Introduction to Special issue on Reconfigurable computing and FPGAs

During the last three decades, FPGA technology has quickly evolved to become a major subject of research in computer and electrical engineering as it has been identified as a powerful alternative for creating highly efficient computing systems. FPGA devices offer substantial performance improvements when compared against traditional processing architectures via custom design and reconfiguration capabilities.

GovCloud: the usage of cloud computing in public organizations

It has been shown that cloud computing brings cost benefits and promotes efficiency in the operations of the organizations, no matter what their type or size. However, few public organizations are benefiting from this new paradigm shift in the way the organizations consume and manage computational resources. The objective of this thesis is to analyze both internal and external factors that may influence the adoption of cloud computing by public organizations and propose possible strategies that can assist these organizations in their path to cloud usage. In order to achieve this objective, a SWOT analysis has been conducted, detecting internal factors (strengths and weaknesses) and external factors (opportunities and threats) that can influence the adoption of a governmental cloud. With the application of a TOWS matrix, by combining the internal and external factors, a list of possible strategies have been formulated to be used as a guide to decision-making related to the transition to a cloud environment.

Planificación multinivel eficiente con aprovisionamiento dinámico en grids y clouds

La consolidación de las grandes infraestructuras para la Computación Distribuida ha resultado en una plataforma de Computación de Alta Productividad que está lista para grandes cargas de trabajo. Los mejores exponentes de este proceso son las federaciones grid actuales. Por otro lado, la Computación Cloud promete ser más flexible, utilizable, disponible y simple que la Computación Grid, cubriendo además muchas más necesidades computacionales que las requeridas para llevar a cabo cálculos distribuidos. En cualquier caso, debido al dinamismo y la heterogeneidad presente en grids y clouds, encontrar la asignación ideal de las tareas computacionales en los recursos disponibles es, por definición un problema NP-completo, y sólo se pueden encontrar soluciones subóptimas para estos entornos. Sin embargo, la caracterización de estos recursos en ambos tipos de infraestructuras es deficitaria. Los sistemas de información disponibles no proporcionan datos fiables sobre el estado de los recursos, lo cual no permite la planificación avanzada que necesitan los diferentes tipos de aplicaciones distribuidas. Durante la última década esta cuestión no ha sido resuelta para la Computación Grid y las infraestructuras cloud establecidas recientemente presentan el mismo problema. En este marco, los planificadores (brokers) sólo pueden mejorar la productividad de las ejecuciones largas, pero no proporcionan ninguna estimación de su duración. La planificación compleja ha sido abordada tradicionalmente por otras herramientas como los gestores de flujos de trabajo, los auto-planificadores o los sistemas de gestión de producción pertenecientes a ciertas comunidades de investigación. Sin embargo, el bajo rendimiento obtenido con estos mecanismos de asignación anticipada (early-binding) es notorio. Además, la diversidad en los proveedores cloud, la falta de soporte de herramientas de planificación y de interfaces de programación estandarizadas para distribuir la carga de trabajo, dificultan la portabilidad masiva de aplicaciones legadas a los entornos cloud...

Reconfigurable computing for Monte Carlo simulations: results and prospects of the Janus project

We describe Janus, a massively parallel FPGA-based computer optimized for the simulation of spin glasses, theoretical models for the behavior of glassy materials. FPGAs (as compared to GPUs or many-core processors) provide a complementary approach to massively parallel computing. In particular, our model problem is formulated in terms of binary variables, and floating-point operations can be (almost) completely avoided. The FPGA architecture allows us to run many independent threads with almost no latencies in memory access, thus updating up to 1024 spins per cycle. We describe Janus in detail and we summarize the physics results obtained in four years of operation of this machine; we discuss two types of physics applications: long simulations on very large systems (which try to mimic and provide understanding about the experimental non equilibrium dynamics), and low-temperature equilibrium simulations using an artificial parallel tempering dynamics. The time scale of our non-equilibrium simulations spans eleven orders of magnitude (from picoseconds to a tenth of a second). On the other hand, our equilibrium simulations are unprecedented both because of the low temperatures reached and for the large systems that we have brought to equilibrium. A finite-time scaling ansatz emerges from the detailed comparison of the two sets of simulations. Janus has made it possible to perform spin glass simulations that would take several decades on more conventional architectures. The paper ends with an assessment of the potential of possible future versions of the Janus architecture, based on state-of-the-art technology.

Robust fitting of Zernike polynomials to noisy point clouds defined over connected domains of arbitrary shape

A new method for fitting a series of Zernike polynomials to point clouds defined over connected domains of arbitrary shape defined within the unit circle is presented in this work. The method is based on the application of machine learning fitting techniques by constructing an extended training set in order to ensure the smooth variation of local curvature over the whole domain. Therefore this technique is best suited for fitting points corresponding to ophthalmic lenses surfaces, particularly progressive power ones, in non-regular domains. We have tested our method by fitting numerical and real surfaces reaching an accuracy of 1 micron in elevation and 0.1 D in local curvature in agreement with the customary tolerances in the ophthalmic manufacturing industry.

Tethered Monte Carlo: computing the effective potential without critical slowing down

We present Tethered Monte Carlo, a simple, general purpose method of computing the effective potential of the order parameter (Helmholtz free energy). This formalism is based on a new statistical ensemble, closely related to the micromagnetic one, but with an extended configuration space (through Creutz-like demons). Canonical averages for arbitrary values of the external magnetic field are computed without additional simulations. The method is put to work in the two-dimensional Ising model, where the existence of exact results enables us to perform high precision checks. A rather peculiar feature of our implementation, which employs a local Metropolis algorithm, is the total absence, within errors, of critical slowing down for magnetic observables. Indeed, high accuracy results are presented for lattices as large as L = 1024.

Tethered Monte Carlo: computing the effective potential without critical slowing down

We present Tethered Monte Carlo, a simple, general purpose method of computing the effective potential of the order parameter (Helmholtz free energy). This formalism is based on a new statistical ensemble, closely related to the micromagnetic one, but with an extended configuration space (through Creutz-like demons). Canonical averages for arbitrary values of the external magnetic field are computed without additional simulations. The method is put to work in the two-dimensional Ising model, where the existence of exact results enables us to perform high precision checks. A rather peculiar feature of our implementation, which employs a local Metropolis algorithm, is the total absence, within errors, of critical slowing down for magnetic observables. Indeed, high accuracy results are presented for lattices as large as L = 1024.

Janus: an FPGA-based system for high-performance scientific computing

This paper describes JANUS, a modular massively parallel and reconfigurable FPGA-based computing system. Each JANUS module has a computational core and a host. The computational core is a 4x4 array of FPGA-based processing elements with nearest-neighbor data links. Processors are also directly connected to an I/O node attached to the JANUS host, a conventional PC. JANUS is tailored for, but not limited to, the requirements of a class of hard scientific applications characterized by regular code structure, unconventional data manipulation instructions and not too large data-base size. We discuss the architecture of this configurable machine, and focus on its use on Monte Carlo simulations of statistical mechanics. On this class of application JANUS achieves impressive performances: in some cases one JANUS processing element outperfoms high-end PCs by a factor ≈1000. We also discuss the role of JANUS on other classes of scientific applications.

Collective effects in light scattering from cold strontium clouds

A cold atomic cloud is a versatile object, because it offers many handles to control and tune its properties. This facilitates studies of its behavior in various circumstances, such as sample temperature, size and density, composition, dimensionality and coherence time. The range of possible experiments is constrained by the specifications of the atomic species used. In this thesis presents the work done in the experiment for laser cooling of strontium atoms, focusing on its stability, which should provide cold and ultracold samples for the study of collective effects in light scattering. From the initial apparatus, innumerous changes were performed. The vacuum system got improved and now reached lower ultra high vacuum due to the pre-baking done to its parts and adding a titanium-sublimation stage. The quadrupole trap were improved by the design and construction of a new pair of coils. The stability of the blue, green and red laser systems and the loss prevention of laser light were improved, giving rise to a robust apparatus. Another important point is the development of homemade devices to reduce the costs and to be used as a monitor of different parts of an cold atoms experiment. From this homemade devices, we could demonstrate a dramatic linewidth narrowing by injection lock of an low cost 461 nm diode laser and its application to our strontium experiment. In the end, this improved experimental apparatus made possible the study of a new scattering effect, the mirror assisted coherent back-scattering (mCBS).

Nesting In The Clouds: Evaluating And Predicting Sea Turtle Nesting Beach Parameters From Lidar Data

Humans' desire for knowledge regarding animal species and their interactions with the natural world have spurred centuries of studies. The relatively new development of remote sensing systems using satellite or aircraft-borne sensors has opened up a wide field of research, which unfortunately largely remains dependent on coarse-scale image spatial resolution, particularly for habitat modeling. For habitat-specialized species, such data may not be sufficient to successfully capture the nuances of their preferred areas. Of particular concern are those species for which topographic feature attributes are a main limiting factor for habitat use. Coarse spatial resolution data can smooth over details that may be essential for habitat characterization. Three studies focusing on sea turtle nesting beaches were completed to serve as an example of how topography can be a main deciding factor for certain species. Light Detection and Ranging (LiDAR) data were used to illustrate that fine spatial scale data can provide information not readily captured by either field work or coarser spatial scale sources. The variables extracted from the LiDAR data could successfully model nesting density for loggerhead (Caretta caretta), green (Chelonia mydas), and leatherback (Dermochelys coriacea) sea turtle species using morphological beach characteristics, highlight beach changes over time and their correlations with nesting success, and provide comparisons for nesting density models across large geographic areas. Comparisons between the LiDAR dataset and other digital elevation models (DEMs) confirmed that fine spatial scale data sources provide more similar habitat information than those with coarser spatial scales. Although these studies focused solely on sea turtles, the underlying principles are applicable for many other wildlife species whose range and behavior may be influenced by topographic features.

Informações de suporte ao escalonamento de workflows científicos para a execução em plataformas de computação em nuvem

A ciência tem feito uso frequente de recursos computacionais para execução de experimentos e processos científicos, que podem ser modelados como workflows que manipulam grandes volumes de dados e executam ações como seleção, análise e visualização desses dados segundo um procedimento determinado. Workflows científicos têm sido usados por cientistas de várias áreas, como astronomia e bioinformática, e tendem a ser computacionalmente intensivos e fortemente voltados à manipulação de grandes volumes de dados, o que requer o uso de plataformas de execução de alto desempenho como grades ou nuvens de computadores. Para execução dos workflows nesse tipo de plataforma é necessário o mapeamento dos recursos computacionais disponíveis para as atividades do workflow, processo conhecido como escalonamento. Plataformas de computação em nuvem têm se mostrado um alternativa viável para a execução de workflows científicos, mas o escalonamento nesse tipo de plataforma geralmente deve considerar restrições específicas como orçamento limitado ou o tipo de recurso computacional a ser utilizado na execução. Nesse contexto, informações como a duração estimada da execução ou limites de tempo e de custo (chamadas aqui de informações de suporte ao escalonamento) são importantes para garantir que o escalonamento seja eficiente e a execução ocorra de forma a atingir os resultados esperados. Este trabalho identifica as informações de suporte que podem ser adicionadas aos modelos de workflows científicos para amparar o escalonamento e a execução eficiente em plataformas de computação em nuvem. É proposta uma classificação dessas informações, e seu uso nos principais Sistemas Gerenciadores de Workflows Científicos (SGWC) é analisado. Para avaliar o impacto do uso das informações no escalonamento foram realizados experimentos utilizando modelos de workflows científicos com diferentes informações de suporte, escalonados com algoritmos que foram adaptados para considerar as informações inseridas. Nos experimentos realizados, observou-se uma redução no custo financeiro de execução do workflow em nuvem de até 59% e redução no makespan chegando a 8,6% se comparados à execução dos mesmos workflows sendo escalonados sem nenhuma informação de suporte disponível.

Pitfalls on computing liquid-liquid phase equilibria using the k-value method

Poster presented in the 11th Mediterranean Congress of Chemical Engineering, Barcelona, October 21-24, 2008.