Automating embedded analysis capabilities and managing software complexity in multiphysics simulation part I: template-based generic programming

An approach for incorporating embedded simulation and analysis capabilities in complex simulation codes through template-based generic programming is presented. This approach relies on templating and operator overloading within the C++ language to transform a given calculation into one that can compute a variety of additional quantities that are necessary for many state-of-the-art simulation and analysis algorithms. An approach for incorporating these ideas into complex simulation codes through general graph-based assembly is also presented. These ideas have been implemented within a set of packages in the Trilinos framework and are demonstrated on a simple problem from chemical engineering

SPAR data handling utilities

The SPAR computer software system is a collection of processors that perform particular steps in the finite-element structural analysis procedure. The data generated by each processor are stored on a data base complex residing on an auxiliary storage device, and these data are then used by subsequent processors. The SPAR data handling utilities use routines to transfer data between the processors and the data base complex. A detailed description of the data base complex organization is presented. A discussion of how these SPAR data handling utilities are used in an application program to perform desired user functions is given with the steps necessary to convert an existing program to a SPAR processor by incorporating these utilities. Finally, a sample SPAR processor is included to illustrate the use of the data handling utilities

A NASA family of minicomputer systems, Appendix A

This investigation was undertaken to establish sufficient specifications, or standards, for minicomputer hardware and software to provide NASA with realizable economics in quantity purchases, interchangeability of minicomputers, software, storage and peripherals, and a uniformly high quality. The standards will define minicomputer system component types, each specialized to its intended NASA application, in as many levels of capacity as required

LATENT VARIABLE GENERALIZED LINEAR MODELS

Generalized Linear Models (GLMs) (McCullagh and Nelder, 1989) provide a unified framework for fixed effect models where response data arise from exponential family distributions. Much recent research has attempted to extend the framework to include random effects in the linear predictors. Different methodologies have been employed to solve different motivating problems, for example Generalized Linear Mixed Models (Clayton, 1994) and Multilevel Models (Goldstein, 1995). A thorough review and classification of this and related material is presented. In Item Response Theory (IRT) subjects are tested using banks of pre-calibrated test items. A useful model is based on the logistic function with a binary response dependent on the unknown ability of the subject. Item parameters contribute to the probability of a correct response. Within the framework of the GLM, a latent variable, the unknown ability, is introduced as a new component of the linear predictor. This approach affords the opportunity to structure intercept and slope parameters so that item characteristics are represented. A methodology for fitting such GLMs with latent variables, based on the EM algorithm (Dempster, Laird and Rubin, 1977) and using standard Generalized Linear Model fitting software GLIM (Payne, 1987) to perform the expectation step, is developed and applied to a model for binary response data. Accurate numerical integration to evaluate the likelihood functions is a vital part of the computational process. A study of the comparative benefits of two different integration strategies is undertaken and leads to the adoption, unusually, of Gauss-Legendre rules. It is shown how the fitting algorithms are implemented with GLIM programs which incorporate FORTRAN subroutines. Examples from IRT are given. A simulation study is undertaken to investigate the sampling distributions of the estimators and the effect of certain numerical attributes of the computational process. Finally a generalized latent variable model is developed for responses from any exponential family distribution

New Techniques for On-line Testing and Fault Mitigation in GPUs

L'abstract è presente nell'allegato / the abstract is in the attachmen

RVSDG: An Intermediate Representation for Optimizing Compilers

Intermediate Representations (IRs) are central to optimizing compilers as the way the program is represented may enhance or limit analyses and transformations. Suitable IRs focus on exposing the most relevant information and establish invariants that different compiler passes can rely on. While control-flow centric IRs appear to be a natural fit for imperative programming languages, analyses required by compilers have increasingly shifted to understand data dependencies and work at multiple abstraction layers at the same time. This is partially evidenced in recent developments such as the MLIR proposed by Google. However, rigorous use of data flow centric IRs in general purpose compilers has not been evaluated for feasibility and usability as previous works provide no practical implementations. We present the Regionalized Value State Dependence Graph (RVSDG) IR for optimizing compilers. The RVSDG is a data flow centric IR where nodes represent computations, edges represent computational dependencies, and regions capture the hierarchical structure of programs. It represents programs in demand-dependence form, implicitly supports structured control flow, and models entire programs within a single IR. We provide a complete specification of the RVSDG, construction and destruction methods, as well as exemplify its utility by presenting Dead Node and Common Node Elimination optimizations. We implemented a prototype compiler and evaluate it in terms of performance, code size, compilation time, and representational overhead. Our results indicate that the RVSDG can serve as a competitive IR in optimizing compilers while reducing complexity

Modular digital holographic fringe data processing system

A software architecture suitable for reducing holographic fringe data into useful engineering data is developed and tested. The results, along with a detailed description of the proposed architecture for a Modular Digital Fringe Analysis System, are presented