166,074 research outputs found
An intelligent allocation algorithm for parallel processing
The problem of allocating nodes of a program graph to processors in a parallel processing architecture is considered. The algorithm is based on critical path analysis, some allocation heuristics, and the execution granularity of nodes in a program graph. These factors, and the structure of interprocessor communication network, influence the allocation. To achieve realistic estimations of the executive durations of allocations, the algorithm considers the fact that nodes in a program graph have to communicate through varying numbers of tokens. Coarse and fine granularities have been implemented, with interprocessor token-communication duration, varying from zero up to values comparable to the execution durations of individual nodes. The effect on allocation of communication network structures is demonstrated by performing allocations for crossbar (non-blocking) and star (blocking) networks. The algorithm assumes the availability of as many processors as it needs for the optimal allocation of any program graph. Hence, the focus of allocation has been on varying token-communication durations rather than varying the number of processors. The algorithm always utilizes as many processors as necessary for the optimal allocation of any program graph, depending upon granularity and characteristics of the interprocessor communication network
Block designs for experiments with non-normal response
Many experiments measure a response that cannot be adequately described by a linear model withnormally distributed errors and are often run in blocks of homogeneous experimental units. Wedevelop the first methods of obtaining efficient block designs for experiments with an exponentialfamily response described by a marginal model fitted via Generalized Estimating Equations. Thismethodology is appropriate when the blocking factor is a nuisance variable as, for example, occursin industrial experiments. A D-optimality criterion is developed for finding designs robust to thevalues of the marginal model parameters and applied using three strategies: unrestricted algorithmicsearch, use of minimum-support designs, and blocking of an optimal design for the correspondingGeneralized Linear Model. Designs obtained from each strategy are critically compared and shownto be much more efficient than designs that ignore the blocking structure. The designs are comparedfor a range of values of the intra-block working correlation and for exchangeable, autoregressive andnearest neighbor structures. An analysis strategy is developed for a binomial response that allows es-timation from experiments with sparse data, and its efectiveness demonstrated. The design strategiesare motivated and demonstrated through the planning of an experiment from the aeronautics industr
Supercomputer optimizations for stochastic optimal control applications
Supercomputer optimizations for a computational method of solving stochastic, multibody, dynamic programming problems are presented. The computational method is valid for a general class of optimal control problems that are nonlinear, multibody dynamical systems, perturbed by general Markov noise in continuous time, i.e., nonsmooth Gaussian as well as jump Poisson random white noise. Optimization techniques for vector multiprocessors or vectorizing supercomputers include advanced data structures, loop restructuring, loop collapsing, blocking, and compiler directives. These advanced computing techniques and superconducting hardware help alleviate Bellman's curse of dimensionality in dynamic programming computations, by permitting the solution of large multibody problems. Possible applications include lumped flight dynamics models for uncertain environments, such as large scale and background random aerospace fluctuations
Dominant transport pathways in an atmospheric blocking event
A Lagrangian flow network is constructed for the atmospheric blocking of
eastern Europe and western Russia in summer 2010. We compute the most probable
paths followed by fluid particles which reveal the {\it Omega}-block skeleton
of the event. A hierarchy of sets of highly probable paths is introduced to
describe transport pathways when the most probable path alone is not
representative enough. These sets of paths have the shape of narrow coherent
tubes flowing close to the most probable one. Thus, even when the most probable
path is not very significant in terms of its probability, it still identifies
the geometry of the transport pathways.Comment: Appendix added with path calculations for a simple kinematic model
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Optimal Algorithms for Near-Hitless Network Restoration via Diversity Coding
Diversity coding is a network restoration technique which offers near-hitless
restoration, while other state-of-the art techniques are significantly slower.
Furthermore, the extra spare capacity requirement of diversity coding is
competitive with the others. Previously, we developed heuristic algorithms to
employ diversity coding structures in networks with arbitrary topology. This
paper presents two algorithms to solve the network design problems using
diversity coding in an optimal manner. The first technique pre-provisions
static traffic whereas the second technique carries out the dynamic
provisioning of the traffic on-demand. In both cases, diversity coding results
in smaller restoration time, simpler synchronization, and much reduced
signaling complexity than the existing techniques in the literature. A Mixed
Integer Programming (MIP) formulation and an algorithm based on Integer Linear
Programming (ILP) are developed for pre-provisioning and dynamic provisioning,
respectively. Simulation results indicate that diversity coding has
significantly higher restoration speed than Shared Path Protection (SPP) and
p-cycle techniques. It requires more extra capacity than the p-cycle technique
and SPP. However, the increase in the total capacity is negligible compared to
the increase in the restoration speed.Comment: An old version of this paper is submitted to IEEE Globecom 2012
conferenc
Optimal designs for conjoint experiments.
Design; Model-sensitive; Optimal; Optimal design; Data;
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