O modelo de programação baseado em atores é frequentemente utilizado para o desenvolvimento de grandes aplicações e sistemas. Podemos citar como exemplo o serviço de bate-papo do Facebook ou ainda o WhatsApp. Estes sistemas dão suporte a milhares de usuários conectados simultaneamente levando em conta estritas restrições de desempenho e interatividade. Tais sistemas normalmente são amparados por infraestruturas de hardware com processadores de múltiplos núcleos. Normalmente, máquinas deste porte são baseadas em uma estrutura de memória compartilhada hierarquicamente (NUMA - Non-Uniform Memory Access). Nossa análise dos atuais ambientes de execução para atores e a pesquisa na literatura mostram que poucos estudos sobre a adequação deste ambientes a essas plataformas hierárquicas foram conduzidos. Estes ambientes de execução normalmente assumem que o espaço de memória é uniforme o que pode causar sérios problemas de desempenho. Nesta tese nós estudamos os desafios enfrentados por um ambiente de execução para atores quando da sua execução nestas plataformas. Estudamos particularmente os problemas de gerenciamento de memória, de escalonamento e de balanceamento de carga. Neste documento nós também analisamos e caracterizamos as aplicações baseadas no modelo de atores. Tal análise nos permitiu evidenciar o fato de que a execução de benchmarks e aplicações criam estruturas de comunicação peculiares entre os atores. Tais peculiaridades podem, então, ser utilizadas pelos ambientes de execução para otimizar o seu desempenho. A avaliação dos grafos de comunicação e a implementação da prova de conceito foram feitas utilizando um ambiente de execução real, a máquina virtual da linguagem Erlang. A linguagem Erlang utiliza o modelo de atores para concorrência com uma sintaxe clara e consistente. As modificações que nós efetuamos nesta máquina virtual permitiram uma melhora significativa no desempenho de certas aplicações através de uma melhor afinidade de comunicação entre os atores. O escalonamento e o balanceamento de carga também foram melhorados graças à utilização do conhecimento sobre o comportamento da aplicação e sobre a plataforma de hardware. / The actor model is present in several mission-critical systems, such as those supporting WhatsApp and Facebook Chat. These systems serve thousands of clients simultaneously, therefore demanding substantial computing resources usually provided by multi-processor and multi-core platforms. Non-Uniform Memory Access (NUMA) architectures account for an important share of these platforms. Yet, research on the suitability of the current actor runtime environments for these machines is very limited. Current runtime environments, in general, assume a flat memory space, thus not performing as well as they could. In this thesis we study the challenges hierarchical shared memory multi-core platforms present to actor runtime environments. In particular, we investigate aspects related to memory management, scheduling, and load-balancing. In this document, we analyze and characterize actor based applications to, in light of the above, propose improvements to actor runtime environments. This analysis highlighted the existence of peculiar communication structures. We argue that the comprehension of these structures and the knowledge about the underlying hardware architecture can be used in tandem to improve application performance. As a proof of concept, we implemented our proposal using a real actor runtime environment, the Erlang Virtual Machine (VM). Concurrency in Erlang is based on the actor model and the language has a consistent syntax for actor handling. Our modifications to the Erlang VM significantly improved the performance of some applications thanks to better informed decisions on scheduling and on load-balancing.

