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Accounts from a Claims Reuse Experience: Design of an Airline Fares Tracker
Get PDFPrevious research efforts have led to the establishment of a repository of claims as reusable knowledge entities. Through the analysis, design, and prototyping of a notification system aimed at monitoring airfares across time, airlines, and location, this paper presents the various work-products resulting from a scenario-based design approach coupled with the Claims Reuse Library to support reuse-centric claims analysis. Finally, we share our experience and findings using the Claims Reuse Library as a core to knowledge transfer
A Case Study of Using Domain Analysis for the Conflation Algorithms Domain
Get PDFThis paper documents the domain engineering process for much
of the conflation algorithms domain. Empirical data on the process and
products of domain engineering were collected. Six conflation
algorithms of four different types: three affix removal, one successor
variety, one table lookup, and one n-gram were analyzed. Products of
the analysis include a generic architecture, reusable components, a
little language and an application generator that extends the scope of
the domain analysis beyond previous generators. The application
generator produces source code for not only affix removal type but
also successor variety, table lookup, and n-gram stemmers. The
performance of the stemmers generated automatically was compared with
the stemmers developed manually in terms of stem similarity, source
and executable sizes, and development and execution times. All five
stemmers generated by the application generator produced more than
99.9% identical stems with the manually developed stemmers. Some of
the generated stemmers were as efficient as their manual equivalents
and some were not
DENSERKS: Fortran sensitivity solvers using continuous, explicit Runge-Kutta schemes
Get PDFDENSERKS is a Fortran sensitivity equation solver package designed for integrating models whose evolution can be described by ordinary differential equations (ODEs). A salient feature of DENSERKS is its support for both forward and adjoint sensitivity analyses, with built-in integrators for both first and second order continuous adjoint models. The software implements explicit Runge-Kutta methods with adaptive timestepping and high-order dense output schemes for the forward and the tangent linear model trajectory interpolation. Implementations of six Runge-Kutta methods are provided, with orders of accuracy ranging from two to eight. This makes DENSERKS suitable for a wide range of practical applications. The use of dense output, a novel approach in adjoint sensitivity analysis solvers, allows for a high-order cost-effective interpolation. This is a necessary feature when solving adjoints of nonlinear systems using highly accurate Runge-Kutta methods (order five and above). To minimize memory requirements and make long-time integrations computationally efficient, DENSERKS implements a two-level checkpointing mechanism. The code is tested on a selection of problems illustrating first and second order sensitivity analysis with respect to initial model conditions. The resulting derivative information is also used in a gradient-based optimization algorithm to minimize cost functionals dependent on a given set of model parameters
Performance Modeling and Analysis of a Massively Parallel DIRECT— Part 2
Get PDFModeling and analysis techniques are used to investigate
the performance of a massively parallel version
of DIRECT, a global search algorithm widely used
in multidisciplinary design optimization applications.
Several highdimensional
benchmark functions and
real world problems are used to test the design
effectiveness under various problem structures. In
this second part of a twopart
work, theoretical and
experimental results are compared for two parallel
clusters with different system scale and network
connectivity. The first part studied performance
sensitivity to important parameters for problem configurations
and parallel schemes, using performance
metrics such as memory usage, load balancing,
and parallel efficiency. Here linear regression models
are used to characterize two major overhead
sources—interprocessor communication and processor
idleness—and also applied to the isoefficiency
functions in scalability analysis. For a variety of
highdimensional
problems and large scale systems,
the massively parallel design has achieved reasonable
performance. The results of the performance
study provide guidance for efficient problem and
scheme configuration. More importantly, the design
considerations and analysis techniques generalize to
the transformation of other global search algorithms
into effective large scale parallel optimization tools
Update on Multirate Timestepping Methods for Hyperbolic Conservation Laws
Get PDFThis paper constructs multirate time discretizations for hyperbolic conservation laws that allow different timesteps to be used in different parts of the spatial domain. The proposed family of discretizations is second order accurate in time and has conservation and linear and nonlinear stability properties under local CFL conditions. Multirate timestepping avoids the necessity to take small global timesteps (restricted by the largest value of the Courant number on the grid) and therefore results in more efficient algorithms. Numerical results obtained for the advection and Burgers equations confirm the theoretical findings
Homotopy methods for constraint relaxation in unilevel reliability based design optimization
Get PDFReliability based design optimization is a methodology for finding optimized designs
that are characterized with a low probability of failure. The main ob jective in reliability
based design optimization is to minimize a merit function while satisfying the reliability
constraints. The reliability constraints are constraints on the probability of failure corre-
sponding to each of the failure modes of the system or a single constraint on the system
probability of failure. The probability of failure is usually estimated by performing a relia-
bility analysis. During the last few years, a variety of different techniques have been devel-
oped for reliability based design optimization. Traditionally, these have been formulated
as a double-loop (nested) optimization problem. The upper level optimization loop gen-
erally involves optimizing a merit function sub ject to reliability constraints and the lower
level optimization loop(s) compute the probabilities of failure corresponding to the failure
mode(s) that govern the system failure. This formulation is, by nature, computationally
intensive. A new efficient unilevel formulation for reliability based design optimization was
developed by the authors in earlier studies. In this formulation, the lower level optimiza-
