Supporting distributed computation over wide area gigabit networks

The advent of high bandwidth fibre optic links that may be used over very large distances has lead to much research and development in the field of wide area gigabit networking. One problem that needs to be addressed is how loosely coupled distributed systems may be built over these links, allowing many computers worldwide to take part in complex calculations in order to solve "Grand Challenge" problems. The research conducted as part of this PhD has looked at the practicality of implementing a communication mechanism proposed by Craig Partridge called Late-binding Remote Procedure Calls (LbRPC). LbRPC is intended to export both code and data over the network to remote machines for evaluation, as opposed to traditional RPC mechanisms that only send parameters to pre-existing remote procedures. The ability to send code as well as data means that LbRPC requests can overcome one of the biggest problems in Wide Area Distributed Computer Systems (WADCS): the fixed latency due to the speed of light. As machines get faster, the fixed multi-millisecond round trip delay equates to ever increasing numbers of CPU cycles. For a WADCS to be efficient, programs should minimise the number of network transits they incur. By allowing the application programmer to export arbitrary code to the remote machine, this may be achieved. This research has looked at the feasibility of supporting secure exportation of arbitrary code and data in heterogeneous, loosely coupled, distributed computing environments. It has investigated techniques for making placement decisions for the code in cases where there are a large number of widely dispersed remote servers that could be used. The latter has resulted in the development of a novel prototype LbRPC using multicast IP for implicit placement and a sequenced, multi-packet saturation multicast transport protocol. These prototypes show that it is possible to export code and data to multiple remote hosts, thereby removing the need to perform complex and error prone explicit process placement decisions

Elliptic Curve Cryptography on Modern Processor Architectures

Abstract Elliptic Curve Cryptography (ECC) has been adopted by the US National Security Agency (NSA) in Suite "B" as part of its "Cryptographic Modernisation Program ". Additionally, it has been favoured by an entire host of mobile devices due to its superior performance characteristics. ECC is also the building block on which the exciting field of pairing/identity based cryptography is based. This widespread use means that there is potentially a lot to be gained by researching efficient implementations on modern processors such as IBM's Cell Broadband Engine and Philip's next generation smart card cores. ECC operations can be thought of as a pyramid of building blocks, from instructions on a core, modular operations on a finite field, point addition & doubling, elliptic curve scalar multiplication to application level protocols. In this thesis we examine an implementation of these components for ECC focusing on a range of optimising techniques for the Cell's SPU and the MIPS smart card. We show significant performance improvements that can be achieved through of adoption of EC

Inter-workgroup barrier synchronisation on graphics processing units

GPUs are parallel devices that are able to run thousands of independent threads concurrently. Traditional GPU programs are data-parallel, requiring little to no communication, i.e. synchronisation, between threads. However, classical concurrency in the context of CPUs often exploits synchronisation idioms that are not supported on GPUs. By studying such idioms on GPUs, with an aim to facilitate them in a portable way, a wider and more generic space of GPU applications can be made possible. While the breadth of this thesis extends to many aspects of GPU systems, the common thread throughout is the global barrier: an execution barrier that synchronises all threads executing a GPU application. The idea of such a barrier might seem straightforward, however this investigation reveals many challenges and insights. In particular, this thesis includes the following studies: Execution models: while a general global barrier can deadlock due to starvation on GPUs, it is shown that the scheduling guarantees of current GPUs can be used to dynamically create an execution environment that allows for a safe and portable global barrier across a subset of the GPU threads. Application optimisations: a set GPU optimisations are examined that are tailored for graph applications, including one optimisation enabled by the global barrier. It is shown that these optimisations can provided substantial performance improvements, e.g. the barrier optimisation achieves over a 10X speedup on AMD and Intel GPUs. The performance portability of these optimisations is investigated, as their utility varies across input, application, and architecture. Multitasking: because many GPUs do not support preemption, long-running GPU compute tasks (e.g. applications that use the global barrier) may block other GPU functions, including graphics. A simple cooperative multitasking scheme is proposed that allows graphics tasks to meet their deadlines with reasonable overheads.Open Acces

Arquitectura, técnicas y modelos para posibilitar la Ciencia de Datos en el Archivo de la Misión Gaia

