72 research outputs found
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Resource placement, data rearrangement, and Hamiltonian cycles in torus networks
Many parallel machines, both commercial and experimental, have been/are being designed with toroidal interconnection networks. For a given number of nodes, the torus has a relatively larger diameter, but better cost/performance tradeoffs, such as higher channel bandwidth, and lower node degree, when compared to the hypercube. Thus, the torus is becoming a popular topology for the interconnection network of a high performance parallel computers.
In a multicomputer, the resources, such as I/O devices or software packages, are distributed over the networks. The first part of the thesis investigates efficient methods of distributing resources in a torus network. Three classes of placement methods are studied. They are (1) distant-t placement problem: in this case, any non-resource node is at a distance of at most t from some resource nodes, (2) j-adjacency problem: here, a non-resource node is adjacent to at least j resource nodes, and (3) generalized placement problem: a non-resource node must be a distance of at most t from at least j resource nodes.
This resource placement technique can be applied to allocating spare processors to provide fault-tolerance in the case of the processor failures. Some efficient
spare processor placement methods and reconfiguration schemes in the case of processor failures are also described.
In a torus based parallel system, some algorithms give best performance if the data are distributed to processors numbered in Cartesian order; in some other cases, it is better to distribute the data to processors numbered in Gray code order. Since the placement patterns may be changed dynamically, it is essential to find efficient methods of rearranging the data from Gray code order to Cartesian order and vice versa. In the second part of the thesis, some efficient methods for data transfer from Cartesian order to radix order and vice versa are developed.
The last part of the thesis gives results on generating edge disjoint Hamiltonian cycles in k-ary n-cubes, hypercubes, and 2D tori. These edge disjoint cycles are quite useful for many communication algorithms
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Gray codes and their applications
An n-bit Gray code is an ordered set of all 2n binary strings of length n. The
special property of this listing is that Hamming distance between consecutive vectors
is exactly 1. If the last and first codeword also have a Hamming distance 1 then the
code is said to be cyclic. This dissertation addresses problems dealing with the design
and applications of new and existing types of both binary and non-binary Gray codes.
It is shown how properties of certain Gray codes can be used to solve problems arising
in different domains. New types of Gray codes to solve specific types of problems are
also designed. We construct Gray codes over higher integral radices and show their
applications. Applications of new classes of Gray codes defined over residue classes of
Gaussian integers are also shown. We also propose new classes of binary Gray codes
and prove some important properties of these codes
HPCCP/CAS Workshop Proceedings 1998
This publication is a collection of extended abstracts of presentations given at the HPCCP/CAS (High Performance Computing and Communications Program/Computational Aerosciences Project) Workshop held on August 24-26, 1998, at NASA Ames Research Center, Moffett Field, California. The objective of the Workshop was to bring together the aerospace high performance computing community, consisting of airframe and propulsion companies, independent software vendors, university researchers, and government scientists and engineers. The Workshop was sponsored by the HPCCP Office at NASA Ames Research Center. The Workshop consisted of over 40 presentations, including an overview of NASA's High Performance Computing and Communications Program and the Computational Aerosciences Project; ten sessions of papers representative of the high performance computing research conducted within the Program by the aerospace industry, academia, NASA, and other government laboratories; two panel sessions; and a special presentation by Mr. James Bailey
DTT - Divertor Tokamak Test facility - Interim Design Report
The “Divertor Tokamak Test facility, DTT” is a milestone along the international program aimed at demonstrating – in the second half of this century – the feasibility of obtaining to commercial electricity from controlled thermonuclear fusion. DTT is a Tokamak conceived and designed in Italy with a broad international vision. The construction will be carried out in the ENEA Frascati site, mainly supported by national funds, complemented by EUROfusion and European incentive schemes for innovative investments. The project team includes more than 180 high-standard researchers from ENEA, CREATE, CNR, INFN, RFX and various universities.
