A review of High Performance Computing foundations for scientists

The increase of existing computational capabilities has made simulation emerge as a third discipline of Science, lying midway between experimental and purely theoretical branches [1, 2]. Simulation enables the evaluation of quantities which otherwise would not be accessible, helps to improve experiments and provides new insights on systems which are analysed [3-6]. Knowing the fundamentals of computation can be very useful for scientists, for it can help them to improve the performance of their theoretical models and simulations. This review includes some technical essentials that can be useful to this end, and it is devised as a complement for researchers whose education is focused on scientific issues and not on technological respects. In this document we attempt to discuss the fundamentals of High Performance Computing (HPC) [7] in a way which is easy to understand without much previous background. We sketch the way standard computers and supercomputers work, as well as discuss distributed computing and discuss essential aspects to take into account when running scientific calculations in computers.Comment: 33 page

Fast exact Bayesian inference for high-dimensional models

In this text, we present the principles that allow the tractable implementation of exact inference processes concerning a group of widespread classes of Bayesian generative models, which have until recently been deemed as intractable whenever formulated using high-dimensional joint distributions. We will demonstrate the usefulness of such a principled approach with an example of real-time OpenCL implementation using GPUs of a full-fledged, computer vision-based model to estimate gaze direction in human-robot interaction (HRI)

Computing 3SLS Solutions of Simultaneous Equation Models with a Possible Singular Variance-Convariance Matrix

Algorithms for computing the three-stage least squares (3SLS) estimator usually require the disturbance convariance matrix to be non-singular. However, the solution of a reformulated simultaneous equation model (SEM) results into the redundancy of this condition. Having as a basic tool the QR decomposition, the 3SLS estimator, its dispersion matrix and methods for estimating the singular disturbance covariance matrix and derived. Expressions revealing linear combinations between the observations which become redundant have also been presented. Algorithms for computing the 3SLS estimator after the SEM have been modified by deleting or adding new observations or variables are found not to be very efficient, due to the necessity of removing the endogeneity of the new data or by re-estimating the disturbance covariance matrix. Three methods have been described for solving SEMs subject to separable linear equalities constraints. The first method considers the constraints as additional precise observations while the other two methods reparameterized the constraints to solve reduced unconstrained SEMs. Method for computing the main matrix factorizations illustrate the basic principles to be adopted for solving SEMs on serial or parallel computer

07361 Abstracts Collection -- Programming Models for Ubiquitous Parallelism

From 02.09. to 07.09.2007, the Dagstuhl Seminar 07361 ``Programming Models for Ubiquitous Parallelism\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Processor-In-Memory (PIM) Based Architectures for PetaFlops Potential Massively Parallel Processing

The report summarizes the work performed at the University of Notre Dame under a NASA grant from July 15, 1995 through July 14, 1996. Researchers involved in the work included the PI, Dr. Peter M. Kogge, and three graduate students under his direction in the Computer Science and Engineering Department: Stephen Dartt, Costin Iancu, and Lakshmi Narayanaswany. The organization of this report is as follows. Section 2 is a summary of the problem addressed by this work. Section 3 is a summary of the project's objectives and approach. Section 4 summarizes PIM technology briefly. Section 5 overviews the main results of the work. Section 6 then discusses the importance of the results and future directions. Also attached to this report are copies of several technical reports and publications whose contents directly reflect results developed during this study

Activities of the Institute for Computer Applications in Science and Engineering (ICASE)

This report summarizes research conducted at the Institute for Computer Applications Science and Engineering in applied mathematics, numerical analysis, and computer science during the period October 2, 1987 through March 31, 1988