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 26/05/2017.The massive amounts of data that the world produces every day pose new challenges to modern societies in terms of how to leverage their inherent value. Social networks, instant messaging, video, smart devices and scientific missions are just mere examples of the vast number of sources generating data every second. As the world becomes more and more digitalized, new needs arise for organizing, archiving, sharing, analyzing, visualizing and protecting the ever-increasing data sets, so that we can truly develop into a data-driven economy that reduces inefficiencies and increases sustainability, creating new business opportunities on the way. Traditional approaches for harnessing data are not suitable any more as they lack the means for scaling to the larger volumes in a timely and cost efficient manner. This has somehow changed with the advent of Internet companies like Google and Facebook, which have devised new ways of tackling this issue. However, the variety and complexity of the value chains in the private sector as well as the increasing demands and constraints in which the public one operates, needs an ongoing research that can yield newer strategies for dealing with data, facilitate the integration of providers and consumers of information, and guarantee a smooth and prompt transition when adopting these cutting-edge technological advances. This thesis aims at providing novel architectures and techniques that will help perform this transition towards Big Data in massive scientific archives. It highlights the common pitfalls that must be faced when embracing it and how to overcome them, especially when the data sets, their transformation pipelines and the tools used for the analysis are already present in the organizations. Furthermore, a new perspective for facilitating a smoother transition is laid out. It involves the usage of higher-level and use case specific frameworks and models, which will naturally bridge the gap between the technological and scientific domains. This alternative will effectively widen the possibilities of scientific archives and therefore will contribute to the reduction of the time to science. The research will be applied to the European Space Agency cornerstone mission Gaia, whose final data archive will represent a tremendous discovery potential. It will create the largest and most precise three dimensional chart of our galaxy (the Milky Way), providing unprecedented position, parallax and proper motion measurements for about one billion stars. The successful exploitation of this data archive will depend to a large degree on the ability to offer the proper architecture, i.e. infrastructure and middleware, upon which scientists will be able to do exploration and modeling with this huge data set. In consequence, the approach taken needs to enable data fusion with other scientific archives, as this will produce the synergies leading to an increment in scientific outcome, both in volume and in quality. The set of novel techniques and frameworks presented in this work addresses these issues by contextualizing them with the data products that will be generated in the Gaia mission. All these considerations have led to the foundations of the architecture that will be leveraged by the Science Enabling Applications Work Package. Last but not least, the effectiveness of the proposed solution will be demonstrated through the implementation of some ambitious statistical problems that will require significant computational capabilities, and which will use Gaia-like simulated data (the first Gaia data release has recently taken place on September 14th, 2016). These ambitious problems will be referred to as the Grand Challenge, a somewhat grandiloquent name that consists in inferring a set of parameters from a probabilistic point of view for the Initial Mass Function (IMF) and Star Formation Rate (SFR) of a given set of stars (with a huge sample size), from noisy estimates of their masses and ages respectively. This will be achieved by using Hierarchical Bayesian Modeling (HBM). In principle, the HBM can incorporate stellar evolution models to infer the IMF and SFR directly, but in this first step presented in this thesis, we will start with a somewhat less ambitious goal: inferring the PDMF and PDAD. Moreover, the performance and scalability analyses carried out will also prove the suitability of the models for the large amounts of data that will be available in the Gaia data archive.Las grandes cantidades de datos que se producen en el mundo diariamente plantean nuevos retos a la sociedad en términos de cómo extraer su valor inherente. Las redes sociales, mensajería instantánea, los dispositivos inteligentes y las misiones científicas son meros ejemplos del gran número de fuentes generando datos en cada momento. Al mismo tiempo que el mundo se digitaliza cada vez más, aparecen nuevas necesidades para organizar, archivar, compartir, analizar, visualizar y