10,296 research outputs found
Recursive Algorithms for Distributed Forests of Octrees
The forest-of-octrees approach to parallel adaptive mesh refinement and
coarsening (AMR) has recently been demonstrated in the context of a number of
large-scale PDE-based applications. Although linear octrees, which store only
leaf octants, have an underlying tree structure by definition, it is not often
exploited in previously published mesh-related algorithms. This is because the
branches are not explicitly stored, and because the topological relationships
in meshes, such as the adjacency between cells, introduce dependencies that do
not respect the octree hierarchy. In this work we combine hierarchical and
topological relationships between octree branches to design efficient recursive
algorithms.
We present three important algorithms with recursive implementations. The
first is a parallel search for leaves matching any of a set of multiple search
criteria. The second is a ghost layer construction algorithm that handles
arbitrarily refined octrees that are not covered by previous algorithms, which
require a 2:1 condition between neighboring leaves. The third is a universal
mesh topology iterator. This iterator visits every cell in a domain partition,
as well as every interface (face, edge and corner) between these cells. The
iterator calculates the local topological information for every interface that
it visits, taking into account the nonconforming interfaces that increase the
complexity of describing the local topology. To demonstrate the utility of the
topology iterator, we use it to compute the numbering and encoding of
higher-order nodal basis functions.
We analyze the complexity of the new recursive algorithms theoretically, and
assess their performance, both in terms of single-processor efficiency and in
terms of parallel scalability, demonstrating good weak and strong scaling up to
458k cores of the JUQUEEN supercomputer.Comment: 35 pages, 15 figures, 3 table
Equal-time correlation function for directed percolation
We suggest an equal-time n-point correlation function for systems in the
directed percolation universality class which is well defined in all phases and
independent of initial conditions. It is defined as the probability that all
points are connected with a common ancestor in the past by directed paths.Comment: LaTeX, 12 pages, 8 eps figure
Optimal Resource Allocation in Random Networks with Transportation Bandwidths
We apply statistical physics to study the task of resource allocation in
random sparse networks with limited bandwidths for the transportation of
resources along the links. Useful algorithms are obtained from recursive
relations. Bottlenecks emerge when the bandwidths are small, causing an
increase in the fraction of idle links. For a given total bandwidth per node,
the efficiency of allocation increases with the network connectivity. In the
high connectivity limit, we find a phase transition at a critical bandwidth,
above which clusters of balanced nodes appear, characterised by a profile of
homogenized resource allocation similar to the Maxwell's construction.Comment: 28 pages, 11 figure
Path storage in the particle filter
This article considers the problem of storing the paths generated by a
particle filter and more generally by a sequential Monte Carlo algorithm. It
provides a theoretical result bounding the expected memory cost by where is the time horizon, is the number of particles and
is a constant, as well as an efficient algorithm to realise this. The
theoretical result and the algorithm are illustrated with numerical
experiments.Comment: 9 pages, 5 figures. To appear in Statistics and Computin
Parallel resampling in the particle filter
Modern parallel computing devices, such as the graphics processing unit
(GPU), have gained significant traction in scientific and statistical
computing. They are particularly well-suited to data-parallel algorithms such
as the particle filter, or more generally Sequential Monte Carlo (SMC), which
are increasingly used in statistical inference. SMC methods carry a set of
weighted particles through repeated propagation, weighting and resampling
steps. The propagation and weighting steps are straightforward to parallelise,
as they require only independent operations on each particle. The resampling
step is more difficult, as standard schemes require a collective operation,
such as a sum, across particle weights. Focusing on this resampling step, we
analyse two alternative schemes that do not involve a collective operation
(Metropolis and rejection resamplers), and compare them to standard schemes
(multinomial, stratified and systematic resamplers). We find that, in certain
circumstances, the alternative resamplers can perform significantly faster on a
GPU, and to a lesser extent on a CPU, than the standard approaches. Moreover,
in single precision, the standard approaches are numerically biased for upwards
of hundreds of thousands of particles, while the alternatives are not. This is
particularly important given greater single- than double-precision throughput
on modern devices, and the consequent temptation to use single precision with a
greater number of particles. Finally, we provide auxiliary functions useful for
implementation, such as for the permutation of ancestry vectors to enable
in-place propagation.Comment: 21 pages, 6 figure
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