Performance Evaluation of Plasma and Astrophysics Applications on Modern Parallel Vector Systems

Abstract. The last decade has witnessed a rapid proliferation of superscalar cache-based microprocessors to build high-end computing (HEC) platforms, primarily because of their generality, scalability, and cost effectiveness. However, the growing gap between sustained and peak performance for full-scale scientific applications on such platforms has become major concern in high performance computing. The latest generation of custom-built parallel vector systems have the potential to address this concern for numerical algorithms with sufficient regularity in their computational structure. In this work, we explore two and three dimensional implementations of a plasma physics application, as well as a leading astrophysics package on some of today's most powerful supercomputing platforms. Results compare performance between the the vector-based Cray X1, Earth Simulator, and newly-released NEC SX-8, with the commodity-based superscalar platforms of the IBM Power3, Intel Itanium2, and AMD Opteron. Overall results show that the SX-8 attains unprecedented aggregate performance across our evaluated applications

Performance Evaluation of Plasma and Astrophysics Applications onModern Parallel Vector Systems

The last decade has witnessed a rapid proliferation ofsuperscalar cache-based microprocessors to build high-endcomputing (HEC)platforms, primarily because of their generality,scalability, and costeffectiveness. However, the growing gap between sustained and peakperformance for full-scale scientific applications on such platforms hasbecome major concern in highperformance computing. The latest generationof custom-built parallel vector systems have the potential to addressthis concern for numerical algorithms with sufficient regularity in theircomputational structure. In this work, we explore two and threedimensional implementations of a plasma physics application, as well as aleading astrophysics package on some of today's most powerfulsupercomputing platforms. Results compare performance between the thevector-based Cray X1, EarthSimulator, and newly-released NEC SX- 8, withthe commodity-based superscalar platforms of the IBM Power3, IntelItanium2, and AMDOpteron. Overall results show that the SX-8 attainsunprecedented aggregate performance across our evaluatedapplications

Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

Interoperability and computational framework for simulating open channel hydraulics: application to sensitivity analysis and calibration of Gironde Estuary model

Water resource management is of crucial societal and economic importance, requiring a strong capacity for anticipating environmental change. Progress in physical process knowledge, numerical methods and computational power, allows us to address hydro-environmental problems of growing complexity. Modeling of river and marine flows is no exception. With the increase in IT resources, environmental modeling is evolving to meet the challenges of complex real-world problems. This paper presents a new distributed Application Programming Interface (API) of the open source TELEMAC-MASCARET system to run hydro-environmental simulations with the help of the interoperability concept. Use of the API encourages and facilitates the combination of worldwide reference environmental libraries with the hydro-informatic system. Consequently, the objective of the paper is to promote the interoperability concept for studies dealing with such issues as uncertainty propagation, global sensitivity analysis, optimization, multi-physics or multi-dimensional coupling. To illustrate the capability of the API, an operational problem for improving the navigation capacity of the Gironde Estuary is presented. The API potential is demonstrated in a re-calibration context. The API is used for a multivariate sensitivity analysis to quickly reveal the most influential parameters which can then be optimally calibrated with the help of a data assimilation technique

Modelling the Eddy Pattern in the harbour of Zeebrugge

Hydrodynamic

Scientific and Computational Challenges of the Fusion Simulation Program (FSP)

This paper highlights the scientific and computational challenges facing the Fusion Simulation Program (FSP) a major national initiative in the United States with the primary objective being to enable scientific discovery of important new plasma phenomena with associated understanding that emerges only upon integration. This requires developing a predictive integrated simulation capability for magnetically-confined fusion plasmas that are properly validated against experiments in regimes relevant for producing practical fusion energy. It is expected to provide a suite of advanced modeling tools for reliably predicting fusion device behavior with comprehensive and targeted science-based simulations of nonlinearly-coupled phenomena in the core plasma, edge plasma, and wall region on time and space scales required for fusion energy production. As such, it will strive to embody the most current theoretical and experimental understanding of magnetic fusion plasmas and to provide a living framework for the simulation of such plasmas as the associated physics understanding continues to advance over the next several decades. Substantive progress on answering the outstanding scientific questions in the field will drive the FSP toward its ultimate goal of developing the ability to predict the behavior of plasma discharges in toroidal magnetic fusion devices with high physics fidelity on all relevant time and space scales. From a computational perspective, this will demand computing resources in the petascale range and beyond together with the associated multi-core algorithmic formulation needed to address burning plasma issues relevant to ITER - a multibillion dollar collaborative experiment involving seven international partners representing over half the world's population. Even more powerful exascale platforms will be needed to meet the future challenges of designing a demonstration fusion reactor (DEMO). Analogous to other major applied physics modeling projects (e.g., Climate Modeling), the FSP will need to develop software in close collaboration with computers scientists and applied mathematicians and validated against experimental data from tokamaks around the world. Specific examples of expected advances needed to enable such a comprehensive integrated modeling capability and possible "co-design" approaches will be discussed. _________________________________________________

