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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Learning to read aloud: A neural network approach using sparse distributed memory

An attempt to solve a problem of text-to-phoneme mapping is described which does not appear amenable to solution by use of standard algorithmic procedures. Experiments based on a model of distributed processing are also described. This model (sparse distributed memory (SDM)) can be used in an iterative supervised learning mode to solve the problem. Additional improvements aimed at obtaining better performance are suggested.

Joglekar, Umesh Dwarkanath↗
NASA NTRS1989

Some methods of encoding simple visual images for use with a sparse distributed memory, with applications to character recognition

To study the problems of encoding visual images for use with a Sparse Distributed Memory (SDM), I consider a specific class of images- those that consist of several pieces, each of which is a line segment or an arc of a circle. This class includes line drawings of characters such as letters of the alphabet. I give a method of representing a segment of an arc by five numbers in a continuous way; that is, similar arcs have similar representations. I also give methods for encoding these numbers as bit strings in an approximately continuous way. The set of possible segments and arcs may be viewed as a five-dimensional manifold M, whose structure is like a Mobious strip. An image, considered to be an unordered set of segments and arcs, is therefore represented by a set of points in M - one for each piece. I then discuss the problem of constructing a preprocessor to find the segments and arcs in these images, although a preprocessor has not been developed. I also describe a possible extension of the representation.

Jaeckel, Louis A.↗
NASA NTRS1990

An empirical investigation of sparse distributed memory using discrete speech recognition

Presented here is a step by step analysis of how the basic Sparse Distributed Memory (SDM) model can be modified to enhance its generalization capabilities for classification tasks. Data is taken from speech generated by a single talker. Experiments are used to investigate the theory of associative memories and the question of generalization from specific instances.

Danforth, Douglas G.↗
NASA NTRS1992

Kanerva's sparse distributed memory with multiple hamming thresholds

If the stored input patterns of Kanerva's Sparse Distributed Memory (SDM) are highly correlated, utilization of the storage capacity is very low compared to the case of uniformly distributed random input patterns. We consider a variation of SDM that has a better storage capacity utilization for correlated input patterns. This approach uses a separate selection threshold for each physical storage address or hard location. The selection of the hard locations for reading or writing can be done in parallel of which SDM implementations can benefit.

Pohja, Seppo↗
NASA NTRS1992

Sparse distributed memory and related models

Described here is sparse distributed memory (SDM) as a neural-net associative memory. It is characterized by two weight matrices and by a large internal dimension - the number of hidden units is much larger than the number of input or output units. The first matrix, A, is fixed and possibly random, and the second matrix, C, is modifiable. The SDM is compared and contrasted to (1) computer memory, (2) correlation-matrix memory, (3) feet-forward artificial neural network, (4) cortex of the cerebellum, (5) Marr and Albus models of the cerebellum, and (6) Albus' cerebellar model arithmetic computer (CMAC). Several variations of the basic SDM design are discussed: the selected-coordinate and hyperplane designs of Jaeckel, the pseudorandom associative neural memory of Hassoun, and SDM with real-valued input variables by Prager and Fallside. SDM research conducted mainly at the Research Institute for Advanced Computer Science (RIACS) in 1986-1991 is highlighted.

Kanerva, Pentti↗
NASA NTRS1992

Gravitational microlensing - The effect of a single lens and the impact of sparse sampling

Topics in gravitational microlensing are examined. One issue is that even in cases in which the surface mass density is quite low, microlensing events cannot be considered as caused by individual stars. Whenever a high-magnification event occurs, the quasar has crossed a caustic. These caustics, however, are complex structures even for low surface-mass densities. This is illustrated by removing a single low-mass star from an ensemble of microlenses. The magnification patterns and light curves are compared with and without this star. The differences are striking and much more complex than expected. The effect of sparse sampling on the perception of microlensed light curves is examined, and attention is given to the influence of poor time coverage on simulated light curves. A regular monitoring program for microlensing candidates (especially 2237 + 0305) with a dedicated telescope is needed to make observations in different wavebands each night.

