Exploring New Search Algorithms and Hardware for Phylogenetics: RAxML Meets the IBM Cell

Phylogenetic inference is considered to be one of the grand challenges in Bioinformatics due to the immense computational requirements. RAxML is currently among the fastest and most accurate programs for phylogenetic tree inference under the Maximum Likelihood (ML) criterion. First, we introduce new tree search heuristics that accelerate RAxML by a factor of 2.43 while returning equally good trees. The performance of the new search algorithm has been assessed on 18 real-world datasets comprising 148 up to 4,843 DNA sequences. We then present the implementation, optimization, and evaluation of RAxML on the IBM Cell Broadband Engine. We address the problems and provide solutions pertaining to the optimization of floating point code, control flow, communication, and scheduling of multi-level parallelism on the Cel

Electromagnetic Radiation

The application of electromagnetic radiation in modern life is one of the most developing technologies. In this timely book, the authors comprehensively treat two integrated aspects of electromagnetic radiation, theory and application. It covers a wide scope of practical topics, including medical treatment, telecommunication systems, and radiation effects. The book sections have clear presentation, some state of the art examples, which makes this book an indispensable reference book for electromagnetic radiation applications

Learning for Optimization with Virtual Savant

Optimization problems arising in multiple fields of study demand efficient algorithms that can exploit modern parallel computing platforms. The remarkable development of machine learning offers an opportunity to incorporate learning into optimization algorithms to efficiently solve large and complex problems. This thesis explores Virtual Savant, a paradigm that combines machine learning and parallel computing to solve optimization problems. Virtual Savant is inspired in the Savant Syndrome, a mental condition where patients excel at a specific ability far above the average. In analogy to the Savant Syndrome, Virtual Savant extracts patterns from previously-solved instances to learn how to solve a given optimization problem in a massively-parallel fashion. In this thesis, Virtual Savant is applied to three optimization problems related to software engineering, task scheduling, and public transportation. The efficacy of Virtual Savant is evaluated in different computing platforms and the experimental results are compared against exact and approximate solutions for both synthetic and realistic instances of the studied problems. Results show that Virtual Savant can find accurate solutions, effectively scale in the problem dimension, and take advantage of the availability of multiple computing resources.Los problemas de optimización que surgen en múltiples campos de estudio demandan algoritmos eficientes que puedan explotar las plataformas modernas de computación paralela. El notable desarrollo del aprendizaje automático ofrece la oportunidad de incorporar el aprendizaje en algoritmos de optimización para resolver problemas complejos y de grandes dimensiones de manera eficiente. Esta tesis explora Savant Virtual, un paradigma que combina aprendizaje automático y computación paralela para resolver problemas de optimización. Savant Virtual está inspirado en el Sı́ndrome de Savant, una condición mental en la que los pacientes se destacan en una habilidad especı́fica muy por encima del promedio. En analogı́a con el sı́ndrome de Savant, Savant Virtual extrae patrones de instancias previamente resueltas para aprender a resolver un determinado problema de optimización de forma masivamente paralela. En esta tesis, Savant Virtual se aplica a tres problemas de optimización relacionados con la ingenierı́a de software, la planificación de tareas y el transporte público. La eficacia de Savant Virtual se evalúa en diferentes plataformas informáticas y los resultados se comparan con soluciones exactas y aproximadas para instancias tanto sintéticas como realistas de los problemas estudiados. Los resultados muestran que Savant Virtual puede encontrar soluciones precisas, escalar eficazmente en la dimensión del problema y aprovechar la disponibilidad de múltiples recursos de cómputo.Fundación Carolina Agencia Nacional de Investigación e Innovación (ANII, Uruguay) Universidad de Cádiz Universidad de la Repúblic

