioning scheme (PHG) of the Zoltan partitioning framework. The ...

Parallel structured adaptive mesh refinement methods decrease the execution time and memory requirements of partial differential equation solvers. These methods result in an adaptive and dynamic grid hierarchy that repeatedly needs to be re-partitioned and distributed over the processors. No single partitioning algorithm can consistently construct high-quality partitionings for all possible gri...

More and more vector-based cellular automata (VCA) models have been built to leverage parallel computing to model rapidly changing cities and urban regions. During parallel simulation, common task decomposition methods based on space partitioning, e.g., grid partitioning (GRID) and recursive binary space partitioning (BSP), do not work well given the heterogeneity of VCA parcel tasks. In this p...

This paper describes a block data partitioning algorithm suited to parallel processing in a heterogeneous network environment where some of the processors have the same performance capacity. Grid problems are particularly suited to block data partitioning schemes where communication cost is reduced by locating a grid point and its neighbors on the same physical processor whenever possible. Our ...

Dynamic load balancing is considered in the context of adaptive multilevel methods for partial differential equations on distributed memory multiprocessors. An approach that periodically repartitions the grid is taken. The important properties of a partitioning algorithm are presented and discussed in this context. A partitioning algorithm based on the refinement tree of the adaptive grid is pr...

Clustering is a technique used in data mining that groups similar objects into one cluster, while dissimilar objects are grouped into different clusters. The clustering techniques can be categorized into partitioning methods, hierarchical methods, density-based methods and grid-based methods. The different partitioning methods studied here are k-means and k-medoids. The different hierarchical t...

Three Neuro-Fuzzy Inference Systems (ANFIS) including Grid Partitioning (GP), Subtractive Clustering (SCM) and Fuzzy C-means clustering Methods (FCM) have been used to predict the groutability of granular soil samples with cement-based grouts. Laboratory data from related available in litterature was used for the tests. Several parameters were taken into account in the proposed models: water:ce...

Clustering is one of the important data mining techniques which discover clusters in many real-world data sets. Recent algorithms attempt to find clusters in subspaces of high dimensional data. Density based clustering algorithms uses grid structure for partitioning each dimensions into intervals (bins) which yields good computation and quality results on large databases. In this paper, we prop...

Clustering is one of the important data mining techniques which discover clusters in many real-world data sets. Recent algorithms attempt to find clusters in subspaces of high dimensional data. Density based clustering algorithms uses grid structure for partitioning each dimensions into intervals (bins) which yields good computation and quality results on large databases. In this paper, we prop...