SKIRT: hybrid parallelization of radiative transfer simulations

We describe the design, implementation and performance of the new hybrid parallelization scheme in our Monte Carlo radiative transfer code SKIRT, which has been used extensively for modeling the continuum radiation of dusty astrophysical systems including late-type galaxies and dusty tori. The hybrid scheme combines distributed memory parallelization, using the standard Message Passing Interface (MPI) to communicate between processes, and shared memory parallelization, providing multiple execution threads within each process to avoid duplication of data structures. The synchronization between multiple threads is accomplished through atomic operations without high-level locking (also called lock-free programming). This improves the scaling behavior of the code and substantially simplifies the implementation of the hybrid scheme. The result is an extremely flexible solution that adjusts to the number of available nodes, processors and memory, and consequently performs well on a wide variety of computing architectures.Comment: 21 pages, 20 figure

Contributions à l’Optimisation de Requêtes Multidimensionnelles

Analyser les données consiste à choisir un sous-ensemble des dimensions qui les décriventafin d'en extraire des informations utiles. Or, il est rare que l'on connaisse a priori les dimensions"intéressantes". L'analyse se transforme alors en une activité exploratoire où chaque passe traduit par une requête. Ainsi, il devient primordiale de proposer des solutions d'optimisationde requêtes qui ont une vision globale du processus plutôt que de chercher à optimiser chaque requêteindépendamment les unes des autres. Nous présentons nos contributions dans le cadre de cette approcheexploratoire en nous focalisant sur trois types de requêtes: (i) le calcul de bordures,(ii) les requêtes dites OLAP (On Line Analytical Processing) dans les cubes de données et (iii) les requêtesde préférence type skyline

Scalable Data Analysis on MapReduce-based Systems

Ph.DDOCTOR OF PHILOSOPH

Managing Population and Workload Imbalance in Structured Overlays

Every day the number of data produced by networked devices increases. The current paradigm is to offload the data produced to data centers to be processed. However as more and more devices are offloading their data do cloud centers, accessing data becomes increasingly more challenging. To combat this problem, systems are bringing data closer to the consumer and distributing network responsibilities among the end devices. We are witnessing a change in networking paradigm, where data storage and computation that was once only handled in the cloud, is being processed by Internet of Things (IoT) and mobile devices, thanks to the ever increasing technological capabilities of these devices. One approach, leverages devices into a structured overlay network. Structured Overlays are a common approach to address the organization and distri- bution of data in peer-to-peer distributed systems. Due to their nature, indexing and searching for elements of the system becomes trivial, thus structured overlays become ideal building blocks of resource location based applications. Such overlays assume that the data is distributed evenly over the peers, and that the popularity of those data items is also evenly balanced. However in many systems, due to many factors outside of the system domain, popularity may behave rather randomly, al- lowing for some nodes to spare more resources looking for the popular items than others. In this work we intend to exploit the properties of cluster-based structured overlays propose to address this problem by improving a structure overlay with the mechanisms to manage the population and workload imbalance and achieve more uniform use of resources. Our approach focus on implementing a Group-Based Distributed Hash Table (DHT) capable of dynamically changing its groups to accommodate the changes in churn in the network. With the conclusion of our work we believe that we have indeed created a network capable of withstanding high levels of churn, while ensuring fairness to all members of the network.Todos os dias aumenta o número de dados produzidos por dispositivos em rede. O pa- radigma atual é descarregar os dados produzidos para centros de dados para serem pro- cessados. No entanto com o aumento do número de dispositivos a descarregar dados para estes centros, o acesso aos dados torna-se cada vez mais desafiante. Para combater este problema, os sistemas estão a aproximar os dados dos consumidores e a distribuir responsabilidades de rede entre os dispositivos. Estamos a assistir a uma mudança no paradigma de redes, onde o armazenamento de dados e a computação que antes eram da responsabilidade dos centros de dados, está a ser processado por dispositivos móveis IoT, graças às crescentes capacidades tecnológicas destes dispositivos. Uma abordagem, junta os dispositivos em redes estruturadas. As redes estruturadas são o meio mais comum de organizar e distribuir dados em redes peer-to-peer. Gradas às suas propriedades, indexar e procurar por elementos torna- se trivial, assim, as redes estruturadas tornam-se o bloco de construção ideal para sistemas de procura de ficheiros. Estas redes assumem que os dados estão distribuídos equitativamente por todos os participantes e que todos esses dados são igualmente procurados. no entanto em muitos sistemas, por factores externos a popularidade tem um comportamento volátil e imprevi- sível sobrecarregando os participantes que guardam os dados mais populares. Este trabalho tenta explorar as propriedades das redes estruturadas em grupo para confrontar o problema, vamos equipar uma destas redes com os mecanismos necessários para coordenar os participantes e a sua carga. A nossa abordagem focasse na implementação de uma DHT baseado em grupos capaz de alterar dinamicamente os grupos para acomodar as mudanças de membros da rede. Com a conclusão de nosso trabalho, acreditamos que criamos uma rede capaz de suportar altos níveis de instabilidade, enquanto garante justiça a todos os membros da rede

