173,006 research outputs found
A Direct Multigrid Poisson Solver for Oct-Tree Adaptive Meshes
We describe a finite-volume method for solving the Poisson equation on
oct-tree adaptive meshes using direct solvers for individual mesh blocks. The
method is a modified version of the method presented by Huang and Greengard
(2000), which works with finite-difference meshes and does not allow for shared
boundaries between refined patches. Our algorithm is implemented within the
FLASH code framework and makes use of the PARAMESH library, permitting
efficient use of parallel computers. We describe the algorithm and present test
results that demonstrate its accuracy.Comment: 10 pages, 6 figures, accepted by the Astrophysical Journal; minor
revisions in response to referee's comments; added char
Numerical methods for option pricing.
This thesis aims to introduce some fundamental concepts underlying option valuation theory including implementation of computational tools. In many cases analytical solution for option pricing does not exist, thus the following numerical methods are used: binomial trees, Monte Carlo simulations and finite difference methods. First, an algorithm based on Hull and Wilmott is written for every method. Then these algorithms are improved in different ways. For the binomial tree both speed and memory usage is significantly improved by using only one vector instead of a whole price storing matrix. Computational time in Monte Carlo simulations is reduced by implementing a parallel algorithm (in C) which is capable of improving speed by a factor which equals the number of processors used. Furthermore, MatLab code for Monte Carlo was made faster by vectorizing simulation process. Finally, obtained option values are compared to those obtained with popular finite difference methods, and it is discussed which of the algorithms is more appropriate for which purpose
Fast Parallel Operations on Search Trees
Using (a,b)-trees as an example, we show how to perform a parallel split with
logarithmic latency and parallel join, bulk updates, intersection, union (or
merge), and (symmetric) set difference with logarithmic latency and with
information theoretically optimal work. We present both asymptotically optimal
solutions and simplified versions that perform well in practice - they are
several times faster than previous implementations
GreeM : Massively Parallel TreePM Code for Large Cosmological N-body Simulations
In this paper, we describe the implementation and performance of GreeM, a
massively parallel TreePM code for large-scale cosmological N-body simulations.
GreeM uses a recursive multi-section algorithm for domain decomposition. The
size of the domains are adjusted so that the total calculation time of the
force becomes the same for all processes. The loss of performance due to
non-optimal load balancing is around 4%, even for more than 10^3 CPU cores.
GreeM runs efficiently on PC clusters and massively-parallel computers such as
a Cray XT4. The measured calculation speed on Cray XT4 is 5 \times 10^4
particles per second per CPU core, for the case of an opening angle of
\theta=0.5, if the number of particles per CPU core is larger than 10^6.Comment: 13 pages, 11 figures, accepted by PAS
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Harmonic scheduling of linear recurrences in digital filter design
Linear difference equations involving recurrences are fundamental equations that describe many important signal processing applications. For many high sample rate digital filter applications, we need to effectively parallelize the linear difference equations used to describe digital filters - a difficult task due to the recurrences inherent in the data dependences. We present a novel approach, Harmonic Scheduling, that exploits parallelism in these recurrences beyond loop-carried dependencies, and which generates optimal schedules for parallel evaluation of linear difference equations with resource constraints. This approach also enables us to derive a parallel schedule with minimum control overhead, given an execution time with resource constraints. We also present a Harmonic Scheduling algorithm that generates optimal schedules for digital filters described by second-order difference equations with resource constraints
Computationally efficient algorithms for the two-dimensional Kolmogorov-Smirnov test
Goodness-of-fit statistics measure the compatibility of random samples against some theoretical or reference probability distribution function. The classical one-dimensional Kolmogorov-Smirnov test is a non-parametric statistic for comparing two empirical distributions which defines the largest absolute difference between the two cumulative distribution functions as a measure of disagreement. Adapting this test to more than one dimension is a challenge because there are 2^d-1 independent ways of ordering a cumulative distribution function in d dimensions. We discuss Peacock's version of the Kolmogorov-Smirnov test for two-dimensional data sets which computes the differences between cumulative distribution functions in 4n^2 quadrants. We also examine Fasano and Franceschini's variation of Peacock's test, Cooke's algorithm for Peacock's test, and ROOT's version of the two-dimensional Kolmogorov-Smirnov test. We establish a lower-bound limit on the work for computing Peacock's test of
Omega(n^2.lg(n)), introducing optimal algorithms for both this and Fasano and Franceschini's test, and show that Cooke's algorithm is not a faithful implementation of Peacock's test. We also discuss and evaluate parallel algorithms for Peacock's test
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
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