Optimal pre-scheduling of problem remappings

A large class of scientific computational problems can be characterized as a sequence of steps where a significant amount of computation occurs each step, but the work performed at each step is not necessarily identical. Two good examples of this type of computation are: (1) regridding methods which change the problem discretization during the course of the computation, and (2) methods for solving sparse triangular systems of linear equations. Recent work has investigated a means of mapping such computations onto parallel processors; the method defines a family of static mappings with differing degrees of importance placed on the conflicting goals of good load balance and low communication/synchronization overhead. The performance tradeoffs are controllable by adjusting the parameters of the mapping method. To achieve good performance it may be necessary to dynamically change these parameters at run-time, but such changes can impose additional costs. If the computation's behavior can be determined prior to its execution, it can be possible to construct an optimal parameter schedule using a low-order-polynomial-time dynamic programming algorithm. Since the latter can be expensive, the performance is studied of the effect of a linear-time scheduling heuristic on one of the model problems, and it is shown to be effective and nearly optimal

Automated problem scheduling and reduction of synchronization delay effects

It is anticipated that in order to make effective use of many future high performance architectures, programs will have to exhibit at least a medium grained parallelism. A framework is presented for partitioning very sparse triangular systems of linear equations that is designed to produce favorable preformance results in a wide variety of parallel architectures. Efficient methods for solving these systems are of interest because: (1) they provide a useful model problem for use in exploring heuristics for the aggregation, mapping and scheduling of relatively fine grained computations whose data dependencies are specified by directed acrylic graphs, and (2) because such efficient methods can find direct application in the development of parallel algorithms for scientific computation. Simple expressions are derived that describe how to schedule computational work with varying degrees of granularity. The Encore Multimax was used as a hardware simulator to investigate the performance effects of using the partitioning techniques presented in shared memory architectures with varying relative synchronization costs

Multicore-optimized wavefront diamond blocking for optimizing stencil updates

The importance of stencil-based algorithms in computational science has focused attention on optimized parallel implementations for multilevel cache-based processors. Temporal blocking schemes leverage the large bandwidth and low latency of caches to accelerate stencil updates and approach theoretical peak performance. A key ingredient is the reduction of data traffic across slow data paths, especially the main memory interface. In this work we combine the ideas of multi-core wavefront temporal blocking and diamond tiling to arrive at stencil update schemes that show large reductions in memory pressure compared to existing approaches. The resulting schemes show performance advantages in bandwidth-starved situations, which are exacerbated by the high bytes per lattice update case of variable coefficients. Our thread groups concept provides a controllable trade-off between concurrency and memory usage, shifting the pressure between the memory interface and the CPU. We present performance results on a contemporary Intel processor

Absorbing new subjects: holography as an analog of photography

I discuss the early history of holography and explore how perceptions, applications, and forecasts of the subject were shaped by prior experience. I focus on the work of Dennis Gabor (1900–1979) in England,Yury N. Denisyuk (b. 1924) in the Soviet Union, and Emmett N. Leith (1927–2005) and Juris Upatnieks (b. 1936) in the United States. I show that the evolution of holography was simultaneously promoted and constrained by its identification as an analog of photography, an association that influenced its assessment by successive audiences of practitioners, entrepreneurs, and consumers. One consequence is that holography can be seen as an example of a modern technical subject that has been shaped by cultural influences more powerfully than generally appreciated. Conversely, the understanding of this new science and technology in terms of an older one helps to explain why the cultural effects of holography have been more muted than anticipated by forecasters between the 1960s and 1990s

A FAST ITERATIVE METHOD FOR EIKONAL EQUATIONS

In this paper we propose a novel computational technique to solve the Eikonal equation efficiently on parallel architectures. The proposed method manages the list of active nodes and iteratively updates the solutions on those nodes until they converge. Nodes are added to or removed from the list based on a convergence measure, but the management of this list does not entail an extra burden of expensive ordered data structures or special updating sequences. The proposed method has suboptimal worst-case performance but, in practice, on real and synthetic datasets, runs faster than guaranteed-optimal alternatives. Furthermore, the proposed method uses only local, synchronous updates and therefore has better cache coherency, is simple to implement, and scales efficiently on parallel architectures. This paper describes the method, proves its consistency, gives a performance analysis that compares the proposed method against the state-of-the-art Eikonal solvers, and describes the implementation on a single instruction multiple datastream (SIMD) parallel architecture.open393

