Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space 1994

The Third International Symposium on Artificial Intelligence, Robotics, and Automation for Space (i-SAIRAS 94), held October 18-20, 1994, in Pasadena, California, was jointly sponsored by NASA, ESA, and Japan's National Space Development Agency, and was hosted by the Jet Propulsion Laboratory (JPL) of the California Institute of Technology. i-SAIRAS 94 featured presentations covering a variety of technical and programmatic topics, ranging from underlying basic technology to specific applications of artificial intelligence and robotics to space missions. i-SAIRAS 94 featured a special workshop on planning and scheduling and provided scientists, engineers, and managers with the opportunity to exchange theoretical ideas, practical results, and program plans in such areas as space mission control, space vehicle processing, data analysis, autonomous spacecraft, space robots and rovers, satellite servicing, and intelligent instruments

Application level runtime load management : a bayesian approach

A computação paralela em sistemas distribuídos partilhados exige novas abordagens ao problema da gestão da carga computacional, uma vez que os algoritmos existentes ficam aquém das expectativas. A execução eficiente de aplicações paralelas irregulares em clusters de computadores partilhados dinamicamente exibe um comportamento imprevisível, devido à à variabilidade dos requisitos da aplicação e da disponibilidade dos recursos do sistema. Esta tese investiga as vantagens de incluir explicitamente no modelo de execução de um escalonador ao nível da aplicação a incerteza que este tem sobre o estado do ambiente em cada instante. Propõe-se um mecanismo de decisão baseado em redes de decisão de Bayes, complementado por uma estrutura genérica para estas redes, vocacionada para o escalonamento ao nível da aplicação; a utilização de um algoritmo de inferência probabilística permite ao escalonador tomar decisões mais eficazes, baseadas em previsões escolásticas das consequências destas decisões, geradas a partir de informação incompleta e desactualizada sobre o estado do ambiente. É proposto um modelo de desempenho da aplicação e respectivas métricas, que permite prever o comportamento da aplicação e do sistema distribuído; estas métricas são utilizadas quer no mecanismo de decisão do escalonador, quer para avaliar o desempenho do mesmo. Para verificar se esta abordagem contribui para melhorar o tempo de execução das aplicações e a eficiência do escalonador, foi desenvolvido um ray tracer paralelo, representativo de uma classe de aplicações baseada em passagem de mensagens com paralelismo no domínio dos dados e comportamento irregular. Este protótipo foi executado num cluster com sete nodos partilhados no tempo e submetidos a vários padrões sintéticos de cargas de trabalho dinâmicas. Para avaliar a eficácia da gestão de carga proposta, o desempenho do escalonador estocástico foi comparado com três escalonadores de referência: uma distribuição estática e uniforme da carga, uma estratégia orientada ao pedido e uma política de escalonamento determinística baseada em sensores. Os resultados obtidos demonstram que estratégias dinâmicas baseadas em sensores obtêm grandes melhorias de desempenho sobre estratégias que não usam informação sobre o estado do ambiente, e realçam as vantagens do escalonador estocástico relativamente a um escalonador determinístico com um nível de complexidade equivalente.Affordable parallel computing on distributed shared systems requires novel approaches to manage the runtime load distribution, since current algorithms fall below expectations. The efficient execution of irregular parallel applications, on dynamically shared computing clusters, has an unpredictable dynamic behaviour, due both to the application requirements and to the available system's resources. This thesis addresses the explicit inclusion of the uncertainty an application level scheduling agent has about the environment, on its internal model of the world and on its decision making mechanism. Bayesian decision networks are introduced and a generic framework is proposed for application level scheduling, where a probabilistic inference algorithm helps the scheduler to efficiently make decisions with improved predictions, based on available incomplete and aged measured data. An application level performance model and associated metrics (performance, environment and overheads) are proposed to obtain application and system behaviour estimates, to include in the scheduling agent's model and to help the evaluation. To verify that this novel approach improves the overall application execution time and the scheduling efficiency, a parallel ray tracer was developed as a message passing irregular data parallel application, and an execution model prototype was built to run on a seven time-shared nodes computing cluster, with dynamically variable synthetic workloads. To assess the effectiveness of the load management, the stochastic scheduler was evaluated rendering several complex scenes, and compared with three reference scheduling strategies: a uniform work distribution, a demand driven work allocation and a sensor based deterministic scheduling strategy. The evaluation results show considerable performance improvements over blind strategies, and stress the decision network based scheduler improvements over the sensor based deterministic approach of identical complexity.Fundação para a Ciência e Tecnologia - PRAXIS XXI 2/2.1/TTT/1557/95

On-demand distributed image processing over an adaptive Campus-Grid

This thesis explores how scientific applications, which are based upon short jobs (seconds and minutes) can capitalize upon the idle workstations of a Campus-Grid. These resources are donated on a voluntary basis, and consequently, the Campus-Grid is constantly adapting and the availability of workstations changes. Typically, to utilize these resources a Condor system or equivalent would be used. However, such systems are designed with different trade-offs and incentives in mind and therefore do not provide intrinsic support for short jobs. The motivation for creating a provisioning scenario for short jobs is that Image Processing, as well as other areas of scientific analysis, are typically composed of short running jobs, but still require parallel solutions. Much of the literature in this area comments on the challenges of performing such analysis efficiently and effectively even when dedicated resources are in use. The main challenges are: latency and scheduling penalties, granularity and the potential for very short jobs. A volunteer Grid retains these challenges but also adds further challenges. These can be summarized as: unpredictable re source availability and longevity, multiple machine owners and administrators who directly affect the operating environment. Ultimately, this creates the requirement for well conceived and effective fault management strategies. However, these are typically not in place to enable transparent fault-free job administration for the user. This research demonstrates that these challenges are answerable, and that in doing so opportunistically sourced Campus-Grid resources can host disparate applications constituted of short running jobs, of as little as one second in length. This is demonstrated by the significant improvements in performance when the system presented here was compared to a well established Condor system. Here, improvements are increased job efficiency from 60–70% to 95%–100%, up to a 99% reduction in application makespan and up to a 13000% increase in the efficiency of resource utilization. The Condor pool in use is approximately 1,600 workstations distributed across 27 administrative domains of Cardiff University. The application domain of this research is Matlab-based image processing, and the application area used to demonstrate the approach is the analysis of Magnetic Resonance Imagery (MRI). However, the presented approach is generalizable to any application domain with similar characteristics

Software for Exascale Computing - SPPEXA 2016-2019

This open access book summarizes the research done and results obtained in the second funding phase of the Priority Program 1648 "Software for Exascale Computing" (SPPEXA) of the German Research Foundation (DFG) presented at the SPPEXA Symposium in Dresden during October 21-23, 2019. In that respect, it both represents a continuation of Vol. 113 in Springer’s series Lecture Notes in Computational Science and Engineering, the corresponding report of SPPEXA’s first funding phase, and provides an overview of SPPEXA’s contributions towards exascale computing in today's sumpercomputer technology. The individual chapters address one or more of the research directions (1) computational algorithms, (2) system software, (3) application software, (4) data management and exploration, (5) programming, and (6) software tools. The book has an interdisciplinary appeal: scholars from computational sub-fields in computer science, mathematics, physics, or engineering will find it of particular interest