Towards Intelligent Runtime Framework for Distributed Heterogeneous Systems

Scientific applications strive for increased memory and computing performance, requiring massive amounts of data and time to produce results. Applications utilize large-scale, parallel computing platforms with advanced architectures to accommodate their needs. However, developing performance-portable applications for modern, heterogeneous platforms requires lots of effort and expertise in both the application and systems domains. This is more relevant for unstructured applications whose workflow is not statically predictable due to their heavily data-dependent nature. One possible solution for this problem is the introduction of an intelligent Domain-Specific Language (iDSL) that transparently helps to maintain correctness, hides the idiosyncrasies of lowlevel hardware, and scales applications. An iDSL includes domain-specific language constructs, a compilation toolchain, and a runtime providing task scheduling, data placement, and workload balancing across and within heterogeneous nodes. In this work, we focus on the runtime framework. We introduce a novel design and extension of a runtime framework, the Parallel Runtime Environment for Multicore Applications. In response to the ever-increasing intra/inter-node concurrency, the runtime system supports efficient task scheduling and workload balancing at both levels while allowing the development of custom policies. Moreover, the new framework provides abstractions supporting the utilization of heterogeneous distributed nodes consisting of CPUs and GPUs and is extensible to other devices. We demonstrate that by utilizing this work, an application (or the iDSL) can scale its performance on heterogeneous exascale-era supercomputers with minimal effort. A future goal for this framework (out of the scope of this thesis) is to be integrated with machine learning to improve its decision-making and performance further. As a bridge to this goal, since the framework is under development, we experiment with data from Nuclear Physics Particle Accelerators and demonstrate the significant improvements achieved by utilizing machine learning in the hit-based track reconstruction process

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Parallel source code transformation techniques using design patterns

Mención Internacional en el título de doctorIn recent years, the traditional approaches for improving performance, such as increasing the clock frequency, has come to a dead-end. To tackle this issue, parallel architectures, such as multi-/many-core processors, have been envisioned to increase the performance by providing greater processing capabilities. However, programming efficiently for this architectures demands big efforts in order to transform sequential applications into parallel and to optimize such applications. Compared to sequential programming, designing and implementing parallel applications for operating on modern hardware poses a number of new challenges to developers such as data races, deadlocks, load imbalance, etc. To pave the way, parallel design patterns provide a way to encapsulate algorithmic aspects, allowing users to implement robust, readable and portable solutions with such high-level abstractions. Basically, these patterns instantiate parallelism while hiding away the complexity of concurrency mechanisms, such as thread management, synchronizations or data sharing. Nonetheless, frameworks following this philosophy does not share the same interface and users require understanding different libraries, and their capabilities, not only to decide which fits best for their purposes but also to properly leverage them. Furthermore, in order to parallelize these applications, it is necessary to analyze the sequential code in order to detect the regions of code that can be parallelized that is a time consuming and complex task. Additionally, different libraries targeted to specific devices provide some algorithms implementations that are already parallel and highly-tuned. In these situations, it is also necessary to analyze and determine which routine implementation is the most suitable for a given problem. To tackle these issues, this thesis aims at simplifying and minimizing the necessary efforts to transform sequential applications into parallel. This way, resulting codes will improve their performance by fully exploiting the available resources while the development efforts will be considerably reduced. Basically, in this thesis, we contribute with the following. First, we propose a technique to detect potential parallel patterns in sequential code. Second, we provide a novel generic C++ interface for parallel patterns which acts as a switch among existing frameworks. Third, we implement a framework that is able to transform sequential code into parallel using the proposed pattern discovery technique and pattern interface. Finally, we propose mechanisms that are able to select the most suitable device and routine implementation to solve a given problem based on previous performance information. The evaluation demonstrates that using the proposed techniques can minimize the refactoring and optimization time while improving the performance of the resulting applications with respect to the original code.En los últimos años, las técnicas tradicionales para mejorar el rendimiento, como es el caso del incremento de la frecuencia de reloj, han llegado a sus límites. Con el fin de seguir mejorando el rendimiento, se han desarrollado las arquitecturas paralelas, las cuales proporcionan un incremento del rendimiento al estar provistas de mayores capacidades de procesamiento. Sin embargo, programar de forma eficiente para estas arquitecturas requieren de grandes esfuerzos por parte de los desarrolladores. Comparado con la programación secuencial, diseñar e implementar aplicaciones paralelas enfocadas a trabajar en estas arquitecturas presentan una gran cantidad de dificultades como son las condiciones de carrera, los deadlocks o el incorrecto balanceo de la carga. En este sentido, los patrones paralelos son una forma de encapsular aspectos algorítmicos de las aplicaciones permitiendo el desarrollo de soluciones robustas, portables y legibles gracias a las abstracciones de alto nivel. En general, estos patrones son capaces de proporcionar el paralelismo a la vez que ocultan las complejidades derivadas de los mecanismos de control de concurrencia necesarios como el manejo de los hilos, las sincronizaciones o la compartición de datos. No obstante, los diferentes frameworks que siguen esta filosofía no comparten una única interfaz lo que conlleva que los usuarios deban conocer múltiples bibliotecas y sus capacidades, con el fin de decidir cuál de ellos es mejor para una situación concreta y como usarlos de forma eficiente. Además, con el fin de paralelizar aplicaciones existentes, es necesario analizar e identificar las regiones del código que pueden ser paralelizadas, lo cual es una tarea ardua y compleja. Además, algunos algoritmos ya se encuentran implementados en paralelo y optimizados para arquitecturas concretas en diversas bibliotecas. Esto da lugar a que sea necesario analizar y determinar que implementación concreta es la más adecuada para solucionar un problema dado. Para paliar estas situaciones, está tesis busca simplificar y minimizar el esfuerzo necesario para transformar aplicaciones secuenciales en paralelas. De esta forma, los códigos resultantes serán capaces de explotar los recursos disponibles a la vez que se reduce considerablemente el esfuerzo de desarrollo necesario. En general, esta tesis contribuye con lo siguiente. En primer lugar, se propone una técnica de detección de patrones paralelos en códigos secuenciales. En segundo lugar, se presenta una interfaz genérica de patrones paralelos para C++ que permite seleccionar la implementación de dichos patrones proporcionada por frameworks ya existentes. En tercer lugar, se introduce un framework de transformación de código secuencial a paralelo que hace uso de las técnicas de detección de patrones y la interfaz presentadas. Finalmente, se proponen mecanismos capaces de seleccionar la implementación más adecuada para solucionar un problema concreto basándose en el rendimiento obtenido en ejecuciones previas. Gracias a la evaluación realizada se ha podido demostrar que uso de las técnicas presentadas pueden minimizar el tiempo necesario para transformar y optimizar el código a la vez que mejora el rendimiento de las aplicaciones transformadas.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: David Expósito Singh.- Secretario: Rafael Asenjo Plaza.- Vocal: Marco Aldinucc