The volume, entitled DTT Interim Design Report (“Green Book” from the colour of the cover), briefly describes the status of the project, the planning of the design future activities and its organizational structure. The publication of the Green Book also provides an occasion for thorough discussions in the fusion community and a broad international collaboration on the DTT challenge
Automatic learning for the classification of chemical reactions and in statistical thermodynamics
This Thesis describes the application of automatic learning methods for a) the classification of organic and metabolic reactions, and b) the mapping of Potential Energy Surfaces(PES). The classification of reactions was approached with two distinct methodologies: a representation of chemical reactions based on NMR data, and a representation of chemical reactions from the reaction equation based on the physico-chemical and topological features of chemical bonds.
NMR-based classification of photochemical and enzymatic reactions. Photochemical
and metabolic reactions were classified by Kohonen Self-Organizing Maps (Kohonen
SOMs) and Random Forests (RFs) taking as input the difference between the 1H
NMR spectra of the products and the reactants. The development of such a representation can be applied in automatic analysis of changes in the 1H NMR spectrum of a mixture and their interpretation in terms of the chemical reactions taking place. Examples of possible applications are the monitoring of reaction processes, evaluation of the stability of chemicals, or even the interpretation of metabonomic data.
A Kohonen SOM trained with a data set of metabolic reactions catalysed by transferases
was able to correctly classify 75% of an independent test set in terms of the EC
number subclass. Random Forests improved the correct predictions to 79%. With photochemical reactions classified into 7 groups, an independent test set was classified with 86-93% accuracy. The data set of photochemical reactions was also used to simulate mixtures with two reactions occurring simultaneously. Kohonen SOMs and Feed-Forward Neural Networks (FFNNs) were trained to classify the reactions occurring in a mixture based on the 1H NMR spectra of the products and reactants. Kohonen SOMs allowed the correct assignment of 53-63% of the mixtures (in a test set). Counter-Propagation Neural Networks (CPNNs) gave origin to similar results. The use of supervised learning techniques allowed an improvement in the results. They were improved to 77% of correct assignments when an ensemble of ten FFNNs were used and to 80% when Random Forests were used.
This study was performed with NMR data simulated from the molecular structure by
the SPINUS program. In the design of one test set, simulated data was combined with
experimental data. The results support the proposal of linking databases of chemical
reactions to experimental or simulated NMR data for automatic classification of reactions and mixtures of reactions.
Genome-scale classification of enzymatic reactions from their reaction equation.
The MOLMAP descriptor relies on a Kohonen SOM that defines types of bonds on the basis of their physico-chemical and topological properties. The MOLMAP descriptor of a molecule represents the types of bonds available in that molecule. The MOLMAP
descriptor of a reaction is defined as the difference between the MOLMAPs of the products and the reactants, and numerically encodes the pattern of bonds that are broken,
changed, and made during a chemical reaction.
The automatic perception of chemical similarities between metabolic reactions is required for a variety of applications ranging from the computer validation of classification systems, genome-scale reconstruction (or comparison) of metabolic pathways, to the classification of enzymatic mechanisms. Catalytic functions of proteins are generally described by the EC numbers that are simultaneously employed as identifiers of reactions, enzymes, and enzyme genes, thus linking metabolic and genomic information. Different methods
should be available to automatically compare metabolic reactions and for the automatic
assignment of EC numbers to reactions still not officially classified.
In this study, the genome-scale data set of enzymatic reactions available in the KEGG
database was encoded by the MOLMAP descriptors, and was submitted to Kohonen
SOMs to compare the resulting map with the official EC number classification, to explore
the possibility of predicting EC numbers from the reaction equation, and to assess the
internal consistency of the EC classification at the class level.
A general agreement with the EC classification was observed, i.e. a relationship between the similarity of MOLMAPs and the similarity of EC numbers. At the same time, MOLMAPs were able to discriminate between EC sub-subclasses. EC numbers could be assigned at the class, subclass, and sub-subclass levels with accuracies up to 92%, 80%, and 70% for independent test sets. The correspondence between chemical similarity of metabolic reactions and their MOLMAP descriptors was applied to the identification of a number of reactions mapped into the same neuron but belonging to different EC classes, which demonstrated the ability of the MOLMAP/SOM approach to verify the internal consistency of classifications in databases of metabolic reactions.
RFs were also used to assign the four levels of the EC hierarchy from the reaction
equation. EC numbers were correctly assigned in 95%, 90%, 85% and 86% of the cases
(for independent test sets) at the class, subclass, sub-subclass and full EC number level,respectively. Experiments for the classification of reactions from the main reactants and products were performed with RFs - EC numbers were assigned at the class, subclass and sub-subclass level with accuracies of 78%, 74% and 63%, respectively.
In the course of the experiments with metabolic reactions we suggested that the
MOLMAP / SOM concept could be extended to the representation of other levels of
metabolic information such as metabolic pathways. Following the MOLMAP idea, the pattern of neurons activated by the reactions of a metabolic pathway is a representation of the reactions involved in that pathway - a descriptor of the metabolic pathway. This reasoning enabled the comparison of different pathways, the automatic classification of pathways, and a classification of organisms based on their biochemical machinery. The three levels of classification (from bonds to metabolic pathways) allowed to map and perceive chemical similarities between metabolic pathways even for pathways of different
types of metabolism and pathways that do not share similarities in terms of EC numbers.
Mapping of PES by neural networks (NNs). In a first series of experiments, ensembles of Feed-Forward NNs (EnsFFNNs) and Associative Neural Networks (ASNNs) were trained to reproduce PES represented by the Lennard-Jones (LJ) analytical potential
function. The accuracy of the method was assessed by comparing the results of molecular dynamics simulations (thermal, structural, and dynamic properties) obtained from the NNs-PES and from the LJ function.
The results indicated that for LJ-type potentials, NNs can be trained to generate
accurate PES to be used in molecular simulations. EnsFFNNs and ASNNs gave better
results than single FFNNs. A remarkable ability of the NNs models to interpolate between distant curves and accurately reproduce potentials to be used in molecular simulations is shown.
The purpose of the first study was to systematically analyse the accuracy of different NNs. Our main motivation, however, is reflected in the next study: the mapping
of multidimensional PES by NNs to simulate, by Molecular Dynamics or Monte Carlo,
the adsorption and self-assembly of solvated organic molecules on noble-metal electrodes.
Indeed, for such complex and heterogeneous systems the development of suitable analytical functions that fit quantum mechanical interaction energies is a non-trivial or even impossible task.
The data consisted of energy values, from Density Functional Theory (DFT) calculations,
at different distances, for several molecular orientations and three electrode
adsorption sites. The results indicate that NNs require a data set large enough to cover
well the diversity of possible interaction sites, distances, and orientations. NNs trained with such data sets can perform equally well or even better than analytical functions.
Therefore, they can be used in molecular simulations, particularly for the ethanol/Au
(111) interface which is the case studied in the present Thesis. Once properly trained,
the networks are able to produce, as output, any required number of energy points for
accurate interpolations
New Directions for Contact Integrators
Contact integrators are a family of geometric numerical schemes which
guarantee the conservation of the contact structure. In this work we review the
construction of both the variational and Hamiltonian versions of these methods.
We illustrate some of the advantages of geometric integration in the
dissipative setting by focusing on models inspired by recent studies in
celestial mechanics and cosmology.Comment: To appear as Chapter 24 in GSI 2021, Springer LNCS 1282
The 1982 NASA/ASEE Summer Faculty Fellowship Program
A NASA/ASEE Summer Faculty Fellowship Research Program was conducted to further the professional knowledge of qualified engineering and science faculty members, to stimulate an exchange of ideas between participants and NASA, to enrich and refresh the research and teaching activities of participants' institutions, and to contribute to the research objectives of the NASA Centers
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