Modern optical astronomy: technology and impact of interferometry

The present `state of the art' and the path to future progress in high spatial resolution imaging interferometry is reviewed. The review begins with a treatment of the fundamentals of stellar optical interferometry, the origin, properties, optical effects of turbulence in the Earth's atmosphere, the passive methods that are applied on a single telescope to overcome atmospheric image degradation such as speckle interferometry, and various other techniques. These topics include differential speckle interferometry, speckle spectroscopy and polarimetry, phase diversity, wavefront shearing interferometry, phase-closure methods, dark speckle imaging, as well as the limitations imposed by the detectors on the performance of speckle imaging. A brief account is given of the technological innovation of adaptive-optics (AO) to compensate such atmospheric effects on the image in real time. A major advancement involves the transition from single-aperture to the dilute-aperture interferometry using multiple telescopes. Therefore, the review deals with recent developments involving ground-based, and space-based optical arrays. Emphasis is placed on the problems specific to delay-lines, beam recombination, polarization, dispersion, fringe-tracking, bootstrapping, coherencing and cophasing, and recovery of the visibility functions. The role of AO in enhancing visibilities is also discussed. The applications of interferometry, such as imaging, astrometry, and nulling are described. The mathematical intricacies of the various `post-detection' image-processing techniques are examined critically. The review concludes with a discussion of the astrophysical importance and the perspectives of interferometry.Comment: 65 pages LaTeX file including 23 figures. Reviews of Modern Physics, 2002, to appear in April issu

Unifying the spatial epidemiology and molecular evolution of emerging epidemics

We introduce a conceptual bridge between the previously unlinked fields of phylogenetics and mathematical spatial ecology, which enables the spatial parameters of an emerging epidemic to be directly estimated from sampled pathogen genome sequences. By using phylogenetic history to correct for spatial autocorrelation, we illustrate how a fundamental spatial variable, the diffusion coefficient, can be estimated using robust nonparametric statistics, and how heterogeneity in dispersal can be readily quantified. We apply this framework to the spread of the West Nile virus across North America, an important recent instance of spatial invasion by an emerging infectious disease. We demonstrate that the dispersal of West Nile virus is greater and far more variable than previously measured, such that its dissemination was critically determined by rare, long-range movements that are unlikely to be discerned during field observations. Our results indicate that, by ignoring this heterogeneity, previous models of the epidemic have substantially overestimated its basic reproductive number. More generally, our approach demonstrates that easily obtainable genetic data can be used to measure the spatial dynamics of natural populations that are otherwise difficult or costly to quantify

Wave propagation: laser propagation and quantum transport

2021 Spring.Includes bibliographical references.This dissertation consists of two independent projects, where wave propagation is the common theme. The first project considers modeling the propagation of laser light through the atmosphere using an approximation procedure we call the variational scaling law (VSL). We begin by introducing the Helmholtz equation and the paraxial approximation to the Helmholtz equation, which is the starting point of the VSL. The approximation method is derived by pairing the variational formulation of the paraxial Helmholtz equation with a generalized Gaussian ansatz which depends on the laser beam parameters. The VSL is a system of stochastic ODEs that describe the evolution of the Gaussian beam parameters. We will conclude with a numerical comparison between the variational scaling law and the paraxial Helmholtz equation. Through exploring numerical examples for increasing strengths of atmospheric turbulence, we show the VSL provides, at least, an order-one approximation to the paraxial Helmholtz equation. The second project focuses on quantum transport by numerically studying the quantum Liouville equation (QLE) equipped with the BGK-collision operator. The collision operator is a relaxation-type operator which locally relaxes the solution towards a local quantum equilibrium. This equilibrium operator is nonlinear and is obtained by solving a moment problem under a local density constraint using the quantum entropy minimization principle introduced by Degond and Ringhofer in \cite{degondringhofer}. A Strang splitting scheme is defined for the QLE in which the collision and transport of particles is treated separately. It is proved that the numerical scheme is well-defined and convergent in-time. The splitting scheme for the QLE is applied in a numerical study of electron transport in different collision regimes by comparing the QLE with the ballistic Liouville equation and the quantum drift-diffusion model. The quantum drift-diffusion model is an example of a quantum diffusion model which is derived from the QLE through a diffusive limit. Finally, it is numerically verified that the QLE converges to the solution to the quantum drift-diffusion equation in the long-time limit