tion (evaluation of reliability constraints in the double loop formulation) was replaced by its corresponding first order Karush-Kuhn-Tucker (KKT) necessary optimality conditions
at the upper level optimization. It was shown that the unilevel formulation is computation-
ally equivalent to solving the original nested optimization if the lower level optimization is
solved by numerically satisfying the KKT conditions (which is typically the case), and the
two formulations are mathematically equivalent under constraint qualification and general-
ized convexity assumptions. In the unilevel formulation, the KKT conditions of the inner
optimization for each probabilistic constraint evaluation are imposed at the system level as
equality constraints. Most commercial optimizers are usually numerically unreliable when
applied to problems accompanied by many equality constraints. In this investigation an
optimization framework for reliability based design using the unilevel formulation is de-
veloped. Homotopy methods are used for constraint relaxation and to obtain a relaxed
feasible design. A series of optimization problems are solved as the relaxed optimization
problem is transformed via a homotopy to the original problem. A heuristic scheme is
employed in this paper to update the homotopy parameter. The proposed algorithm is
illustrated with example problems
A Polynomial Chaos Based Bayesian Approach for Estimating Uncertain Parameters of Mechanical Systems - Part II: Applications to Vehicle Systems
Get PDFThis is the second part of a two-part article. In the first part, a new computational approach for parameter estimation was proposed based on the application of the polynomial chaos theory. The maximum likelihood estimates are obtained by minimizing a cost function derived from the Bayesian theorem. In this part, the new parameter estimation method is illustrated on a nonlinear four-degree-of-freedom roll plane model of a vehicle in which an uncertain mass with an uncertain position is added on the roll bar. The value of the mass and its position are estimated from periodic observations of the displacements and velocities across the suspensions. Appropriate excitations are needed in order to obtain accurate results. For some excitations, different combinations of uncertain parameters lead to essentially the same time responses, and no estimation method can work without additional information. Regularization techniques can still yield most likely values among the possible combinations of uncertain parameters resulting in the same time responses than the ones observed. When using appropriate excitations, the results obtained with this approach are close to the actual values of the parameters. The accuracy of the estimations has been shown to be sensitive to the number of terms used in the polynomial expressions and to the number of collocation points, and thus it may become computationally expensive when a very high accuracy of the results is desired. However, the noise level in the measurements affects the accuracy of the estimations as well. Therefore, it is usually not necessary to use a large number of terms in the polynomial expressions and a very large number of collocation points since the addition of extra precision eventually affects the results less than the effect of the measurement noise. Possible applications of this theory to the field of vehicle dynamics simulations include the estimation of mass, inertia properties, as well as other parameters of interest
On Consistency Properties of Discrete Adjoint Linear Multistep Methods
Get PDFIn this paper we analyze the consistency properties of discrete adjoints of linear multistep methods. Discrete adjoints are very popular in optimization and control since they can be constructed automatically by reverse mode automatic differentiation. The consistency analysis reveals that the discrete linear multistep adjoints are, in general, inconsistent approximations of the adjoint ODE solution along the trajectory. However, the discrete adjoints at the initial time (and therefore the discrete adjoint gradients) converge to the adjoint ODE solution with the same order as the original linear multistep method. Discrete adjoints inherit the zero-stability properties of the forward method. Numerical results confirm the theoretical findings
Effect of flexible joints on the stability and large deflections of a triangular frame
Get PDFAn isosceles triangular frame with rotationally resistive joints under a tip load is studied. The large in-plane deformation elastica equations are formulated. Stability analysis shows the frame can buckle symmetrically or asymmetrically. Post-buckling behavior
showing limit load and hysteresis are obtained by shooting and homotopy numerical
algorithms. The behavior of a frame with rigid joints is studied in detail. The effects of
joint spring constant and base length are found
Prediction-based Power-Performance Adaptation of Multithreaded Scientific Codes
Get PDFComputing is currently at an inflection point, with the degree of
on-chip thread-level parallelism doubling every one to two years. The
number of cores has become one of the most important architectural
parameters that characterize performance and power-efficiency of a
modern microprocessor, and a computer system in general. Concurrency
lends itself naturally to allowing a program to trade some of its
performance for power savings, by regulating the number of active
cores. Unfortunately, in several computing domains, users are
unwilling to sacrifice performance to save power. Futhermore, the
opportunities for saving power via other means, such as voltage and
frequency scaling, may be limited in heavily optimized applications.
In this paper, we present a prediction model for identifying
energy-efficient operating points of concurrency in well-tuned
multithreaded scientific applications, and a runtime system which uses
live analysis of hardware event rates through the prediction model, to
optimize applications dynamically. The runtime system throttles
concurrency so that power consumption can be reduced and performance
can be set at the knee of the scalability curve of each parallel
execution phase. We present a dynamic, phase-aware performance
prediction model (DPAPP), which combines multivariate regression
techniques with runtime analysis of data collected from hardware event
counters, to locate optimal operating points of concurrency. DPAPP is
hardware-aware, in the sense that it takes into account the dimensions
of parallelism in the architecture, using distinct predictors and
hardware events for each dimension. It is also phase-aware. Using
DPAPP, we develop a prediction-driven runtime optimization scheme,
which drastically reduces the overhead of searching the optimization
space for power-performance efficiency, while achieving near-optimal
performance and power savings in real parallel applications