610 research outputs found
Multi density DBSCAN
Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clusters with arbitrary shape and good efficiency on large databases.DBSCAN clustering algorithm relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape. DBSCAN requires only one input parameter and supports the user in determining an appropriate value for it. DBSCAN cannot find clusters based on difference in densities. We extend the DBSCAN algorithm so that it can also detect clusters that differ in densities and without the need to input the value of Eps because our algorithm can find the appropriate value for each cluster individually by replacing Eps by Local
Parallel Unsmoothed Aggregation Algebraic Multigrid Algorithms on GPUs
We design and implement a parallel algebraic multigrid method for isotropic
graph Laplacian problems on multicore Graphical Processing Units (GPUs). The
proposed AMG method is based on the aggregation framework. The setup phase of
the algorithm uses a parallel maximal independent set algorithm in forming
aggregates and the resulting coarse level hierarchy is then used in a K-cycle
iteration solve phase with a -Jacobi smoother. Numerical tests of a
parallel implementation of the method for graphics processors are presented to
demonstrate its effectiveness.Comment: 18 pages, 3 figure
Re-entrant ferroelectricity in liquid crystals
The ferroelectric (Sm C) -- antiferroelectric (Sm C) -- reentrant
ferroelectric (re Sm C) phase temperature sequence was observed for system
with competing synclinic - anticlinic interactions. The basic properties of
this system are as follows (1) the Sm C phase is metastable in temperature
range of the Sm C stability (2) the double inversions of the helix
handedness at Sm C -- Sm C and Sm C% -- re-Sm C phase
transitions were found (3) the threshold electric field that is necessary to
induce synclinic ordering in the Sm C phase decreases near both Sm
C -- Sm C and Sm C -- re-Sm C phase boundaries, and it has
maximum in the middle of the Sm C stability region. All these properties
are properly described by simple Landau model that accounts for nearest
neighboring layer steric interactions and quadrupolar ordering only.Comment: 10 pages, 5 figures, submitted to PR
Asynchronous Graph Pattern Matching on Multiprocessor Systems
Pattern matching on large graphs is the foundation for a variety of
application domains. Strict latency requirements and continuously increasing
graph sizes demand the usage of highly parallel in-memory graph processing
engines that need to consider non-uniform memory access (NUMA) and concurrency
issues to scale up on modern multiprocessor systems. To tackle these aspects,
graph partitioning becomes increasingly important. Hence, we present a
technique to process graph pattern matching on NUMA systems in this paper. As a
scalable pattern matching processing infrastructure, we leverage a
data-oriented architecture that preserves data locality and minimizes
concurrency-related bottlenecks on NUMA systems. We show in detail, how graph
pattern matching can be asynchronously processed on a multiprocessor system.Comment: 14 Pages, Extended version for ADBIS 201
Partitioning Complex Networks via Size-constrained Clustering
The most commonly used method to tackle the graph partitioning problem in
practice is the multilevel approach. During a coarsening phase, a multilevel
graph partitioning algorithm reduces the graph size by iteratively contracting
nodes and edges until the graph is small enough to be partitioned by some other
algorithm. A partition of the input graph is then constructed by successively
transferring the solution to the next finer graph and applying a local search
algorithm to improve the current solution.
In this paper, we describe a novel approach to partition graphs effectively
especially if the networks have a highly irregular structure. More precisely,
our algorithm provides graph coarsening by iteratively contracting
size-constrained clusterings that are computed using a label propagation
algorithm. The same algorithm that provides the size-constrained clusterings
can also be used during uncoarsening as a fast and simple local search
algorithm.
Depending on the algorithm's configuration, we are able to compute partitions
of very high quality outperforming all competitors, or partitions that are
comparable to the best competitor in terms of quality, hMetis, while being
nearly an order of magnitude faster on average. The fastest configuration
partitions the largest graph available to us with 3.3 billion edges using a
single machine in about ten minutes while cutting less than half of the edges
than the fastest competitor, kMetis
Correlated defects, metal-insulator transition, and magnetic order in ferromagnetic semiconductors
The effect of disorder on transport and magnetization in ferromagnetic III-V
semiconductors, in particular (Ga,Mn)As, is studied theoretically. We show that
Coulomb-induced correlations of the defect positions are crucial for the
transport and magnetic properties of these highly compensated materials. We
employ Monte Carlo simulations to obtain the correlated defect distributions.
Exact diagonalization gives reasonable results for the spectrum of valence-band
holes and the metal-insulator transition only for correlated disorder. Finally,
we show that the mean-field magnetization also depends crucially on defect
correlations.Comment: 4 pages RevTeX4, 5 figures include
Multiobjective hypergraph-partitioning algorithms for cut and maximum subdomain-degree minimization
dráma 3 felvonásban - írta Don Pedro Calderon de la Barka - spanyol eredetiből forditotta Győry Vilmos, a Kisfaludy-társaság tagja - zenéjét irta Nikolits Sándor - rendező: Mándoki.
Debreczeni Szinház. Csütörtökön, 1874. márczius 19-kén adatik. Calderon drámája itt másodszor.Debreceni Egyetem Egyetemi és Nemzeti Könyvtá
Optimal, scalable forward models for computing gravity anomalies
We describe three approaches for computing a gravity signal from a density
anomaly. The first approach consists of the classical "summation" technique,
whilst the remaining two methods solve the Poisson problem for the
gravitational potential using either a Finite Element (FE) discretization
employing a multilevel preconditioner, or a Green's function evaluated with the
Fast Multipole Method (FMM). The methods utilizing the PDE formulation
described here differ from previously published approaches used in gravity
modeling in that they are optimal, implying that both the memory and
computational time required scale linearly with respect to the number of
unknowns in the potential field. Additionally, all of the implementations
presented here are developed such that the computations can be performed in a
massively parallel, distributed memory computing environment. Through numerical
experiments, we compare the methods on the basis of their discretization error,
CPU time and parallel scalability. We demonstrate the parallel scalability of
all these techniques by running forward models with up to voxels on
1000's of cores.Comment: 38 pages, 13 figures; accepted by Geophysical Journal Internationa
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