Vinicius da Fonseca Vieira

Genetic Algorithm for the History Matching Problem

Abstract In this work we present a study of genetic algorithms for the automatic history matching problem of reservoir simulation. The history matching process is an inverse problem that searches a set of parameters that minimizes the difference between the model performance and the historical performance of the field. This model validation process is essential and gives credibility to the predictions of the reservoir model. We studied a Parallel Genetic Algorithm implementation, several tests were performed and the preliminary results are presented and discussed in this work.

Multi-level Parallelism in the Computational Modeling of the Heart

One way to exploit Thread Level Parallelism (TLP) is to use architectures that implement novel multithreaded execution models, like Scheduled Data- Flow (SDF). This latter model promises an elegant decoupled and non-blocking execution of threads. Here we extend that model in order to be used in future scalable CMP systems where wire delay imposes to partition the design. In this paper we describe our approach and experiment with different distributed schedulers, different number of clusters and processors per cluster to show good scalability of our architecture. We describe our approach and present initial results on system scalability and performance. We suggest design choices to improve the scalability of the basic design.Computational modeling of the heart has demonstrated to be a useful tool for the investigation and comprehension of the complex biophysical processes that underlie cardiac function. Unfortunately, large scale simulations, such as those resulting from the discretization of an entire heart, remain a computational challenge. In order to reduce simulation execution times, parallel implementations have traditionally exploited data parallelism via numerical schemes based on domain-decomposition. However, it has been verified that the parallel efficiency of these implementations severely degrades as the number of processors increases. In this work, we propose and implement a new parallel algorithm for the solution of cardiac models. By relaxing the coherence of the execution, a new level of parallelism could be identified and exploited: pipelining. A synchronous parallel algorithm that uses both pipelining and data decomposition techniques was implemented and used the MPI library for communication. Numerical tests were performed in a 8-node Linux-cluster. Our preliminary results indicate that the proposed algorithm is able to increase the parallel efficiency up to 20% when compared to the traditional approach that uses pure data-level parallelism. In addition, the numerical precision was kept under control (relative errors under 4%) when the relaxed coherence execution was adopted.

Multi-level parallelism for the cardiac Bidomain equations

Cardiovascular diseases are associated with high mortality rates in the globe. The development of new drugs, new medical equipment and non-invasive techniques for the heart demand multidisciplinary efforts towards the characterization of cardiac anatomy and function from the molecular to the organ level. Computational modeling has demonstrated to be a useful tool for the investigation and comprehension of the complex biophysical processes that underlie cardiac function. The set of Bidomain equations is currently one of the most complete mathematical models for simulating the electrical activity in cardiac tissue. Unfortunately, large scale simulations, such as those resulting from the discretization of an entire heart, remain a computational challenge. In order to reduce simulation execution times, parallel implementations have traditionally exploited data parallelism via numerical schemes based on domain-decomposition. However, it has been verified that the parallel efficiency of these implementations severely degrades as the number of processors increases. In this work we propose and implement a new parallel algorithm for the solution of cardiac models. By relaxing the coherence of the execution, a new level of parallelism could be identified and exploited: pipelining. A synchronous parallel algorithm that uses both pipelining and data decomposition techniques was implemented and used the MPI library for communication. Numerical tests were performed in two different cluster configurations. Our preliminary results indicated that the proposed algorithm is able to increase the parallel efficiency up to 20% on an 8-core cluster. On a 32-core cluster the multi-level algorithm was 1.7 times faster than the traditional domain decomposition algorithm. In addition, the numerical precision was kept under control (relative errors under 6%) when the relaxed coherence execution was adopted.

DECoDe - Differential Evolution Algorithm for Community Detection

Community structure of networks, i.e., groups of nodes densely connected inside the same group and weakly connected outside the group, are one of their most important property and there is great interest in the investigation of methods that are able to automatically detect such divisions. This work presents a novel method for community detection based on Differential Evolution, the Differential Evolution Algorithm for Community Detection (DECoDe). Differential evolution is an evolutionary algorithm frequently applied to continuous problems and this work presents a novel approach which adapts the algorithm to discrete problems, allowing the solution of the community detection problem. Several tests were executed with real networks and the results show that the presented approach is able to find consistent community structures, when compared to other methods in the literature.

Identification of dynamic systems using a differential evolution-based recurrent fuzzy system

This work presents the development of a simulation model based on a recurrent fuzzy system with structure and parameter identification by a differential evolution algorithm. The proposed model is formulated by state space equation, in which the state transition function is a recurrent fuzzy system with two feedback connections and adjustable delay operators and the output function is a linear function of the states. The identification process relies on two instances of the differential evolution algorithm in a hierarchical fashion. The outermost is considered for combinatorial structure optimization and the innermost for optimization of continuous parameters. The new model is evaluated in some benchmark problems and the results showed the model achieved good numerical performance. Moreover, the results demonstrated the ability of differential evolution algorithm to optimize both the parameters as well as the structure of the model.

Performance Evaluation of Modularity Based Community Detection Algorithms in Large Scale Networks

Community structure detection is one of the major research areas of network science and it is particularly useful for large real networks applications. This work presents a deep study of the most discussed algorithms for community detection based on modularity measure: Newman’s spectral method using a fine-tuning stage and the method of Clauset, Newman, and Moore (CNM) with its variants. The computational complexity of the algorithms is analysed for the development of a high performance code to accelerate the execution of these algorithms without compromising the quality of the results, according to the modularity measure. The implemented code allows the generation of partitions with modularity values consistent with the literature and it overcomes 1 million nodes with Newman’s spectral method. The code was applied to a wide range of real networks and the performances of the algorithms are evaluated.

Populational Algorithm for Influence Maximization

Influence maximization is one of the most challenging tasks in network and consists in finding a set of the k seeder nodes which maximize the number of reached nodes, considering a propagation model. This work presents a Genetic Algorithm for influence maximization in networks considering Spreading Activation model for influence propagation. Four strategies for contructing the initial population were explored: a random strategy, a PageRank based strategy and two strategies which considers the community structure and the communities to which the seeders belong. The results show that GA was able to significantly improve the quality of the seeders, increasing the number of reached nodes in about \(25\,\%\).

Investigação Sobre Robustez de Comunidades em Redes

Resumo—Em sistemas complexos modelados como redes, onde os nós representam os indivíduos e as arestas representam os relacionamentos entre os indivíduos, uma das principais questões a serem exploradas é a robustez, ou seja, a capacidade de uma rede suportar ataques a seus elementos. Além disso, o estudo da estrutura de comunidades de redes é bastante importante para a compreensão da topologia da rede em uma organização local. Neste trabalho, estuda-se o efeito da estrutura de comunidades na robustez de redes reais. Para isso, é feita uma investigação comparativa entre o comportamento exibido por redes e suas comunidades correspondentes submetidas a ataques. Os resultados mostram uma forte analogia entre o comportamento exibido nas redes completas e suas comunidades isoladas.

Estudo comparativo de métricas de ranqueamento em redes complexas utilizando coeficientes de correlação

Resumo—A classificação e o ranqueamento de vértices é um tema muito estudado em redes complexas. Existem na literatura diversas métricas utilizadas na classificação de vértices em uma rede. Este trabalho visa comparar as diferentes métricas calculando o coeficiente de correlação entre elas. Resultados demonstram que as métricas Grau e Hub apresentam a maior correlação, ranqueando os vértices de maneira mais similar, enquanto as métricas Hub e PageRank obtiveram o menor coeficiente de correlação.