Three Experiments on Complex Fluids

The behaviour of complex fluids is fundamentally interesting and important in many applications. This thesis reports on three experiments on the thermal and rheological behaviour of complex fluids. The first is a study of the rheological properties of and heat transport in a saline solution of hydroxyethyl cellulose. This material has been used as a tissue phantom in testing the behavior of medical devices in MRI scanners. We find it behaves as a typical entangled polymer, and flows in response to local heating, such as could occur due to eddy-current heating of metallic devices in an MR scanner. We use laboratory experiments and numerical simulations to determine the convective and conductive contributions to the heat transport in a simple model of this system. Our results indicate that convective heat transport is of the same order of magnitude as conductive transport under conditions typical of MRI device tests. The second project is an investigation of the start-up flow and yielding of a simple yield-stress fluid (Carbopol 940) in a vertical pipe. The Carbopol was displaced from below by an immiscible Newtonian liquid (Fluorinert FC-40) injected at a constant, controlled rate. Rough and smooth-walled pipes were used to study the effects of wall boundary conditions. In the rough-walled pipe, the yielding involved a long transient with several steps: elastic deformation, the onset of wall slip, yielding at the wall, and finally a steady-state plug flow that is well-described by the predictions of the Herschel-Bulkley model. In contrast, in the smooth-walled pipe, the wall shear stress never exceeded the yield stress. In the third project, we study the flow of Carbopol solutions confined to square microchannels with sides ranging from 500 down to 50 um. In the larger channels, the measured velocity profiles agreed well with simulations based on the bulks-scale rheology of the Carbopol and the Herschel-Bulkley model. In contrast, in microchannels with sides less than 150 um the velocity profiles could not be fitted by a model with a finite yield stress, but instead were described by a power-law model with zero yield stress. We explain the vanishing of the yield stress in terms of the confinement of the Carbopol’s microstructure by the microchannels

