Alineamiento de secuencias genéticas en procesadores multicore

Este trabajo analiza el rendimiento del algoritmo de alineamiento de secuencias conocido como Needleman-Wunsch, sobre 3 sistemas de cómputo multiprocesador diferentes. Se analiza y se codifica el algoritmo serie usando el lenguaje de programación C y se plantean una serie de optimizaciones con la finalidad de minimizar el volumen y el tiempo de cómputo. Posteriormente, se realiza un análisis de las prestaciones del programa sobre los diferentes sistemas de cómputo. En la segunda parte del trabajo, se paraleliza el algoritmo serie y se codifica ayudándonos de OpenMP. El resultado son dos variantes del programa que difieren en la relación entre la cantidad de cómputo y la de comunicación. En la primera variante, la comunicación entre procesadores es poco frecuente y se realiza tras largos periodos de ejecución (granularidad gruesa). En cambio, en la segunda variante las tareas individuales son relativamente pequeñas en término de tiempo de ejecución y la comunicación entre los procesadores es frecuente (granularidad fina). Ambas variantes se ejecutan y analizan en arquitecturas multicore que explotan el paralelismo a nivel de thread. Los resultados obtenidos muestran la importancia de entender y saber analizar el efecto del multicore y multithreading en el rendimiento.

Factores de rendimiento en entornos multicore

Este documento refleja el estudio de investigación para la detección de factores que afectan al rendimiento en entornos multicore. Debido a la gran diversidad de arquitecturas multicore se ha definido un marco de trabajo, que consiste en la adopción de una arquitectura específica, un modelo de programación basado en paralelismo de datos, y aplicaciones del tipo Single Program Multiple Data. Una vez definido el marco de trabajo, se han evaluado los factores de rendimiento con especial atención al modelo de programación. Por este motivo, se ha analizado la librería de threads y la API OpenMP para detectar aquellas funciones sensibles de ser sintonizadas al permitir un comportamiento adaptativo de la aplicación al entorno, y que dependiendo de su adecuada utilización han de mejorar el rendimiento de la aplicación.

An introduction to model-independent diffusion magnetic resonance imaging.

ABSTRACT: q-Space-based techniques such as diffusion spectrum imaging, q-ball imaging, and their variations have been used extensively in research for their desired capability to delineate complex neuronal architectures such as multiple fiber crossings in each of the image voxels. The purpose of this article was to provide an introduction to the q-space formalism and the principles of basic q-space techniques together with the discussion on the advantages as well as challenges in translating these techniques into the clinical environment. A review of the currently used q-space-based protocols in clinical research is also provided.

Parallelization of whole genome alignment

With the advent of High performance computing, it is now possible to achieve orders of magnitude performance and computation e ciency gains over conventional computer architectures. This thesis explores the potential of using high performance computing to accelerate whole genome alignment. A parallel technique is applied to an algorithm for whole genome alignment, this technique is explained and some experiments were carried out to test it. This technique is based in a fair usage of the available resource to execute genome alignment and how this can be used in HPC clusters. This work is a rst approximation to whole genome alignment and it shows the advantages of parallelism and some of the drawbacks that our technique has. This work describes the resource limitations of current WGA applications when dealing with large quantities of sequences. It proposes a parallel heuristic to distribute the load and to assure that alignment quality is mantained.

LittleProc 2.0. : l'evolució als 16 bits, amb arquitectura superescalar i amb pipeline

Aquest projecte consisteix en evolucionar el LittleProc 1.0, un processador simple dissenyat per ser destinat al món de la docència per tres professors de la UAB. Aquestes evolucions consisteixen en aplicar diversos mètodes i arquitectures diferents per tal d’obtenir un millor rendiment del processador, arribant a executar programes amb la meitat de temps que tardava el LittleProc 1.0. Un cop implementades les diferents arquitectures per tal de millorar el rendiment, es realitzarà un estudi de quin tant per cent de millora ha sigut aquest rendiment.