La tendance en HPC est à l'accroissement du nombre de coeurs par noeud de calcul pour une quantité totale de mémoire par noeud constante. A large échelle, l'un des principaux défis pour les applications parallèles est de garder une faible consommation mémoire. Cette thèse présente une couche de communication multi-threadée sur Infiniband, laquelle fournie de bonnes performances et une faible consommation mémoire. Nous ciblons les applications scientifiques parallélisées grâce à la bibliothèque MPI ou bien combinées avec un modèle de programmation en mémoire partagée. En partant du constat que le nombre de connexions réseau et de buffers de communication est critique pour la mise à l'échelle des bibliothèques MPI, la première contribution propose trois approches afin de contrôler leur utilisation. Nous présentons une topologie virtuelle extensible et entièrement connectée pour réseaux rapides orientés connexion. Dans un contexte agrégeant plusieurs cartes permettant d'ajuster dynamiquement la configuration des buffers réseau utilisant la technologie RDMA. La seconde contribution propose une optimisation qui renforce le potentiel d'asynchronisme des applications MPI, laquelle montre une accélération de deux des communications. La troisième contribution évalue les performances de plusieurs bibliothèques MPI exécutant une application de modélisation sismique en contexte hybride. Les expériences sur des noeuds de calcul jusqu'à 128 coeurs montrent une économie de 17 % sur la mémoire. De plus, notre couche de communication multi-threadée réduit le temps d'exécution dans le cas où plusieurs threads OpenMP participent simultanément aux communications MPI. / A recent trend in high performance computing shows a rising number of cores per compute node, while the total amount of memory per compute node remains constant. To scale parallel applications on such large machines, one of the major challenges is to keep a low memory consumption. This thesis develops a multi-threaded communication layer over Infiniband which provides both good performance of communications and a low memory consumption. We target scientific applications parallelized using the MPI standard in pure mode or combined with a shared memory programming model. Starting with the observation that network endpoints and communication buffers are critical for the scalability of MPI runtimes, the first contribution proposes three approaches to control their usage. We introduce a scalable and fully-connected virtual topology for connection-oriented high-speed networks. In the context of multirail configurations, we then detail a runtime technique which reduces the number of network connections. We finally present a protocol for dynamically resizing network buffers over the RDMA technology. The second contribution proposes a runtime optimization to enforce the overlap potential of MPI communications, showing a 2x improvement factor on communications. The third contribution evaluates the performance of several MPI runtimes running a seismic modeling application in a hybrid context. On large compute nodes up to 128 cores, the introduction of OpenMP in the MPI application saves up to 17 % of memory. Moreover, we show a performance improvement with our multi-threaded communication layer where the OpenMP threads concurrently participate to the MPI communications

O modelo de programação baseado em atores é frequentemente utilizado para o desenvolvimento de grandes aplicações e sistemas. Podemos citar como exemplo o serviço de bate-papo do Facebook ou ainda o WhatsApp. Estes sistemas dão suporte a milhares de usuários conectados simultaneamente levando em conta estritas restrições de desempenho e interatividade. Tais sistemas normalmente são amparados por infraestruturas de hardware com processadores de múltiplos núcleos. Normalmente, máquinas deste porte são baseadas em uma estrutura de memória compartilhada hierarquicamente (NUMA - Non-Uniform Memory Access). Nossa análise dos atuais ambientes de execução para atores e a pesquisa na literatura mostram que poucos estudos sobre a adequação deste ambientes a essas plataformas hierárquicas foram conduzidos. Estes ambientes de execução normalmente assumem que o espaço de memória é uniforme o que pode causar sérios problemas de desempenho. Nesta tese nós estudamos os desafios enfrentados por um ambiente de execução para atores quando da sua execução nestas plataformas. Estudamos particularmente os problemas de gerenciamento de memória, de escalonamento e de balanceamento de carga. Neste documento nós também analisamos e caracterizamos as aplicações baseadas no modelo de atores. Tal análise nos permitiu evidenciar o fato de que a execução de benchmarks e aplicações criam estruturas de comunicação peculiares entre os atores. Tais peculiaridades podem, então, ser utilizadas pelos ambientes de execução para otimizar o seu desempenho. A avaliação dos grafos de comunicação e a implementação da prova de conceito foram feitas utilizando um ambiente de execução real, a máquina virtual da linguagem Erlang. A linguagem Erlang utiliza o modelo de atores para concorrência com uma sintaxe clara e consistente. As modificações que nós efetuamos nesta máquina virtual permitiram uma melhora significativa no desempenho de certas aplicações através de uma melhor afinidade de comunicação entre os atores. O escalonamento e o balanceamento de carga também foram melhorados graças à utilização do conhecimento sobre o comportamento da aplicação e sobre a plataforma de hardware. / The actor model is present in several mission-critical systems, such as those supporting WhatsApp and Facebook Chat. These systems serve thousands of clients simultaneously, therefore demanding substantial computing resources usually provided by multi-processor and multi-core platforms. Non-Uniform Memory Access (NUMA) architectures account for an important share of these platforms. Yet, research on the suitability of the current actor runtime environments for these machines is very limited. Current runtime environments, in general, assume a flat memory space, thus not performing as well as they could. In this thesis we study the challenges hierarchical shared memory multi-core platforms present to actor runtime environments. In particular, we investigate aspects related to memory management, scheduling, and load-balancing. In this document, we analyze and characterize actor based applications to, in light of the above, propose improvements to actor runtime environments. This analysis highlighted the existence of peculiar communication structures. We argue that the comprehension of these structures and the knowledge about the underlying hardware architecture can be used in tandem to improve application performance. As a proof of concept, we implemented our proposal using a real actor runtime environment, the Erlang Virtual Machine (VM). Concurrency in Erlang is based on the actor model and the language has a consistent syntax for actor handling. Our modifications to the Erlang VM significantly improved the performance of some applications thanks to better informed decisions on scheduling and on load-balancing.