Wambsganss, Joachim↗
NASA NTRS1993

Turbulent heat transfer from a sparsely vegetated surface - Two-component representation

The conventional calculation of heat fluxes from a vegetated surface involving the coefficient of turbulent heat transfer which increases logarithmically with surface roughness, is inappropriate such highly structured surfaces as desert scrub or open forest. An approach is developed here for computing sensible heat flux from sparsely vegetated surfaces, where the absorption of insolation and the transfer of absorbed heat to the atmosphere are calculated separately for the plants and for the soil. This approach is applied to a desert-scrub surface in the northern Sinai, for which the turbulent transfer coefficient of sensible heat flux from the plants is much larger than that from the soil below, as shown by an analysis of plant, soil, and air temperatures. The plant density is expressed as the sum of products (plant-height) x (plant-diameter) of plants per unit horizontal surface area. The solar heat absorbed by the plants is assumed to be transferred immediately to the airflow. The effective turbulent transfer coefficient k(g-eff) for sensible heat from the desert-scrub/soil surface computed under this assumption increases sharply with increasing solar zenith angle, as the plants absorb a greater fraction of the incoming irradiation. The surface absorptivity (the coalbedo) also increases sharply with increasing solar zenith angle, and thus the sensible heat flux from such complex surfaces is a much broader function of time of day than when computed under constant k(g-eff) and constant albedo assumptions.

Otterman, J.↗
NASA NTRS1994

Improved load distribution in parallel sparse Cholesky factorization

Compared to the customary column-oriented approaches, block-oriented, distributed-memory sparse Cholesky factorization benefits from an asymptotic reduction in interprocessor communication volume and an asymptotic increase in the amount of concurrency that is exposed in the problem. Unfortunately, block-oriented approaches (specifically, the block fan-out method) have suffered from poor balance of the computational load. As a result, achieved performance can be quite low. This paper investigates the reasons for this load imbalance and proposes simple block mapping heuristics that dramatically improve it. The result is a roughly 20% increase in realized parallel factorization performance, as demonstrated by performance results from an Intel Paragon system. We have achieved performance of nearly 3.2 billion floating point operations per second with this technique on a 196-node Paragon system.

Rothberg, Edward↗
NASA NTRS1994

Tensor-GMRES method for large sparse systems of nonlinear equations

This paper introduces a tensor-Krylov method, the tensor-GMRES method, for large sparse systems of nonlinear equations. This method is a coupling of tensor model formation and solution techniques for nonlinear equations with Krylov subspace projection techniques for unsymmetric systems of linear equations. Traditional tensor methods for nonlinear equations are based on a quadratic model of the nonlinear function, a standard linear model augmented by a simple second order term. These methods are shown to be significantly more efficient than standard methods both on nonsingular problems and on problems where the Jacobian matrix at the solution is singular. A major disadvantage of the traditional tensor methods is that the solution of the tensor model requires the factorization of the Jacobian matrix, which may not be suitable for problems where the Jacobian matrix is large and has a 'bad' sparsity structure for an efficient factorization. We overcome this difficulty by forming and solving the tensor model using an extension of a Newton-GMRES scheme. Like traditional tensor methods, we show that the new tensor method has significant computational advantages over the analogous Newton counterpart. Consistent with Krylov subspace based methods, the new tensor method does not depend on the factorization of the Jacobian matrix. As a matter of fact, the Jacobian matrix is never needed explicitly.

Feng, Dan↗
NASA NTRS1990

Partitioning sparse matrices with eigenvectors of graphs

The problem of computing a small vertex separator in a graph arises in the context of computing a good ordering for the parallel factorization of sparse, symmetric matrices. An algebraic approach for computing vertex separators is considered in this paper. It is shown that lower bounds on separator sizes can be obtained in terms of the eigenvalues of the Laplacian matrix associated with a graph. The Laplacian eigenvectors of grid graphs can be computed from Kronecker products involving the eigenvectors of path graphs, and these eigenvectors can be used to compute good separators in grid graphs. A heuristic algorithm is designed to compute a vertex separator in a general graph by first computing an edge separator in the graph from an eigenvector of the Laplacian matrix, and then using a maximum matching in a subgraph to compute the vertex separator. Results on the quality of the separators computed by the spectral algorithm are presented, and these are compared with separators obtained from other algorithms for computing separators. Finally, the time required to compute the Laplacian eigenvector is reported, and the accuracy with which the eigenvector must be computed to obtain good separators is considered. The spectral algorithm has the advantage that it can be implemented on a medium-size multiprocessor in a straightforward manner.

Pothen, Alex↗
NASA NTRS1996

A Multilevel Algorithm for the Solution of Second Order Elliptic Differential Equations on Sparse Grids

A multilevel algorithm is presented that solves general second order elliptic partial differential equations on adaptive sparse grids. The multilevel algorithm consists of several V-cycles. Suitable discretizations provide that the discrete equation system can be solved in an efficient way. Numerical experiments show a convergence rate of order Omicron(1) for the multilevel algorithm.