Design of Novel Frequency Generation Circuit with Application to Ultra-Wideband Distributed Oscillators

Given the urgence of a new paradigm in wireless digital trasmission which should allow for higher bit rate, lower latency and tigher delay constaints, it has been proposed to investigate the fundamental building blocks that at the circuital/device level, will boost the change towards a more efficient network architecture, with high capacity, higher bandwidth and a more satisfactory end user experience. At the core of each transciever, there are inherently analog devices capable of providing the carrier signal, the oscillators. It is strongly believed that many limitations in today's communication protocols, could be relieved by permitting high carrier frequency radio transmission, and having some degree of reconfigurability. This led us to studying distributed oscillator architectures which work in the microwave range and possess wideband tuning capability. As microvave oscillators are essentially nonlinear devices, a full nonlinear analyis, synthesis, and optimization had to be considered for their implementation. Consequently, all the most used nonlinear numerical techniques in commercial EDA software had been reviewed. An application of all the aforementioned techniques has been shown, considering a systems of three coupled oscillator ("triple push" oscillator) in which the stability of the various oscillating modes has been studied. Provided that a certain phase distribution is maintained among the oscillating elements, this topology permits a rise in the output power of the third harmonic; nevertheless due to circuit simmetry, "unwanted" oscillating modes coexist with the intenteded one. Starting with the necessary background on distributed amplification and distributed oscillator theory, the design of a four stage reverse mode distributed voltage controlled oscillator (DVCO) using lumped elments has been presented. All the design steps have been reported and for the first time a method for an optimized design with reduced variations in the output power has been presented. Ongoing work is devoted to model a wideband DVCO and to implement a frequency divider.

Optimització d'una aplicació bioinformàtica d'aliniament de seqüències executada en processadors many-core (GPUs)

Las herramientas de análisis de secuencias genómicas permiten a los biólogos identificar y entender regiones fundamentales que tienen implicación en enfermedades genéticas. Actualmente existe una necesidad de dotar al ámbito científico de herramientas de análisis eficientes. Este proyecto lleva a cabo una caracterización y análisis del rendimiento de algoritmos utilizados en la comparación de secuencias genómicas completas, y ejecutadas en arquitecturas MultiCore y ManyCore. A partir del análisis se evalúa la idoneidad de este tipo de arquitecturas para resolver el problema de comparar secuencias genómicas. Finalmente se propone una serie de modificaciones en las implementaciones de estos algoritmos con el objetivo de mejorar el rendimiento.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

A Bayesian network approach to the database serch problem in criminal proceedings

Background The 'database search problem', that is, the strengthening of a case - in terms of probative value - against an individual who is found as a result of a database search, has been approached during the last two decades with substantial mathematical analyses, accompanied by lively debate and centrally opposing conclusions. This represents a challenging obstacle in teaching but also hinders a balanced and coherent discussion of the topic within the wider scientific and legal community. This paper revisits and tracks the associated mathematical analyses in terms of Bayesian networks. Their derivation and discussion for capturing probabilistic arguments that explain the database search problem are outlined in detail. The resulting Bayesian networks offer a distinct view on the main debated issues, along with further clarity. Methods As a general framework for representing and analyzing formal arguments in probabilistic reasoning about uncertain target propositions (that is, whether or not a given individual is the source of a crime stain), this paper relies on graphical probability models, in particular, Bayesian networks. This graphical probability modeling approach is used to capture, within a single model, a series of key variables, such as the number of individuals in a database, the size of the population of potential crime stain sources, and the rarity of the corresponding analytical characteristics in a relevant population. Results This paper demonstrates the feasibility of deriving Bayesian network structures for analyzing, representing, and tracking the database search problem. The output of the proposed models can be shown to agree with existing but exclusively formulaic approaches. Conclusions The proposed Bayesian networks allow one to capture and analyze the currently most well-supported but reputedly counter-intuitive and difficult solution to the database search problem in a way that goes beyond the traditional, purely formulaic expressions. The method's graphical environment, along with its computational and probabilistic architectures, represents a rich package that offers analysts and discussants with additional modes of interaction, concise representation, and coherent communication.

Multicast extension of unicast charging for QoS Services

In order to successfully deploy multicast services in QoS-aware networks, pricing architectures must take into account the particular characteristics of multicast sessions. With this objective, we propose a charging scheme for QoS multicast services, assuming that the unicast cost of each interconnecting link is determined and that such cost is expressed in terms of quality of service (QoS) parameters. Our scheme allows determining the cost distribution of a multicast session along a cost distribution tree (CDT), and basing such distribution in those pre-existing unicast cost functions. The paper discusses in detail the main characteristics of the problem in a realistic interdomain scenario and how the proposed scheme would contribute to its solution

Hybrid coordination of reinforcement learning-based behaviors for AUV control

This paper proposes a hybrid coordination method for behavior-based control architectures. The hybrid method takes advantages of the robustness and modularity in competitive approaches as well as optimized trajectories in cooperative ones. This paper shows the feasibility of applying this hybrid method with a 3D-navigation to an autonomous underwater vehicle (AUV). The behaviors are learnt online by means of reinforcement learning. A continuous Q-learning implemented with a feed-forward neural network is employed. Realistic simulations were carried out. The results obtained show the good performance of the hybrid method on behavior coordination as well as the convergence of the behaviors