ベクトル型スーパーコンピュータのためのメモリ指向型最適化戦略に関する研究

Tohoku University小林広明課

Multi-agent system for flood forecasting in Tropical River Basin

It is well known, the problems related to the generation of floods, their control, and management, have been treated with traditional hydrologic modeling tools focused on the study and the analysis of the precipitation-runoff relationship, a physical process which is driven by the hydrological cycle and the climate regime and that is directly proportional to the generation of floodwaters. Within the hydrological discipline, they classify these traditional modeling tools according to three principal groups, being the first group defined as trial-and-error models (e.g., "black-models"), the second group are the conceptual models, which are categorized in three main sub-groups as "lumped", "semi-lumped" and "semi-distributed", according to the special distribution, and finally, models that are based on physical processes, known as "white-box models" are the so-called "distributed-models". On the other hand, in engineering applications, there are two types of models used in streamflow forecasting, and which are classified concerning the type of measurements and variables required as "physically based models", as well as "data-driven models". The Physically oriented prototypes present an in-depth account of the dynamics related to the physical aspects that occur internally among the different systems of a given hydrographic basin. However, aside from being laborious to implement, they rely thoroughly on mathematical algorithms, and an understanding of these interactions requires the abstraction of mathematical concepts and the conceptualization of the physical processes that are intertwined among these systems. Besides, models determined by data necessitates an a-priori understanding of the physical laws controlling the process within the system, and they are bound to mathematical formulations, which require a lot of numeric information for field adjustments. Therefore, these models are remarkably different from each other because of their needs for data, and their interpretation of physical phenomena. Although there is considerable progress in hydrologic modeling for flood forecasting, several significant setbacks remain unresolved, given the stochastic nature of the hydrological phenomena, is the challenge to implement user-friendly, re-usable, robust, and reliable forecasting systems, the amount of uncertainty they must deal with when trying to solve the flood forecasting problem. However, in the past decades, with the growing environment and development of the artificial intelligence (AI) field, some researchers have seldomly attempted to deal with the stochastic nature of hydrologic events with the application of some of these techniques. Given the setbacks to hydrologic flood forecasting previously described this thesis research aims to integrate the physics-based hydrologic, hydraulic, and data-driven models under the paradigm of Multi-agent Systems for flood forecasting by designing and developing a multi-agent system (MAS) framework for flood forecasting events within the scope of tropical watersheds. With the emergence of the agent technologies, the "agent-based modeling" and "multiagent systems" simulation methods have provided applications for some areas of hydro base management like flood protection, planning, control, management, mitigation, and forecasting to combat the shocks produced by floods on society; however, all these focused on evacuation drills, and the latter not aimed at the tropical river basin, whose hydrological regime is extremely unique. In this catchment modeling environment approach, it was applied the multi-agent systems approach as a surrogate of the conventional hydrologic model to build a system that operates at the catchment level displayed with hydrometric stations, that use the data from hydrometric sensors networks (e.g., rainfall, river stage, river flow) captured, stored and administered by an organization of interacting agents whose main aim is to perform flow forecasting and awareness, and in so doing enhance the policy-making process at the watershed level. Section one of this document surveys the status of the current research in hydrologic modeling for the flood forecasting task. It is a journey through the background of related concerns to the hydrological process, flood ontologies, management, and forecasting. The section covers, to a certain extent, the techniques, methods, and theoretical aspects and methods of hydrological modeling and their types, from the conventional models to the present-day artificial intelligence prototypes, making special emphasis on the multi-agent systems, as most recent modeling methodology in the hydrological sciences. However, it is also underlined here that the section does not contribute to an all-inclusive revision, rather its purpose is to serve as a framework for this sort of work and a path to underline the significant aspects of the works. In section two of the document, it is detailed the conceptual framework for the suggested Multiagent system in support of flood forecasting. To accomplish this task, several works need to be carried out such as the sketching and implementation of the system’s framework with the (Belief-Desire-Intention model) architecture for flood forecasting events within the concept of the tropical river basin. Contributions of this proposed architecture are the replacement of the conventional hydrologic modeling with the use of multi-agent systems, which makes it quick for hydrometric time-series data administration and modeling of the precipitation-runoff process which conveys to flood in a river course. Another advantage is the user-friendly environment provided by the proposed multi-agent system platform graphical interface, the real-time generation of graphs, charts, and monitors with the information on the immediate event taking place in the catchment, which makes it easy for the viewer with some or no background in data analysis and their interpretation to get a visual idea of the information at hand regarding the flood awareness. The required agents developed in this multi-agent system modeling framework for flood forecasting have been trained, tested, and validated under a series of experimental tasks, using the hydrometric series information of rainfall, river stage, and streamflow data collected by the hydrometric sensor agents from the hydrometric sensors.Como se sabe, los problemas relacionados con la generación de inundaciones, su control y manejo, han sido tratados con herramientas tradicionales de modelado hidrológico enfocados al estudio y análisis