Au milieu des années deux mille, le développement de microprocesseurs a atteint un point à partir duquel l'augmentation de la fréquence de fonctionnement et la complexification des micro-architectures devenaient moins efficaces en termes de consommation d'énergie, poussant ainsi la densité d'énergie au delà du raisonnable. Par conséquent, l'industrie a opté pour des architectures multi-cœurs intégrant plusieurs unités de calcul sur une même puce. Les sytèmes hautes performances d'aujourd'hui sont composés de centaines de cœurs et les systèmes futurs intègreront des milliers d'unités de calcul. Afin de fournir une bande passante mémoire suffisante dans ces systèmes, la mémoire vive est distribuée physiquement sur plusieurs contrôleurs mémoire avec un accès non-uniforme à la mémoire (NUMA). Des travaux de recherche récents ont identifié les modèles de programmation à base de tâches dépendantes à granularité fine comme une approche clé pour exploiter la puissance de calcul des architectures généralistes massivement parallèles. Toutefois, peu de recherches ont été conduites sur l'optimisation dynamique des programmes parallèles à base de tâches afin de réduire l'impact négatif sur les performances résultant de la non-uniformité des accès à la mémoire. L'objectif de cette thèse est de déterminer les enjeux et les opportunités concernant l'exploitation efficace de machines many-core NUMA par des applications à base de tâches et de proposer des mécanismes efficaces, portables et entièrement automatiques pour le placement de tâches et de données, améliorant la localité des accès à la mémoire ainsi que les performances. Les décisions de placement sont basées sur l'exploitation des informations sur les dépendances entre tâches disponibles dans les run-times de langages de programmation à base de tâches modernes. Les évaluations expérimentales réalisées reposent sur notre implémentation dans le run-time du langage OpenStream et un ensemble de benchmarks scientifiques hautes performances. Enfin, nous avons développé et implémenté Aftermath, un outil d'analyse et de débogage de performances pour des applications à base de tâches et leurs run-times. / Within the last decade, microprocessor development reached a point at which higher clock rates and more complex micro-architectures became less energy-efficient, such that power consumption and energy density were pushed beyond reasonable limits. As a consequence, the industry has shifted to more energy efficient multi-core designs, integrating multiple processing units (cores) on a single chip. The number of cores is expected to grow exponentially and future systems are expected to integrate thousands of processing units. In order to provide sufficient memory bandwidth in these systems, main memory is physically distributed over multiple memory controllers with non-uniform access to memory (NUMA). Past research has identified programming models based on fine-grained, dependent tasks as a key technique to unleash the parallel processing power of massively parallel general-purpose computing architectures. However, the execution of task-paralel programs on architectures with non-uniform memory access and the dynamic optimizations to mitigate NUMA effects have received only little interest. In this thesis, we explore the main factors on performance and data locality of task-parallel programs and propose a set of transparent, portable and fully automatic on-line mapping mechanisms for tasks to cores and data to memory controllers in order to improve data locality and performance. Placement decisions are based on information about point-to-point data dependences, readily available in the run-time systems of modern task-parallel programming frameworks. The experimental evaluation of these techniques is conducted on our implementation in the run-time of the OpenStream language and a set of high-performance scientific benchmarks. Finally, we designed and implemented Aftermath, a tool for performance analysis and debugging of task-parallel applications and run-times.

NeMeSys is a NUMA-aware graph pattern processing engine, which uses the Near Memory Processing paradigm to allow for high scalability. With modern server systems incorporating an increasing amount of main memory, we can store graphs and compute analytical graph algorithms like graph pattern matching completely in-memory. Our system blends state-of-the-art approaches from the transactional database world together with graph processing principles. We demonstrate, that graph pattern processing - standalone and workloads - can be controlled by leveraging different partitioning strategies, applying Bloom filter based messaging optimization and, given performance constraints, can save energy by applying frequency scaling of CPU cores.

The ever-growing demand for more computing power forces hardware vendors to put an increasing number of multiprocessors into a single server system, which usually exhibits a non-uniform memory access (NUMA). In-memory database systems running on NUMA platforms face several issues such as the increased latency and the decreased bandwidth when accessing remote main memory. To cope with these NUMA-related issues, a DBMS has to allow flexible data partitioning and data placement at runtime. In this demonstration, we present ERIS, our NUMA-aware in-memory storage engine. ERIS uses an adaptive partitioning approach that exploits the topology of the underlying NUMA platform and significantly reduces NUMA-related issues. We demonstrate throughput numbers and hardware performance counter evaluations of ERIS and a NUMA-unaware index for different workloads and configurations. All experiments are conducted on a standard server system as well as on a system consisting of 64 multiprocessors, 512 cores, and 8 TBs main memory.

Les systèmes de calcul actuels sont généralement des grappes de machines composés de nombreux processeurs à l'architecture fortement hiérarchique. Leur exploitation constitue le défi majeur des implémentations de modèles de programmation tels MPI ou OpenMP. Une pratique courante consiste à mélanger ces deux modèles pour bénéficier des avantages de chacun. Cependant ces modèles n'ont pas été pensés pour fonctionner conjointement ce qui pose des problèmes de performances. Les travaux de cette thèse visent à assister le développeur dans la programmation d'application de type hybride. Il s'appuient sur une analyse de la hiérarchie architecturale du système de calcul pour dimensionner les ressources d'exécution (processus et threads). Plutôt qu'une approche hybride classique, créant un processus MPI multithreadé par noeud, nous évaluons de façon automatique des solutions alternatives, avec plusieurs processus multithreadés par noeud, mieux adaptées aux machines de calcul modernes. / Modern computing servers usually consist in clusters of computers with several multi-core CPUs featuring a highly hierarchical hardware design. The major challenge of the programming models implementations is to efficiently take benefit from these servers. Combining two type of models, like MPI and OpenMP, is a current trend to reach this point. However these programming models haven't been designed to work together and that leads to performance issues. In this thesis, we propose to assist the programmer who develop hybrid applications. We lean on an analysis of the computing system architecture in order to set the number of processes and threads. Rather than a classical hybrid approach, that is to say creating one multithreaded MPI process per node, we automatically evaluate alternative solutions, with several multithreaded processes per node, better fitted to modern computing systems.

Les joueurs exigeants acquièrent dès que possible une carte graphique capable de satisfaire leur soif d'immersion dans des jeux dont la précision, le réalisme et l'interactivité redoublent d'intensité au fil du temps. Depuis l'avènement des cartes graphiques dédiées au calcul généraliste, ils n'en sont plus les seuls clients. Dans un premier temps, nous analysons l'apport de ces architectures parallèles spécifiques pour des simulations physiques à grande échelle. Cette étude nous permet de mettre en avant un goulot d'étranglement en particulier limitant la performance des simulations. Partons d'un cas typique : les fissures d'une structure complexe de type barrage en béton armé peuvent être modélisées par un ensemble de particules. La cohésion de la matière ainsi simulée est assurée par les interactions entre elles. Chaque particule est représentée en mémoire par un ensemble de paramètres physiques à consulter systématiquement pour tout calcul de forces entre deux particules. Ainsi, pour que les calculs soient rapides, les données de particules proches dans l'espace doivent être proches en mémoire. Dans le cas contraire, le nombre de défauts de cache augmente et la limite de bande passante de la mémoire peut être atteinte, particulièrement en parallèle, bornant les performances. L'enjeu est de maintenir l'organisation des données en mémoire tout au long de la simulation malgré les mouvements des particules. Les algorithmes de tri standard ne sont pas adaptés car ils trient systématiquement tous les éléments. De plus, ils travaillent sur des structures denses ce qui implique de nombreux déplacements de données en mémoire. Nous proposons PaVo, un algorithme de tri dit adaptatif, c'est-à-dire qu'il sait tirer parti de l'ordre pré-existant dans une séquence. De plus, PaVo maintient des trous dans la structure, répartis de manière à réduire le nombre de déplacements mémoires nécessaires. Nous présentons une généreuse étude expérimentale et comparons les résultats obtenus à plusieurs tris renommés. La diminution des accès à la mémoire a encore plus d'importance pour des simulations à grande échelles sur des architectures parallèles. Nous détaillons une version parallèle de PaVo et évaluons son intérêt. Pour tenir compte de l'irrégularité des applications, la charge de travail est équilibrée dynamiquement par vol de travail. Nous proposons de distribuer automatiquement les données en mémoire de manière à profiter des architectures hiérarchiques. Les tâches sont pré-assignées aux cœurs pour utiliser cette distribution et nous adaptons le moteur de vol pour favoriser des vols de tâches concernant des données proches en mémoire. / Gamers are used to throw onto the latest graphics cards to play immersive games which precision, realism and interactivity keep increasing over time. With general-propose processing on graphics processing units, scientists now participate in graphics card use too. First, we examine these architectures interest for large-scale physics simulations. Drawing on this experience, we highlight in particular a bottleneck in simulations performance. Let us consider a typical situation: cracks in complex reinforced concrete structures such as dams are modelised by many particles. Interactions between particles simulate the matter cohesion. In computer memory, each particle is represented by a set of physical parameters used for every force calculations between two particles. Then, to speed up computations, data from particles close in space should be close in memory. Otherwise, the number of cache misses raises up and memory bandwidth may be reached, specially in parallel environments, limiting global performance. The challenge is to maintain data organization during the simulations despite particle movements. Classical sorting algorithms do not suit such situations because they consistently sort all the elements. Besides, they work upon dense structures leading to a lot of memory transfers. We propose PaVo, an adaptive sort which means it benefits from sequence presortedness. Moreover, to reduce the number of necessary memory transfers, PaVo spreads some gaps inside the data structure. We present a large experimental study and confront results to reputed sort algorithms. Reducing memory requests is again more important for large scale simulations with parallel architectures. We detail a parallel version of PaVo and evaluate its interest. To deal with application irregularities, we do load balancing with work-stealing. We take advantage of hierarchical architectures by automatically distributing data in memory. Thus, tasks are pre-assigned to cores with respect to this organization and we adapt the scheduler to favor steals of tasks working on data close in memory.