Earth Observation Open Science and Innovation

geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc

Enterprise Data Mining & Machine Learning Framework on Cloud Computing for Investment Platforms

Machine Learning and Data Mining are two key components in decision making systems which can provide valuable in-sights quickly into huge data set. Turning raw data into meaningful information and converting it into actionable tasks makes organizations profitable and sustain immense competition. In the past decade we saw an increase in Data Mining algorithms and tools for financial market analysis, consumer products, manufacturing, insurance industry, social networks, scientific discoveries and warehousing. With vast amount of data available for analysis, the traditional tools and techniques are outdated for data analysis and decision support. Organizations are investing considerable amount of resources in the area of Data Mining Frameworks in order to emerge as market leaders. Machine Learning is a natural evolution of Data Mining. The existing Machine Learning techniques rely heavily on the underlying Data Mining techniques in which the Patterns Recognition is an essential component. Building an efficient Data Mining Framework is expensive and usually culminates in multi-year project for the organizations. The organization pay a heavy price for any delay or inefficient Data Mining foundation. In this research, we propose to build a cost effective and efficient Data Mining (DM) and Machine Learning (ML) Framework on cloud computing environment to solve the inherent limitations in the existing design methodologies. The elasticity of the cloud architecture solves the hardware constraint on businesses. Our research is focused on refining and enhancing the current Data Mining frameworks to build an enterprise data mining and machine learning framework. Our initial studies and techniques produced very promising results by reducing the existing build time considerably. Our technique of dividing the DM and ML Frameworks into several individual components (5 sub components) which can be reused at several phases of the final enterprise build is efficient and saves operational costs to the organization. Effective Aggregation using selective cuboids and parallel computations using Azure Cloud Services are few of many proposed techniques in our research. Our research produced a nimble, scalable portable architecture for enterprise wide implementation of DM and ML frameworks

Multi-wavelength infrared imaging computer systems and applications

This dissertation presents the development of three computer systems for multi-wavelength thermal imaging. Two computer systems were developed for the multi-wavelength imaging pyrometers (M-WIPs) that yield non-contact temperature measurements by remotely sensing the surface of objects with unknown wavelength-dependent emissivity. These M-WIP computer systems represent the state-of-art development in remote temperature measurement system based on the multi-wavelength approach. The dissertation research includes M-WIP computer system integration, software development, performance evaluation, and also applications in monitoring and control of temperature distribution of silicon wafers in a rapid thermal process system. The two M-WIPs are capable of data acquisition, signal processing, system calibration, radiometric measurement, parallel processing and process control. Temperature measurement experiments demonstrated the accuracy of ±1°C against blackbody and ±4°C for colorbody objects. Various algorithms were developed and implemented, including real-time two-point non-uniformity correction, thermal image pseudocoloring, PC to SUN workstation data transfer, automatic IR camera integration time control, and radiometric measurement parallel processing. A third computer system was developed for the demonstration of a 3-color InGaAs FPA which can provide images with information in three different IR wavelength range simultaneously. Numbers of functions were developed to demonstrate and characterize 3-color FPAs, and the system was delivered to be used by the 3-color FPA manufacturer