de la relación precipitación-escorrentía, proceso físico que es impulsado por el ciclo hidrológico y el régimen climático y este esta directamente proporcional a la generación de crecidas. Dentro de la disciplina hidrológica, clasifican estas herramientas de modelado tradicionales en tres grupos principales, siendo el primer grupo el de modelos empíricos (modelos de caja negra), modelos conceptuales (o agrupados, semi-agrupados o semi-distribuidos) dependiendo de la distribución espacial y, por último, los basados en la física, modelos de proceso (o "modelos de caja blanca", y/o distribuidos). En este sentido, clasifican las aplicaciones de predicción de caudal fluvial en la ingeniería de recursos hídricos en dos tipos con respecto a los valores y parámetros que requieren en: modelos de procesos basados en la física y la categoría de modelos impulsados por datos. Los modelos basados en la física proporcionan una descripción detallada de la dinámica relacionada con los aspectos físicos que ocurren internamente entre los diferentes sistemas de una cuenca hidrográfica determinada. Sin embargo, aparte de ser complejos de implementar, se basan completamente en algoritmos matemáticos, y la comprensión de estas interacciones requiere la abstracción de conceptos matemáticos y la conceptualización de los procesos físicos que se entrelazan entre estos sistemas. Además, los modelos impulsados por datos no requieren conocimiento de los procesos físicos que gobiernan, sino que se basan únicamente en ecuaciones empíricas que necesitan una gran cantidad de datos y requieren calibración de los datos en el sitio. Los dos modelos difieren significativamente debido a sus requisitos de datos y de cómo expresan los fenómenos físicos. La elaboración de modelos hidrológicos para el pronóstico de inundaciones ha dado grandes pasos, pero siguen sin resolverse algunos contratiempos importantes, dada la naturaleza estocástica de los fenómenos hidrológicos, es el desafío de implementar sistemas de pronóstico fáciles de usar, reutilizables, robustos y confiables, la cantidad de incertidumbre que deben afrontar al intentar resolver el problema de la predicción de inundaciones. Sin embargo, en las últimas décadas, con el entorno creciente y el desarrollo del campo de la inteligencia artificial (IA), algunos investigadores rara vez han intentado abordar la naturaleza estocástica de los eventos hidrológicos con la aplicación de algunas de estas técnicas. Dados los contratiempos en el pronóstico de inundaciones hidrológicas descritos anteriormente, esta investigación de tesis tiene como objetivo integrar los modelos hidrológicos, basados en la física, hidráulicos e impulsados por datos bajo el paradigma de Sistemas de múltiples agentes para el pronóstico de inundaciones por medio del bosquejo y desarrollo del marco de trabajo del sistema multi-agente (MAS) para los eventos de predicción de inundaciones en el contexto de cuenca hidrográfica tropical. Con la aparición de las tecnologías de agentes, se han emprendido algunos enfoques de simulación recientes en la investigación hidrológica con modelos basados en agentes y sistema multi-agente, principalmente en alerta por inundaciones, seguridad y planificación de inundaciones, control y gestión de inundaciones y pronóstico de inundaciones, todos estos enfocado a simulacros de evacuación, y este último no dirigido a la cuenca tropical, cuyo régimen hidrológico es extremadamente único. En este enfoque de entorno de modelado de cuencas, se aplican los enfoques de sistemas multi-agente como un sustituto del modelado hidrológico convencional para construir un sistema que opera a nivel de cuenca con estaciones hidrométricas desplegadas, que utilizan los datos de redes de sensores hidrométricos (por ejemplo, lluvia , nivel del río, caudal del río) capturado, almacenado y administrado por una organización de agentes interactuantes cuyo objetivo principal es realizar pronósticos de caudal y concientización para mejorar las capacidades de soporte en la formulación de políticas a nivel de cuenca hidrográfica. La primera sección de este documento analiza el estado del arte sobre la investigación actual en modelos hidrológicos para la tarea de pronóstico de inundaciones. Es un viaje a través de los antecedentes preocupantes relacionadas con el proceso hidrológico, las ontologías de inundaciones, la gestión y la predicción. El apartado abarca, en cierta medida, las técnicas, métodos y aspectos teóricos y métodos del modelado hidrológico y sus tipologías, desde los modelos convencionales hasta los prototipos de inteligencia artificial actuales, haciendo hincapié en los sistemas multi-agente, como un enfoque de simulación reciente en la investigación hidrológica. Sin embargo, se destaca que esta sección no contribuye a una revisión integral, sino que su propósito es servir de marco para este tipo de trabajos y una guía para subrayar los aspectos significativos de los trabajos. En la sección dos del documento, se detalla el marco de trabajo propuesto para el sistema multi-agente para el pronóstico de inundaciones. Los trabajos realizados comprendieron el diseño y desarrollo del marco de trabajo del sistema multi-agente con la arquitectura (modelo Creencia-Deseo-Intención) para la predicción de eventos de crecidas dentro del concepto de cuenca hidrográfica tropical. Las contribuciones de esta arquitectura propuesta son el reemplazo del modelado hidrológico convencional con el uso de sistemas multi-agente, lo que agiliza la administración de las series de tiempo de datos hidrométricos y el modelado del proceso de precipitación-escorrentía que conduce a la inundación en el curso de un río. Otra ventaja es el entorno amigable proporcionado por la interfaz gráfica de la plataforma del sistema multi-agente propuesto, la generación en tiempo real de gráficos, cuadros y monitores con la información sobre el evento inmediato que tiene lugar en la cuenca, lo que lo hace fácil para el espectador con algo o sin experiencia en análisis de datos y su interpretación para tener una idea visual de la información disponible con respecto a la cognición de las inundaciones. Los agentes necesarios desarrollados en este marco de modelado de sistemas multi-agente para el pronóstico de inundaciones han sido entrenados, probados y validados en una serie de tareas experimentales, utilizando la información de la serie hidrométrica de datos de lluvia, nivel del río y flujo del curso de agua recolectados por los agentes sensores hidrométricos de los sensores hidrométricos de campo.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: María Araceli Sanchis de Miguel.- Secretario: Juan Gómez Romero.- Vocal: Juan Carlos Corrale