High Performance Computing Techniques to Better Understand Protein Conformational Space

This thesis presents an amalgamation of high performance computing techniques to get better insight into protein molecular dynamics. Key aspects of protein function and dynamics can be learned from their conformational space. Datasets that represent the complex nuances of a protein molecule are high dimensional. Efficient dimensionality reduction becomes indispensable for the analysis of such exorbitant datasets. Dimensionality reduction forms a formidable portion of this work and its application has been explored for other datasets as well. It begins with the parallelization of a known non-liner feature reduction algorithm called Isomap. The code for the algorithm was re-written in C with portions of it parallelized using OpenMP. Next, a novel data instance reduction method was devised which evaluates the information content offered by each data point, which ultimately helps in truncation of the dataset with much fewer data points to evaluate. Once a framework has been established to reduce the number of variables representing a dataset, the work is extended to explore algebraic topology techniques to extract meaningful information from these datasets. This step is the one that helps in sampling the conformations of interest of a protein molecule. The method employs the notion of hierarchical clustering to identify classes within a molecule, thereafter, algebraic topology is used to analyze these classes. Finally, the work is concluded by presenting an approach to solve the open problem of protein folding. A Monte-Carlo based tree search algorithm is put forth to simulate the pathway that a certain protein conformation undertakes to reach another conformation. The dissertation, in its entirety, offers solutions to a few problems that hinder the progress of solution for the vast problem of understanding protein dynamics. The motion of a protein molecule is guided by changes in its energy profile. In this course the molecule gradually slips from one energy class to another. Structurally, this switch is transient spanning over milliseconds or less and hence is difficult to be captured solely by the work in wet laboratories

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications