Cognitive Computing is a new and quickly advancing technology. In thelast decade Cognitive Computing has been used to assist researchers in theirendeavors in many different scientific fields such as Health & medicine,Education, Marketing, Psychology and Financial Services. On the otherhand, Parallel programming is a more complex concept than sequentialprogramming. The additional complexity of Parallel Programming isintroduced by its nature that requires implementations of more complexalgorithms and it introduces additional concepts to the developers, namelythe communication between the processes (Distributed memory systems)that execute the parallel program and their synchronization (Share memorysystems). As a result of this additional complexity, a lot of novice developersare reserved in their attempts to implement parallel programs. The objectiveof this research project was to investigate whether we can assist parallelprogramming process through cognitive computing solutions. In order toachieve our objective, the MPI Assistant, a Q&A system has been developedand a case study has been carried out to determine our application’s efficiencyin our attempt to assist parallel programming developers. The case studyshowed that our MPI Assistant system indeed helped developers reduce thetime they spend to develop their solutions, but not improve the quality ofthe program or its efficiency as these improvements require features that areout of this research project’s scope. However, the case study had limitednumber of participants, which may affect our results’ reliability. As a nextstep in our attempt to determine if cognitive computing technologies are ableto assist developers in their parallel programming development, we movedto investigate if cognitive solutions can extract better and more completeresponses compared to our manually-created responses that we created forthe MPI Assistant. We have experimented with 2 different approaches to theproblem. An approach where we manually created responses for the MPIAssistant, and an approach where we investigated if cognitive solutions canautomatically extract better and complete responses. We compared the qualityof the latter automatic responses with the quality of the former which weremanually created.

Orientadores: Guido Costa Souza de Araújo, Rodolfo Jardim de Azevedo / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-24T12:44:05Z (GMT). No. of bitstreams: 1 Wottrich_RodolfoGuilherme_M.pdf: 2132128 bytes, checksum: b8ac1197909b6cdaf96b95d6097649f3 (MD5) Previous issue date: 2014 / Resumo: A busca por paralelismo sempre foi um importante objetivo no projeto de sistemas computacionais, conduzida principalmente pelo constante interesse na redução de tempos de execução de aplicações. Programação paralela é uma área de pesquisa ativa, na qual o interesse tem crescido devido à emergência de arquiteturas multicore. Por outro lado, aproveitar as grandes capacidades de computação e armazenamento da nuvem e suas características desejáveis de flexibilidade e escalabilidade oferece várias oportunidades interessantes para abordar problemas de pesquisa relevantes em computação científica. Infelizmente, em muitos casos a implementação de aplicações na nuvem demanda conhecimento específico de interfaces de programação paralela e APIs, o que pode se tornar um fardo na programação de aplicações complexas. Para superar tais limitações, neste trabalho propomos OpenMR, um modelo de execução baseado na sintaxe e nos princípios da API OpenMP que facilita a tarefa de programar sistemas distribuídos (isto é, clusters locais ou a nuvem remota). Especificamente, este trabalho aborda o problema de executar a paralelização de laços, usando OpenMR, em um ambiente distribuído, através do mapeamento de iterações do laço para nós MapReduce. Assim, a interface de programação para a nuvem se torna a própria linguagem, livrando o desenvolvedor da tarefa de se preocupar com detalhes da distribuição de cargas de trabalho e dados. Para avaliar a validade da proposta, modificamos benchmarks da suite SPEC OMP2012 para se encaixarem no modelo proposto, desenvolvemos outros toy benchmarks que são I/O-bound e executamo-os em duas configurações: (a) um cluster de computadores disponível localmente através de uma LAN padrão; e (b) clusters disponíveis remotamente através dos serviços Amazon AWS. Comparamos os resultados com a execução utilizando OpenMP em uma arquitetura SMP e mostramos que a técnica de paralelização proposta é factível e demonstra boa escalabilidade / Abstract: The pursuit of parallelism has always been an important goal in the design of computer systems, driven mainly by the constant interest in reducing program execution time. Parallel programming is an active research area, which has grown in interest due to the emergence of multicore architectures. On the other hand, harnessing the large computing and storage capabilities of the cloud and its desirable flexibility and scaling features offers a number of interesting opportunities to address some relevant research problems in scientific computing. Unfortunately, in many cases the implementation of applications on the cloud demands specific knowledge of parallel programming interfaces and APIs, which may become a burden when programming complex applications. To overcome such limitations, in this work we propose OpenMR, an execution model based on the syntax and principles of the OpenMP API which eases the task of programming distributed systems (i.e. local clusters or remote cloud). Specifically, this work addresses the problem of performing loop parallelization, using OpenMR, in a distributed environment, through the mapping of loop iterations to MapReduce nodes. By doing so, the cloud programming interface becomes the programming language itself, freeing the developer from the task of worrying about the details of distributing workload and data. To assess the validity of the proposal, we modified benchmarks from the SPEC OMP2012 suite to fit the proposed model, developed other I/O-bound toy benchmarks and executed them in two settings: (a) a computer cluster locally available through a standard LAN; and (b) clusters remotely available through the Amazon AWS services. We compare the results to the execution using OpenMP in an SMP architecture and show that the proposed parallelization technique is feasible and demonstrates good scalability / Mestrado / Ciência da Computação / Mestre em Ciência da Computação