Addressing Data Resolution in Precision Agriculture

Irrigated agriculture constitutes the greatest consumptive water use globally, so that irrigation efficiency measures are an important part of global efforts to best utilize this limited resource. However, greater irrigation efficiency must be achieved while simultaneously maintaining or increasing crop yields and farming profitability. Incremental water use decisions are made at the local level by farmers under many real world constraints; consequently they face significant risks in operating large and complex irrigation systems. These decisions should be supported by reliable information upon which to base operational plans and irrigation scheduling. Implementing precision irrigation effectively depends upon highly resolved estimates of crop water demand so that application rates match demand precisely both in location and timing. A fundamental challenge in mapping the irrigation requirement is addressing the heterogeneity of soil, biophysical, and atmospheric processes which mediate water demand. However, existing methods to determine the irrigation requirement assume that field conditions are homogeneous. Precision irrigation systems may enable more specific water distribution than traditional irrigation equipment, but allocating the correct amount of water requires crop water estimates that accurately reflect the variability of the irrigation requirement and consider the scale and timing at which irrigation can be delivered. This dissertation synthesizes the results from field studies which analyzed spatial patterns of irrigation performance and crop water demand under real field conditions. The first experiment quantified the performance of a precision irrigation system and determined the data resolution required for effective utilization of the system’s capability (Chapter 2). Field trials were conducted with a variable rate center pivot sprinkler (VRI) under normal farming conditions to determine this spatial resolution. The result was the definition of a performance coefficient and characteristic length scale associated with the irrigation system. The characteristic length scale describes the highest resolution prescription possible with VRI. Following on these findings, a second study compares an electromagnetic (EM) soil mapping method using extensive laboratory soil characterization as a basis for comparison (Chapter 3). The motivation of the study was to validate the EM method’s capability to detect small scale variations in soil water holding capacity, and to determine under which conditions the EM method can obtain reliable and robust soil maps. The findings reinforce earlier work on the importance of instrument calibration, and also show that specific soil characteristics may preclude using EM methods to map soil in some regions. Following the soil mapping study, further studies investigated methods to measure crop evapotranspiration (ET). A literature review was conducted to establish a catalog of contemporary methods to monitor ET, focusing on those commonly used in agriculture (Chapter 4). From this review, the surface renewal method (SR) emerged as potentially able to map ET feasibly and cost-effectively. Four field experiments were conducted over two years under a range of field conditions to establish a robust protocol for the determination of surface fluxes with SR (Chapter 5). Three of these experiments specifically investigated the potential for SR to be implemented from a moving sensor platform, such as an unmanned aerial vehicle. Experiments showed SR could estimate sensible heat flux as accurately as eddy covariance during moving trials. However, analysis of the minimum flux averaging period demonstrated that SR cannot resolve fluxes at the requisite spatial scales for precision irrigation. Nonetheless, SR remains promising for other practical applications in measuring surface fluxes. Future research questions and potential applications are explored in Chapters 5 and 6. The methods described here are directly relevant to water managers at the levels of farms and irrigation districts. Efficient irrigation planning depends on timely, reliable, and site-specific information in order to anticipate crop water demand, irrigate adequately to prevent drought stress, and maximize yield from the available resource. Growers and irrigation specialists currently have many resources at their disposal, including regional and satellite based ET estimates, state and local soil mapping, and scientific irrigation planning software. However, these methods do not provide site-specific and real time measurements of actual crop water demand, and farmers do not have any reliable means by which to validate the accuracy and precision of these estimates. For this information to be directly useful in irrigation planning, it should be validated by on site measurements. Reliable, local, and real time information is required to realize the full potential of precision agriculture