Este trabalho propõe e descreve em detalhes o projeto do AMIGO (DynAMical FlexIble SchedulinG EnvirOnment), uma nova ferramenta de software capaz de viabilizar a união de diferentes algoritmos de escalonamento, de uma maneira completamente transparente ao usuário. O AMIGO é capaz de flexibilizar o escalonamento (em tempo de execução da aplicação) desde a sua configuração até a sua efetiva aplicação. Além da flexibilidade dinâmica e da transparência, o AMIGO também é modular: o seu projeto está dividido em módulos que, entre outras vantagens, facilitam sua execução em diferentes plataformas. Este trabalho também contribui apresentando uma análise crítica da literatura da área, apontando divergências e propondo pontos de convergência importantes. Assim, o levantamento bibliográfico apresentado atua como um material introdutório precioso para que os pesquisadores iniciantes formem um contexto geral sobre a área e, desse modo, aprofundem mais rapidamente seus estudos em outros trabalhos mais específicos. A avaliação de desempenho feita com o AMIGO demonstra que é possível a obtenção de ganhos de desempenho expressivos, com total transparência para o usuário final. Unindo-se desempenho, flexibilidade e transparência, espera-se contribuir para a redução da lacuna existente entre teoria e prática na área de escalonamento de processos / This thesis proposes and describes in details the design of the AMIGO (DynAMical FlexIble SchedulinG EnvirOnment), a novel software tool that makes possible the union of different scheduling algorithms, in a way completely transparent to the user. The AMIGO is able to make flexible the scheduling activity (at run-time), covering all the steps from its configuration up to its effective application. Besides the dynamic flexibility and transparency, AMIGO is also modular: it is split into modules that, among other advantages, facilitate its execution on different platforms. This work also contributes by presenting a critical analysis of the process-scheduling literature, pointing out the existing divergences and proposing important convergence points. Thus, the literature survey presented acts as a precious introductory material, which is able, on one hand, to give to the beginners a broad view of the process-scheduling area and, on the other hand, to facilitate the development of deeper studies in a quicker fashion when more specific works are needed. The performance evaluation of the AMIGO shows that is possible to have expressive performance gains, while having total user transparency. Joining flexibility and transparency it is hoped to contribute for the reduction of the existing gap between theory and practice in the scheduling process area

Este trabalho propõe e descreve em detalhes o projeto do AMIGO (DynAMical FlexIble SchedulinG EnvirOnment), uma nova ferramenta de software capaz de viabilizar a união de diferentes algoritmos de escalonamento, de uma maneira completamente transparente ao usuário. O AMIGO é capaz de flexibilizar o escalonamento (em tempo de execução da aplicação) desde a sua configuração até a sua efetiva aplicação. Além da flexibilidade dinâmica e da transparência, o AMIGO também é modular: o seu projeto está dividido em módulos que, entre outras vantagens, facilitam sua execução em diferentes plataformas. Este trabalho também contribui apresentando uma análise crítica da literatura da área, apontando divergências e propondo pontos de convergência importantes. Assim, o levantamento bibliográfico apresentado atua como um material introdutório precioso para que os pesquisadores iniciantes formem um contexto geral sobre a área e, desse modo, aprofundem mais rapidamente seus estudos em outros trabalhos mais específicos. A avaliação de desempenho feita com o AMIGO demonstra que é possível a obtenção de ganhos de desempenho expressivos, com total transparência para o usuário final. Unindo-se desempenho, flexibilidade e transparência, espera-se contribuir para a redução da lacuna existente entre teoria e prática na área de escalonamento de processos / This thesis proposes and describes in details the design of the AMIGO (DynAMical FlexIble SchedulinG EnvirOnment), a novel software tool that makes possible the union of different scheduling algorithms, in a way completely transparent to the user. The AMIGO is able to make flexible the scheduling activity (at run-time), covering all the steps from its configuration up to its effective application. Besides the dynamic flexibility and transparency, AMIGO is also modular: it is split into modules that, among other advantages, facilitate its execution on different platforms. This work also contributes by presenting a critical analysis of the process-scheduling literature, pointing out the existing divergences and proposing important convergence points. Thus, the literature survey presented acts as a precious introductory material, which is able, on one hand, to give to the beginners a broad view of the process-scheduling area and, on the other hand, to facilitate the development of deeper studies in a quicker fashion when more specific works are needed. The performance evaluation of the AMIGO shows that is possible to have expressive performance gains, while having total user transparency. Joining flexibility and transparency it is hoped to contribute for the reduction of the existing gap between theory and practice in the scheduling process area

Latent Dirichlet Allocation (LDA), a total probability generative model, is a three-tier Bayesian model. LDA computes the latent topic structure of the data and obtains the significant information of documents. However, traditional LDA has several limitations in practical applications. LDA cannot be directly used in classification because it is a non-supervised learning model. It needs to be embedded into appropriate classification algorithms. LDA is a generative model as it normally generates the latent topics in the categories where the target documents do not belong to, producing the deviation in computation and reducing the classification accuracy. The number of topics in LDA influences the learning process of model parameters greatly. Noise samples in the training data also affect the final text classification result. And, the quality of LDA based classifiers depends on the quality of the training samples to a great extent. Although parallel LDA algorithms are proposed to deal with huge amounts of data, balancing computing loads in a computer cluster poses another challenge. This thesis presents a text classification method which combines the LDA model and Support Vector Machine (SVM) classification algorithm for an improved accuracy in classification when reducing the dimension of datasets. Based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN), the algorithm automatically optimizes the number of topics to be selected which reduces the number of iterations in computation. Furthermore, this thesis presents a noise data reduction scheme to process noise data. When the noise ratio is large in the training data set, the noise reduction scheme can always produce a high level of accuracy in classification. Finally, the thesis parallelizes LDA using the MapReduce model which is the de facto computing standard in supporting data intensive applications. A genetic algorithm based load balancing algorithm is designed to balance the workloads among computers in a heterogeneous MapReduce cluster where the computers have a variety of computing resources in terms of CPU speed, memory space and hard disk space.

In recent years program transformation technology has matured into a practical solution for many software reengineering and migration tasks. FermaT, an industrial strength program transformation system, has demonstrated that legacy systems can be successfully transformed into efficient and maintainable structured C or COBOL code. Its core, a transformation engine, is based on mathematically proven program transformations and ensures that transformed programs are semantically equivalent to its original state. Its engine facilitates a Wide Spectrum Language (WSL), with low-level as well as high-level constructs, to capture as much information as possible during transformation steps. FermaT’s methodology and technique lack in provision of concurrent migration and analysis. This provision is crucial if the transformation process is to be further automated. As the constraint based program migration theory has demonstrated, it is inefficient and time consuming, trying to satisfy the enormous computation of the generated transformation sequence search-space and its constraints. With the objective to solve the above problems and to extend the operating range of the FermaT transformation system, this thesis proposes a Parallel Transformations Framework which makes parallel transformations processing within the FermaT environment not only possible but also beneficial for its migration process. During a migration process, many thousands of program transformations have to be applied. For example a 1 million line of assembler to C migration takes over 21 hours to be processed on a single PC. Various approaches of search, prediction techniques and a constraint-based approach to address the presented issues already exist but they solve them unsatisfactorily. To remedy this situation, this dissertation proposes a framework to extend transformation processing systems with parallel processing capabilities. The parallel system can analyse specified parallel transformation tasks and produce appropriate parallel transformations processing outlines. To underpin an automated objective, a formal language is introduced. This language can be utilised to describe and outline parallel transformation tasks whereas parallel processing constraints underpin the parallel objective. This thesis addresses and explains how transformation processing steps can be automatically parallelised within a reengineering domain. It presents search and prediction tactics within this field. The decomposition and parallelisation of transformation sequence search-spaces is outlined. At the end, the presented work is evaluated on practical case studies, to demonstrate different parallel transformations processing techniques and conclusions are drawn.

International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada / The driving force in modern space mission control has been directed towards developing cost effective and reliable communication and data systems. The objective is to maintain and ensure error-free payload commanding and data acquisition as well as efficient processing of the payload data for concurrent, real time and future use. While Mainframe computing still comprises a majority of commercially available communication and data systems, a significant diversion can be noticed towards utilizing a distributed network of workstations and commercially available software and hardware. This motivation reflects advances in modem computer technology and the trend in space mission control today and in the future. The development of communication and data involves the implementation of distributed and parallel processing concepts in a network of highly powerful client server environments. This paper addresses major issues related to developing and integrating communication and data system and the significance for future developments.

My main research topic is about developing new domain decomposition algorithms for the solution of systems of partial differential equations. This was mainly applied to fluid dynamics problems (as compressible Euler or Stokes equations) and electromagnetics (time-harmonic and time-domain first order system of Maxwell's equations). Since the solution of large linear systems is strongly related to the application of a discretization method, I was also interested in developing and analyzing the application of high order methods (such as Discontinuos Galerkin methods) to Maxwell's equations (sometimes in conjuction with time-discretization schemes in the case of time-domain problems). As an active member of NACHOS pro ject (besides my main afiliation as an assistant professor at University of Nice), I had the opportunity to develop certain directions in my research, by interacting with permanent et non-permanent members (Post-doctoral researchers) or participating to supervision of PhD Students. This is strongly refflected in a part of my scientific contributions so far. This memoir is composed of three parts: the first is about the application of Schwarz methods to fluid dynamics problems; the second about the high order methods for the Maxwell's equations and the last about the domain decomposition algorithms for wave propagation problems.

The energy performance in buildings is influenced by many factors, such as ambient weather conditions, building structure and characteristics, occupancy and their behaviors, the operation of sub-level components like Heating, Ventilation and Air-Conditioning (HVAC) system. This complex property makes the prediction, analysis, or fault detection/diagnosis of building energy consumption very difficult to accurately and quickly perform. This thesis mainly focuses on up-to-date artificial intelligence models with the applications to solve these problems. First, we review recently developed models for solving these problems, including detailed and simplified engineering methods, statistical methods and artificial intelligence methods. Then we simulate energy consumption profiles for single and multiple buildings, and based on these datasets, support vector machine models are trained and tested to do the prediction. The results from extensive experiments demonstrate high prediction accuracy and robustness of these models. Second, Recursive Deterministic Perceptron (RDP) neural network model is used to detect and diagnose faulty building energy consumption. The abnormal consumption is simulated by manually introducing performance degradation to electric devices. In the experiment, RDP model shows very high detection ability. A new approach is proposed to diagnose faults. It is based on the evaluation of RDP models, each of which is able to detect an equipment fault.Third, we investigate how the selection of subsets of features influences the model performance. The optimal features are selected based on the feasibility of obtaining them and on the scores they provide under the evaluation of two filter methods. Experimental results confirm the validity of the selected subset and show that the proposed feature selection method can guarantee the model accuracy and reduces the computational time.One challenge of predicting building energy consumption is to accelerate model training when the dataset is very large. This thesis proposes an efficient parallel implementation of support vector machines based on decomposition method for solving such problems. The parallelization is performed on the most time-consuming work of training, i.e., to update the gradient vector f. The inner problems are dealt by sequential minimal optimization solver. The underlying parallelism is conducted by the shared memory version of Map-Reduce paradigm, making the system particularly suitable to be applied to multi-core and multiprocessor systems. Experimental results show that our implementation offers a high speed increase compared to Libsvm, and it is superior to the state-of-the-art MPI implementation Pisvm in both speed and storage requirement.

