Adapting the interior point method for the solution of LPs on serial, coarse grain parallel and massively parallel computers

In this paper we describe a unified scheme for implementing an interior point algorithm (IPM) over a range of computer architectures. In the inner iteration of the IPM a search direction is computed using Newton's method. Computationally this involves solving a sparse symmetric positive definite (SSPD) system of equations. The choice of direct and indirect methods for the solution of this system, and the design of data structures to take advantage of serial, coarse grain parallel and massively parallel computer architectures, are considered in detail. We put forward arguments as to why integration of the system within a sparse simplex solver is important and outline how the system is designed to achieve this integration

Report from the MPP Working Group to the NASA Associate Administrator for Space Science and Applications

NASA's Office of Space Science and Applications (OSSA) gave a select group of scientists the opportunity to test and implement their computational algorithms on the Massively Parallel Processor (MPP) located at Goddard Space Flight Center, beginning in late 1985. One year later, the Working Group presented its report, which addressed the following: algorithms, programming languages, architecture, programming environments, the way theory relates, and performance measured. The findings point to a number of demonstrated computational techniques for which the MPP architecture is ideally suited. For example, besides executing much faster on the MPP than on conventional computers, systolic VLSI simulation (where distances are short), lattice simulation, neural network simulation, and image problems were found to be easier to program on the MPP's architecture than on a CYBER 205 or even a VAX. The report also makes technical recommendations covering all aspects of MPP use, and recommendations concerning the future of the MPP and machines based on similar architectures, expansion of the Working Group, and study of the role of future parallel processors for space station, EOS, and the Great Observatories era

Probabilistic structural mechanics research for parallel processing computers

Aerospace structures and spacecraft are a complex assemblage of structural components that are subjected to a variety of complex, cyclic, and transient loading conditions. Significant modeling uncertainties are present in these structures, in addition to the inherent randomness of material properties and loads. To properly account for these uncertainties in evaluating and assessing the reliability of these components and structures, probabilistic structural mechanics (PSM) procedures must be used. Much research has focused on basic theory development and the development of approximate analytic solution methods in random vibrations and structural reliability. Practical application of PSM methods was hampered by their computationally intense nature. Solution of PSM problems requires repeated analyses of structures that are often large, and exhibit nonlinear and/or dynamic response behavior. These methods are all inherently parallel and ideally suited to implementation on parallel processing computers. New hardware architectures and innovative control software and solution methodologies are needed to make solution of large scale PSM problems practical

Solution of partial differential equations on vector and parallel computers

The present status of numerical methods for partial differential equations on vector and parallel computers was reviewed. The relevant aspects of these computers are discussed and a brief review of their development is included, with particular attention paid to those characteristics that influence algorithm selection. Both direct and iterative methods are given for elliptic equations as well as explicit and implicit methods for initial boundary value problems. The intent is to point out attractive methods as well as areas where this class of computer architecture cannot be fully utilized because of either hardware restrictions or the lack of adequate algorithms. Application areas utilizing these computers are briefly discussed

Enabling rapid iterative model design within the laboratory environment

This thesis presents a proof of concept study for the better integration of the electrophysiological and modelling aspects of neuroscience. Members of these two sub-disciplines collaborate regularly, but due to differing resource requirements, and largely incompatible spheres of knowledge, cooperation is often impeded by miscommunication and delays. To reduce the model design time, and provide a platform for more efficient experimental analysis, a rapid iterative model design method is proposed. The main achievement of this work is the development of a rapid model evaluation method based on parameter estimation, utilising a combination of evolutionary algorithms (EAs) and graphics processing unit (GPU) hardware acceleration. This method is the primary force behind the better integration of modelling and laboratorybased electrophysiology, as it provides a generic model evaluation method that does not require prior knowledge of model structure, or expertise in modelling, mathematics, or computer science. If combined with a suitable intuitive and user targeted graphical user interface, the ideas presented in this thesis could be developed into a suite of tools that would enable new forms of experimentation to be performed. The latter part of this thesis investigates the use of excitability-based models as the basis of an iterative design method. They were found to be computationally and structurally simple, easily extensible, and able to reproduce a wide range of neural behaviours whilst still faithfully representing underlying cellular mechanisms. A case study was performed to assess the iterative design process, through the implementation of an excitability-based model. The model was extended iteratively, using the rapid model evaluation method, to represent a vasopressin releasing neuron. Not only was the model implemented successfully, but it was able to suggest the existence of other more subtle cell mechanisms, in addition to highlighting potential failings in previous implementations of the class of neuron

Parallel computing in network optimization

Caption title.Includes bibliographical references (p. 82-95).Supported by the NSF. CCR-9103804Dimitri Bertsekas ... [et al.]