La résolution de systèmes d'équations linéaires creux est au cœur de nombreux domaines d'applications. De même que la quantité de ressources de calcul augmente dans les architectures modernes, offrant ainsi de nouvelles perspectives, la taille des problèmes rencontré de nos jours dans les applications de simulations numériques augmente aussi et de façon significative. L'exploitation des architectures modernes pour la résolution efficace de problèmes de très grande taille devient ainsi un défit a relever, aussi bien d'un point de vue théorique que d'un point de vue algorithmique. L'objectif de cette thèse est d'adresser les problèmes de scalabilité des solveurs creux directs basés sur les méthodes multifrontales en environnements parallèles asynchrones. Dans la première partie de la thèse, nous nous intéressons a l'exploitation du parallélisme multicoeur sur les architectures a mémoire partagée. Nous introduisons une variante de l'algorithme Geist-Ng afin de gérer aussi bien un parallélisme a grain fin, a travers l'utilisation de librairies BLAS séquentiel et parallèle optimisées, que d'un parallélisme a plus gros grain, a travers l'utilisation de parallélisme a base de directives OpenMP. Nous considérons aussi des aspects mémoire afin d'améliorer les performances sur des architectures NUMA: (i) d'une part, nous analysons l'influence de la localité mémoire et utilisons des stratégies d'allocation mémoire adaptatives pour gérer les espaces de travail privés et partagés; (ii) d'autre part, nous nous intéressons au problème de partages de ressources sur les architectures multicoeurs, qui induisent des pénalités en termes de performances. Enfin, afin d'éviter que des ressources ne reste inertes a la fin de l'exécution de leurs taches, et ainsi, afin d'exploiter au mieux les ressources disponibles, nous proposons un algorithme conceptuellement proche de l'approche dite de vol de travail, et qui consiste a assigner les ressources de calculs inactives au taches de travail actives de façon dynamique. Dans la deuxième partie de cette thèse, nous nous intéressons aux architectures hybrides, a base de mémoire partagées et de mémoire distribuées, pour lesquels un travail particulier est nécessaire afin d'améliorer la scalabilité du traitement de problèmes de grande taille. Nous étudions et optimisons tout d'abord les noyaux d'algèbre linéaire danse utilisé dans les méthodes multifrontales en environnent distribué asynchrone, en repensant les variantes right-looking et left-looking de la factorisation LU avec pivotage partiel dans notre contexte distribué. De plus, du fait du parallélisme multicoeurs, la proportion des communications relativement aux calculs et plus importante. Nous expliquons comment construire des algorithmes de mapping qui minimisent les communications entres nœuds de l'arbre de dépendances de la méthode multifrontale. Nous montrons aussi que les communications asynchrones collectives deviennent christiques sur grand nombres de processeurs, et que les broadcasts asynchrones a base d'arbres de broadcast doivent être utilisés. Nous montrons ensuite que dans un contexte multifrontale complètement asynchrone, où plusieurs instances de tels communications ont lieux, de nouveaux problèmes de synchronisation apparaissent. Nous analysons et caractérisons les situations de deadlock possibles et établissons formellement des propriétés générales simples afin de résoudre ces problèmes de deadlock. Nous établissons par la suite des propriétés nous permettant de relâcher les synchronisations induites par la solutions précédentes, et ainsi, d'améliorer les performances. Enfin, nous montrons que les synchronisations peuvent être relâchées dans un solveur creux danse et illustrons les gains en performances, sur des problèmes de grande taille issue d'applications réelles, dans notre environnement multifrontale complètement asynchrone. / The solution of sparse systems of linear equations is at the heart of numerous applicationfields. While the amount of computational resources in modern architectures increases and offersnew perspectives, the size of the problems arising in today’s numerical simulation applicationsalso grows very much. Exploiting modern architectures to solve very large problems efficiently isthus a challenge, from both a theoretical and an algorithmic point of view. The aim of this thesisis to address the scalability of sparse direct solvers based on multifrontal methods in parallelasynchronous environments.In the first part of this thesis, we focus on exploiting multi-threaded parallelism on sharedmemoryarchitectures. A variant of the Geist-Ng algorithm is introduced to handle both finegrain parallelism through the use of optimized sequential and multi-threaded BLAS libraries andcoarser grain parallelism through explicit OpenMP based parallelization. Memory aspects arethen considered to further improve performance on NUMA architectures: (i) on the one hand,we analyse the influence of memory locality and exploit adaptive memory allocation strategiesto manage private and shared workspaces; (ii) on the other hand, resource sharing on multicoreprocessors induces performance penalties when many cores are active (machine load effects) thatwe also consider. Finally, in order to avoid resources remaining idle when they have finishedtheir share of the work, and thus, to efficiently exploit all computational resources available, wepropose an algorithm wich is conceptually very close to the work-stealing approach and whichconsists in dynamically assigning idle cores to busy threads/activities.In the second part of this thesis, we target hybrid shared-distributed memory architectures,for which specific work to improve scalability is needed when processing large problems. We firststudy and optimize the dense linear algebra kernels used in distributed asynchronous multifrontalmethods. Simulation, experimentation and profiling have been performed to tune parameterscontrolling the algorithm, in correlation with problem size and computer architecture characteristics.To do so, right-looking and left-looking variants of the LU factorization with partialpivoting in our distributed context have been revisited. Furthermore, when computations are acceleratedwith multiple cores, the relative weight of communication with respect to computationis higher. We explain how to design mapping algorithms minimizing the communication betweennodes of the dependency tree of the multifrontal method, and show that collective asynchronouscommunications become critical on large numbers of processors. We explain why asynchronousbroadcasts using standard tree-based communication algorithms must be used. We then showthat, in a fully asynchronous multifrontal context where several such asynchronous communicationtrees coexist, new synchronization issues must be addressed. We analyse and characterizethe possible deadlock situations and formally establish simple global properties to handle deadlocks.Such properties partially force synchronization and may limit performance. Hence, wedefine properties which enable us to relax synchronization and thus improve performance. Ourapproach is based on the observation that, in our case, as long as memory is available, deadlockscannot occur and, consequently, we just need to keep enough memory to guarantee thata deadlock can always be avoided. Finally, we show that synchronizations can be relaxed in astate-of-the-art solver and illustrate the performance gains on large real problems in our fullyasynchronous multifrontal approach.