On AUV control architecture

This paper surveys control architectures proposed in the literature and describes a control architecture that is being developed for a semi-autonomous underwater vehicle for intervention missions (SAUVIM) at the University of Hawaii. Conceived as hybrid, this architecture has been organized in three layers: planning, control and execution. The mission is planned with a sequence of subgoals. Each subgoal has a related task supervisor responsible for arranging a set of pre-programmed task modules in order to achieve the subgoal. Task modules are the key concept of the architecture. They are the main building blocks and can be dynamically re-arranged by the task supervisor. In our architecture, deliberation takes place at the planning layer while reaction is dealt through the parallel execution of the task modules. Hence, the system presents both a hierarchical and an heterarchical decomposition, being able to show a predictable response while keeping rapid reactivity to the dynamic environment

Label Space Reduction in MPLS Networks: How Much Can A Single Stacked Label Do?

Most network operators have considered reducing LSR label spaces (number of labels used) as a way of simplifying management of underlaying virtual private networks (VPNs) and therefore reducing operational expenditure (OPEX). The IETF outlined the label merging feature in MPLS-allowing the configuration of multipoint-to-point connections (MP2P)-as a means of reducing label space in LSRs. We found two main drawbacks in this label space reduction a)it should be separately applied to a set of LSPs with the same egress LSR-which decreases the options for better reductions, and b)LSRs close to the edge of the network experience a greater label space reduction than those close to the core. The later implies that MP2P connections reduce the number of labels asymmetrically

Homology modeling and metabolism prediction of human carboxylesterase-2 using docking analyses by GriDock: a parallelized tool based on AutoDock 4.0.

Metabolic problems lead to numerous failures during clinical trials, and much effort is now devoted to developing in silico models predicting metabolic stability and metabolites. Such models are well known for cytochromes P450 and some transferases, whereas less has been done to predict the activity of human hydrolases. The present study was undertaken to develop a computational approach able to predict the hydrolysis of novel esters by human carboxylesterase hCES2. The study involved first a homology modeling of the hCES2 protein based on the model of hCES1 since the two proteins share a high degree of homology (congruent with 73%). A set of 40 known substrates of hCES2 was taken from the literature; the ligands were docked in both their neutral and ionized forms using GriDock, a parallel tool based on the AutoDock4.0 engine which can perform efficient and easy virtual screening analyses of large molecular databases exploiting multi-core architectures. Useful statistical models (e.g., r (2) = 0.91 for substrates in their unprotonated state) were calculated by correlating experimental pK(m) values with distance between the carbon atom of the substrate's ester group and the hydroxy function of Ser228. Additional parameters in the equations accounted for hydrophobic and electrostatic interactions between substrates and contributing residues. The negatively charged residues in the hCES2 cavity explained the preference of the enzyme for neutral substrates and, more generally, suggested that ligands which interact too strongly by ionic bonds (e.g., ACE inhibitors) cannot be good CES2 substrates because they are trapped in the cavity in unproductive modes and behave as inhibitors. The effects of protonation on substrate recognition and the contrasting behavior of substrates and products were finally investigated by MD simulations of some CES2 complexes.

Exploring Cross-layer power management for PGAS applications on the SCC platform

Technological limitations and power constraints are resulting in high-performance parallel computing architectures that are based on large numbers of high-core-count processors. Commercially available processors are now at 8 and 16 cores and experimental platforms, such as the many-core Intel Single-chip Cloud Computer (SCC) platform, provide much higher core counts. These trends are presenting new sets of challenges to HPC applications including programming complexity and the need for extreme energy efficiency.In this work, we first investigate the power behavior of scientific PGAS application kernels on the SCC platform, and explore opportunities and challenges for power management within the PGAS framework. Results obtained via empirical evaluation of Unified Parallel C (UPC) applications on the SCC platform under different constraints, show that, for specific operations, the potential for energy savings in PGAS is large; and power/performance trade-offs can be effectively managed using a cross-layerapproach. We investigate cross-layer power management using PGAS language extensions and runtime mechanisms that manipulate power/performance tradeoffs. Specifically, we present the design, implementation and evaluation of such a middleware for application-aware cross-layer power management of UPC applications on the SCC platform. Finally, based on our observations, we provide a set of recommendations and insights that can be used to support similar power management for PGAS applications on other many-core platforms.