Pflaum, Christoph↗
NASA NTRS1993

A Spectral Algorithm for Envelope Reduction of Sparse Matrices

The problem of reordering a sparse symmetric matrix to reduce its envelope size is considered. A new spectral algorithm for computing an envelope-reducing reordering is obtained by associating a Laplacian matrix with the given matrix and then sorting the components of a specified eigenvector of the Laplacian. This Laplacian eigenvector solves a continuous relaxation of a discrete problem related to envelope minimization called the minimum 2-sum problem. The permutation vector computed by the spectral algorithm is a closest permutation vector to the specified Laplacian eigenvector. Numerical results show that the new reordering algorithm usually computes smaller envelope sizes than those obtained from the current standard algorithms such as Gibbs-Poole-Stockmeyer (GPS) or SPARSPAK reverse Cuthill-McKee (RCM), in some cases reducing the envelope by more than a factor of two.

Barnard, Stephen T.↗
NASA NTRS1997

Complexity of Kronecker Operations on Sparse Matrices with Applications to the Solution of Markov Models

We present a systematic discussion of algorithms to multiply a vector by a matrix expressed as the Kronecker product of sparse matrices, extending previous work in a unified notational framework. Then, we use our results to define new algorithms for the solution of large structured Markov models. In addition to a comprehensive overview of existing approaches, we give new results with respect to: (1) managing certain types of state-dependent behavior without incurring extra cost; (2) supporting both Jacobi-style and Gauss-Seidel-style methods by appropriate multiplication algorithms; (3) speeding up algorithms that consider probability vectors of size equal to the "actual" state space instead of the "potential" state space.

Buchholz, Peter↗
NASA NTRS1999

Object-Oriented Design for Sparse Direct Solvers

We discuss the object-oriented design of a software package for solving sparse, symmetric systems of equations (positive definite and indefinite) by direct methods. At the highest layers, we decouple data structure classes from algorithmic classes for flexibility. We describe the important structural and algorithmic classes in our design, and discuss the trade-offs we made for high performance. The kernels at the lower layers were optimized by hand. Our results show no performance loss from our object-oriented design, while providing flexibility, case of use, and extensibility over solvers using procedural design.

Dobrian, Florin↗
NASA NTRS2002

Effects of Ordering Strategies and Programming Paradigms on Sparse Matrix Computations

The Conjugate Gradient (CG) algorithm is perhaps the best-known iterative technique to solve sparse linear systems that are symmetric and positive definite. For systems that are ill-conditioned, it is often necessary to use a preconditioning technique. In this paper, we investigate the effects of various ordering and partitioning strategies on the performance of parallel CG and ILU(O) preconditioned CG (PCG) using different programming paradigms and architectures. Results show that for this class of applications: ordering significantly improves overall performance on both distributed and distributed shared-memory systems, that cache reuse may be more important than reducing communication, that it is possible to achieve message-passing performance using shared-memory constructs through careful data ordering and distribution, and that a hybrid MPI+OpenMP paradigm increases programming complexity with little performance gains. A implementation of CG on the Cray MTA does not require special ordering or partitioning to obtain high efficiency and scalability, giving it a distinct advantage for adaptive applications; however, it shows limited scalability for PCG due to a lack of thread level parallelism.

Oliker, Leonid↗
NASA NTRS2002

A Large Sparse Aperture Densified Pupil Hypertelescope Concept for Ground Based Detection of Extra-Solar Earth-Like Planets

A concept is presented for a large (10 - 30 meter) sparse aperture hyper telescope to image extrasolar earth-like planets from the ground in the presence of atmospheric seeing. The telescope achieves high dynamic range very close to bright stellar sources with good image quality using pupil densification techniques. Active correction of the perturbed wavefront is simplified by using 36 small flat mirrors arranged in a parabolic steerable array structure, eliminating the need for large delat lines and operating at near-infrared (1 - 3 Micron) wavelengths with flats comparable in size to the seeing cells.

Gezari, D.↗
NASA NTRS2005

Parallel Domain Decomposition Formulation and Software for Large-Scale Sparse Symmetrical/Unsymmetrical Aeroacoustic Applications

The overall objectives of this research work are to formulate and validate efficient parallel algorithms, and to efficiently design/implement computer software for solving large-scale acoustic problems, arised from the unified frameworks of the finite element procedures. The adopted parallel Finite Element (FE) Domain Decomposition (DD) procedures should fully take advantages of multiple processing capabilities offered by most modern high performance computing platforms for efficient parallel computation. To achieve this objective. the formulation needs to integrate efficient sparse (and dense) assembly techniques, hybrid (or mixed) direct and iterative equation solvers, proper pre-conditioned strategies, unrolling strategies, and effective processors' communicating schemes. Finally, the numerical performance of the developed parallel finite element procedures will be evaluated by solving series of structural, and acoustic (symmetrical and un-symmetrical) problems (in different computing platforms). Comparisons with existing "commercialized" and/or "public domain" software are also included, whenever possible.

Nguyen, D. T.↗