Submitted by Geandra Rodrigues (geandrar@gmail.com) on 2018-04-03T14:25:45Z No. of bitstreams: 1 mirianbartels.pdf: 4004567 bytes, checksum: 2ab29faa5a26d58cd3e55dcbea4f5805 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2018-04-03T19:08:49Z (GMT) No. of bitstreams: 1 mirianbartels.pdf: 4004567 bytes, checksum: 2ab29faa5a26d58cd3e55dcbea4f5805 (MD5) / Made available in DSpace on 2018-04-03T19:08:49Z (GMT). No. of bitstreams: 1 mirianbartels.pdf: 4004567 bytes, checksum: 2ab29faa5a26d58cd3e55dcbea4f5805 (MD5) Previous issue date: 2012-12-17 / PROQUALI (UFJF) / Numa e a ninfa, obra do escritor pré-modernista Afonso Henriques de Lima Barreto, foi publicada em 1915, tendo como alicerces o conto homônimo “Numa e a ninfa” e Aventuras do Dr. Bogóloff, publicados em 1911 e 1912, respectivamente. Esta dissertação foi desenvolvida sob distintos olhares, que, entretanto, inter-relacionam-se: o primeiro olhar, fundamento desta pesquisa, volta-se para a teoria da transtextualidade de Gérard Genette: Numa e a ninfa, o romance, é hipertexto, enquanto os demais “Numa e a ninfa”, o conto, e Aventuras do Dr. Bogóloff, a crônica, são hipotextos. Tanto os textos de partida quanto o texto de chegada permitiram o aproveitamento do contexto sociopolítico do início da República, que tem o caráter de denúncia no palco da exibição literária. Um outro olhar, não menos importante, contemplou a teoria da Nova Crítica, que avalia os elementos da narrativa, confirmando a importância de se pesquisar um texto literário sob a ótica da sua estrutura. Ambas as análises são complementares e expõem a escritura genuína de Lima Barreto nos primórdios do século XX. / Numa e a ninfa, a novel from the pre-modernist writer Afonso Henriques de Lima Barreto, was published in 1915, and its basis was the namesake short story “Numa e a ninfa” and Aventuras do Dr. Bogóloff, published in 1911 and 1912, respectively. This thesis was developed under different views that, however, interrelate with each other: the first view, this research’s basis, is related to the transtextuality theory by Gérard Genette: Numa e a ninfa, the novel, is a hypertext, and the others “Numa e a ninfa”, the short story, and Aventuras do Dr. Bogóloff, the chronicle, are both hypotexts. Both the source texts and the target text allowed the use of the social-political context in the beginning of the Republic, which has the nature of complaint in the stage of the literary display. Another view, not the least, has beheld the New Criticism theory, which evaluates the narrative factors, confirming the importance of researching a literary text beneath the perspective of its structure. Both analysis are additional and show Lima Barreto's genuine writing in the beginning of the twentieth century.