Two global optimization methods are proposed in this thesis. They are the multicanonical basin hopping (MUBH) method and the basin paving (BP) method. <br /><br /> The MUBH method combines the basin hopping (BH) method, which can be used to efficiently map out an energy landscape associated with local minima, with the multicanonical Monte Carlo (MUCA) method, which encourages the system to move out of energy traps during the computation. It is found to be more efficient than the original BH method when applied to the Lennard-Jones systems containing 150-185 particles. <br /><br /> The asynchronous multicanonical basin hopping (AMUBH) method, a parallelization of the MUBH method, is also implemented using the message passing interface (MPI) to take advantage of the full usage of multiprocessors in either a homogeneous or a heterogeneous computational environment. AMUBH, MUBH and BH are used together to find the global minimum structures for Co nanoclusters with system size <em>N</em>&le;200. <br /><br /> The BP method is based on the BH method and the idea of the energy landscape paving (ELP) strategy. In comparison with the acceptance scheme of the ELP method, moving towards the low energy region is enhanced and no low energy configuration may be missed during the simulation. The applications to both the pentapeptide Met-enkephalin and the villin subdomain HP-36 locate new configurations having energies lower than those determined previously. <br /><br /> The MUBH, BP and BH methods are further employed to search for the global minimum structures of several proteins/peptides using the ECEPP/2 and ECEPP/3 force fields. These two force fields may produce global minima with different structures. The present study indicates that the global minimum determination from ECEPP/3 prefers helical structures. Also discussed in this thesis is the effect of the environment on the formation of beta hairpins.

A Large Eddy-Simulation code, based on a mesh transparent algorithm, for hybrid unstructured meshes is presented to deal with complex geometries that are often found in engineering flow problems. While tetrahedral elements are very effective in dealing with complex geometry, excessive numerical diffusion often affects results. Thus, prismatic or hexahedral elements are preferable in regions where turbulence structures are important. A second order reconstruction methodology is used since an investigation of a higher order method based upon Lele's compact scheme has shown this to be impractical on general unstructured meshes. The convective fluxes are treated with the Roe scheme that has been modified by introducing a variable scaling to the dissipation matrix to obtain a nearly second order accurate centred scheme in statistically smooth flow, whilst retaining the high resolution TVD behaviour across a shock discontinuity. The code has been parallelised using MPI to ensure portability. The base numerical scheme has been validated for steady flow computations over complex geometries using inviscid and RANS forms of the governing equations. The extension of the numerical scheme to unsteady turbulent flows and the complete LES code have been validated for the interaction of a shock with a laminar mixing layer, a Mach 0.9 turbulent round jet and a fully developed turbulent pipe flow. The mixing layer and round jet computations indicate that, for similar mesh resolution of the shear layer, the present code exhibits results comparable to previously published work using a higher order scheme on a structured mesh. The unstructured meshes have a significantly smaller total number of nodes since tetrahedral elements are used to fill to the far field region. The pipe flow results show that the present code is capable of producing the correct flow features. Finally, the code has been applied to the LES computation of the impingement of a highly under-expanded jet that produces plate shock oscillation. Comparison with other workers' experiments indicates good qualitative agreement for the major features of the flow. However, in this preliminary computation the computed frequency is somewhat lower than that of experimental measurements.

In the last decade, advances in sensor and computing technology are revolutionary. The latest-generation of hyperspectral and synthetic aperture radar ((SAR) instruments have increased their spectral, spatial, and temporal resolution. Consequently, the data sets collected are increasing rapidly in size and frequency of acquisition. Remote sensing applications are requiring more computing resources for data analysis. High performance computing (HPC) infrastructure such as clusters, distributed networks, grids, clouds and specialized hardware components, have been used to disseminate large volumes of remote sensing data and to accelerate the computational speed in processing raw images and extracting information from remote sensing data. In previous research we have shown that we can improve computational efficiency of a hyperspectral image denoising algorithm by parallelizing the algorithm utilizing a distributed computing grid. In recent years, computational cloud technology is emerging, bringing more flexibility and simplicity for data processing. Hadoop MapReduce is a software framework for distributed commodity computing clusters, allowing parallel processing of massive datasets. In this project, we implement a software application to map forest aboveground biomass (AGB) with normalized difference vegetation indices (NDVI) using Landsat Thematic Mapper’s bands 4 and 5 (ND45). We present observations and experimental results on the performance and the algorithmic complexity of the implementation. There are three research questions answered in this thesis, as follows. 1) How do we implement remote sensing algorithms, such as forest AGB mapping, in a computer cloud environment? 2) What are the requirements to implement distributed processing of remote sensing images using the cloud programming model? 3) What is the performance increase for large area remote sensing image processing in a cloud environment? / Graduate / 0799 / 0984