7,278 research outputs found
Parallel Batch-Dynamic Graph Connectivity
In this paper, we study batch parallel algorithms for the dynamic
connectivity problem, a fundamental problem that has received considerable
attention in the sequential setting. The most well known sequential algorithm
for dynamic connectivity is the elegant level-set algorithm of Holm, de
Lichtenberg and Thorup (HDT), which achieves amortized time per
edge insertion or deletion, and time per query. We
design a parallel batch-dynamic connectivity algorithm that is work-efficient
with respect to the HDT algorithm for small batch sizes, and is asymptotically
faster when the average batch size is sufficiently large. Given a sequence of
batched updates, where is the average batch size of all deletions, our
algorithm achieves expected amortized work per
edge insertion and deletion and depth w.h.p. Our algorithm
answers a batch of connectivity queries in expected
work and depth w.h.p. To the best of our knowledge, our algorithm
is the first parallel batch-dynamic algorithm for connectivity.Comment: This is the full version of the paper appearing in the ACM Symposium
on Parallelism in Algorithms and Architectures (SPAA), 201
Numerical aerodynamic simulation facility
Critical to the advancement of computational aerodynamics capability is the ability to simulate flows about three-dimensional configurations that contain both compressible and viscous effects, including turbulence and flow separation at high Reynolds numbers. Analyses were conducted of two solution techniques for solving the Reynolds averaged Navier-Stokes equations describing the mean motion of a turbulent flow with certain terms involving the transport of turbulent momentum and energy modeled by auxiliary equations. The first solution technique is an implicit approximate factorization finite-difference scheme applied to three-dimensional flows that avoids the restrictive stability conditions when small grid spacing is used. The approximate factorization reduces the solution process to a sequence of three one-dimensional problems with easily inverted matrices. The second technique is a hybrid explicit/implicit finite-difference scheme which is also factored and applied to three-dimensional flows. Both methods are applicable to problems with highly distorted grids and a variety of boundary conditions and turbulence models
Parallel stereo vision algorithm
Integrating a stereo-photogrammetric robot
head into a real-time system requires software
solutions that rapidly resolve the stereo correspondence
problem. The stereo-matcher presented in this
paper uses therefore code parallelisation and was
tested on three different processors with x87 and AVX.
The results show that a 5mega pixels colour image can
be matched in 5,55 seconds or as monochrome in 3,3
seconds
Fast Ensemble Smoothing
Smoothing is essential to many oceanographic, meteorological and hydrological
applications. The interval smoothing problem updates all desired states within
a time interval using all available observations. The fixed-lag smoothing
problem updates only a fixed number of states prior to the observation at
current time. The fixed-lag smoothing problem is, in general, thought to be
computationally faster than a fixed-interval smoother, and can be an
appropriate approximation for long interval-smoothing problems. In this paper,
we use an ensemble-based approach to fixed-interval and fixed-lag smoothing,
and synthesize two algorithms. The first algorithm produces a linear time
solution to the interval smoothing problem with a fixed factor, and the second
one produces a fixed-lag solution that is independent of the lag length.
Identical-twin experiments conducted with the Lorenz-95 model show that for lag
lengths approximately equal to the error doubling time, or for long intervals
the proposed methods can provide significant computational savings. These
results suggest that ensemble methods yield both fixed-interval and fixed-lag
smoothing solutions that cost little additional effort over filtering and model
propagation, in the sense that in practical ensemble application the additional
increment is a small fraction of either filtering or model propagation costs.
We also show that fixed-interval smoothing can perform as fast as fixed-lag
smoothing and may be advantageous when memory is not an issue
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