proteger la creciente cantidad de datos, para que podamos desarrollar economías basadas en datos e información que sean capaces de reducir las ineficiencias e incrementar la sostenibilidad, creando nuevas oportunidades de negocio por el camino. La forma en la que se han manejado los datos tradicionalmente no es la adecuada hoy en día, ya que carece de los medios para escalar a los volúmenes más grandes de datos de una forma oportuna y eficiente. Esto ha cambiado de alguna manera con la llegada de compañías que operan en Internet como Google o Facebook, ya que han concebido nuevas aproximaciones para abordar el problema. Sin embargo, la variedad y complejidad de las cadenas de valor en el sector privado y las crecientes demandas y limitaciones en las que el sector público opera, necesitan una investigación continua en la materia que pueda proporcionar nuevas estrategias para procesar las enormes cantidades de datos, facilitar la integración de productores y consumidores de información, y garantizar una transición rápida y fluida a la hora de adoptar estos avances tecnológicos innovadores. Esta tesis tiene como objetivo proporcionar nuevas arquitecturas y técnicas que ayudarán a realizar esta transición hacia Big Data en archivos científicos masivos. La investigación destaca los escollos principales a encarar cuando se adoptan estas nuevas tecnologías y cómo afrontarlos, principalmente cuando los datos y las herramientas de transformación utilizadas en el análisis existen en la organización. Además, se exponen nuevas medidas para facilitar una transición más fluida. Éstas incluyen la utilización de software de alto nivel y específico al caso de uso en cuestión, que haga de puente entre el dominio científico y tecnológico. Esta alternativa ampliará de una forma efectiva las posibilidades de los archivos científicos y por tanto contribuirá a la reducción del tiempo necesario para generar resultados científicos a partir de los datos recogidos en las misiones de astronomía espacial y planetaria. La investigación se aplicará a la misión de la Agencia Espacial Europea (ESA) Gaia, cuyo archivo final de datos presentará un gran potencial para el descubrimiento y hallazgo desde el punto de vista científico. La misión creará el catálogo en tres dimensiones más grande y preciso de nuestra galaxia (la Vía Láctea), proporcionando medidas sin precedente acerca del posicionamiento, paralaje y movimiento propio de alrededor de mil millones de estrellas. Las oportunidades para la explotación exitosa de este archivo de datos dependerán en gran medida de la capacidad de ofrecer la arquitectura adecuada, es decir infraestructura y servicios, sobre la cual los científicos puedan realizar la exploración y modelado con esta inmensa cantidad de datos. Por tanto, la estrategia a realizar debe ser capaz de combinar los datos con otros archivos científicos, ya que esto producirá sinergias que contribuirán a un incremento en la ciencia producida, tanto en volumen como en calidad de la misma. El conjunto de técnicas e infraestructuras innovadoras presentadas en este trabajo aborda estos problemas, contextualizándolos con los productos de datos que se generarán en la misión Gaia. Todas estas consideraciones han conducido a los fundamentos de la arquitectura que se utilizará en el paquete de trabajo de aplicaciones que posibilitarán la ciencia en el archivo de la misión Gaia (Science Enabling Applications). Por último, la eficacia de la solución propuesta se demostrará a través de la implementación de dos problemas estadísticos que requerirán cantidades significativas de cómputo, y que usarán datos simulados en el mismo formato en el que se producirán en el archivo de la misión Gaia (la primera versión de datos recogidos por la misión está disponible desde el día 14 de Septiembre de 2016). Estos ambiciosos problemas representan el Gran Reto (Grand Challenge), un nombre grandilocuente que consiste en inferir una serie de parámetros desde un punto de vista probabilístico para la función de masa inicial (Initial Mass Function) y la tasa de formación estelar (Star Formation Rate) dado un conjunto de estrellas (con una muestra grande), desde estimaciones con ruido de sus masas y edades respectivamente. Esto se abordará utilizando modelos jerárquicos bayesianos (Hierarchical Bayesian Modeling). Enprincipio,losmodelospropuestos pueden incorporar otros modelos de evolución estelar para inferir directamente la función de masa inicial y la tasa de formación estelar, pero en este primer paso presentado en esta tesis, empezaremos con un objetivo algo menos ambicioso: la inferencia de la función de masa y distribución de edades actual (Present-Day Mass Function y Present-Day Age Distribution respectivamente). Además, se llevará a cabo el análisis de rendimiento y escalabilidad para probar la idoneidad de la implementación de dichos modelos dadas las enormes cantidades de datos que estarán disponibles en el archivo de la misión Gaia...Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu