ATAMM enhancement and multiprocessor performance evaluation

ATAMM (Algorithm To Architecture Mapping Model) enhancement and multiprocessor performance evaluation is discussed. The following topics are included: the ATAMM model; ATAMM enhancement; ADM (Advanced Development Model) implementation of ATAMM; and ATAMM support tools

Run-Time HEV Engine-Generator Power-Speed Optimization for Fuel Consumption and Emissions Reduction

As fuel economy and emissions standards become more stringent, Plug-in Hybrid Electric Vehicles (PHEV) using series architectures are being increasingly explored. Due to the decoupling of the Internal Combustion Engine (ICE) from the road, the primary control challenge in this architecture is the optimization of an ICE control law. A run-time Genset speed controller is presented for use during the charge-sustaining mode in a Series PHEV to find the optimal operating parameters for a conventional diesel engine coupled to an electric generator in terms of minimized fuel consumption and emissions generation. On board vehicle sensors provide real time data to the controller allowing for this method of optimization to be valid regardless of environment or operating conditions. The controller is validated through computer simulations using data from the Embry-Riddle EcoCAR 2 vehicle platform. Compared to the existing static Genset speed controller, the run-time controller resulted in a 40% reduction in fuel consumption and a 45% reduction in NOx production

Cross-layer design of multi-hop wireless networks

MULTI -hop wireless networks are usually defined as a collection of nodes equipped with radio transmitters, which not only have the capability to communicate each other in a multi-hop fashion, but also to route each others’ data packets. The distributed nature of such networks makes them suitable for a variety of applications where there are no assumed reliable central entities, or controllers, and may significantly improve the scalability issues of conventional single-hop wireless networks. This Ph.D. dissertation mainly investigates two aspects of the research issues related to the efficient multi-hop wireless networks design, namely: (a) network protocols and (b) network management, both in cross-layer design paradigms to ensure the notion of service quality, such as quality of service (QoS) in wireless mesh networks (WMNs) for backhaul applications and quality of information (QoI) in wireless sensor networks (WSNs) for sensing tasks. Throughout the presentation of this Ph.D. dissertation, different network settings are used as illustrative examples, however the proposed algorithms, methodologies, protocols, and models are not restricted in the considered networks, but rather have wide applicability. First, this dissertation proposes a cross-layer design framework integrating a distributed proportional-fair scheduler and a QoS routing algorithm, while using WMNs as an illustrative example. The proposed approach has significant performance gain compared with other network protocols. Second, this dissertation proposes a generic admission control methodology for any packet network, wired and wireless, by modeling the network as a black box, and using a generic mathematical 0. Abstract 3 function and Taylor expansion to capture the admission impact. Third, this dissertation further enhances the previous designs by proposing a negotiation process, to bridge the applications’ service quality demands and the resource management, while using WSNs as an illustrative example. This approach allows the negotiation among different service classes and WSN resource allocations to reach the optimal operational status. Finally, the guarantees of the service quality are extended to the environment of multiple, disconnected, mobile subnetworks, where the question of how to maintain communications using dynamically controlled, unmanned data ferries is investigated

Representation learning in unsupervised domain translation

Ce mémoire s'adresse au problème de traduction de domaine non-supervisée. La traduction non-supervisée cherche à traduire un domaine, le domaine source, à un domaine cible sans supervision. Nous étudions d'abord le problème en utilisant le formalisme du transport optimal. Dans un second temps, nous étudions le problème de transfert de sémantique à haut niveau dans les images en utilisant les avancés en apprentissage de représentations et de transfert d'apprentissages développés dans la communauté d'apprentissage profond. Le premier chapitre est dévoué à couvrir les bases des concepts utilisés dans ce travail. Nous décrivons d'abord l'apprentissage de représentation en incluant la description de réseaux de neurones et de l'apprentissage supervisé et non supervisé. Ensuite, nous introduisons les modèles génératifs et le transport optimal. Nous terminons avec des notions pertinentes sur le transfert d'apprentissages qui seront utiles pour le chapitre 3. Le deuxième chapitre présente \textit{Neural Wasserstein Flow}. Dans ce travail, nous construisons sur la théorie du transport optimal et démontrons que les réseaux de neurones peuvent être utilisés pour apprendre des barycentres de Wasserstein. De plus, nous montrons que les réseaux de neurones peuvent amortir n'importe quel barycentre, permettant d'apprendre une interpolation continue. Nous montrons aussi comment utiliser ces concepts dans le cadre des modèles génératifs. Finalement, nous montrons que notre approche permet d'interpoler des formes et des couleurs. Dans le troisième chapitre, nous nous attaquons au problème de transfert de sémantique haut niveau dans les images. Nous montrons que ceci peut être obtenu simplement avec un GAN conditionné sur la représentation apprise par un réseau de neurone. Nous montrons aussi comment ce processus peut être rendu non-supervisé si la représentation apprise est un regroupement. Finalement, nous montrons que notre approche fonctionne sur la tâche de transfert de MNIST à SVHN. Nous concluons en mettant en relation les deux contributions et proposons des travaux futures dans cette direction.This thesis is concerned with the problem of unsupervised domain translation. Unsupervised domain translation is the task of transferring one domain, the source domain, to a target domain. We first study this problem using the formalism of optimal transport. Next, we study the problem of high-level semantic image to image translation using advances in representation learning and transfer learning. The first chapter is devoted to reviewing the background concepts used in this work. We first describe representation learning including a description of neural networks and supervised and unsupervised representation learning. We then introduce generative models and optimal transport. We finish with the relevant notions of transfer learning that will be used in chapter 3. The second chapter presents Neural Wasserstein Flow. In this work, we build on the theory of optimal transport and show that deep neural networks can be used to learn a Wasserstein barycenter of distributions. We further show how a neural network can amortize any barycenter yielding a continuous interpolation. We also show how this idea can be used in the generative model framework. Finally, we show results on shape interpolation and colour interpolation. In the third chapter, we tackle the task of high level semantic image to image translation. We show that high level semantic image to image translation can be achieved by simply learning a conditional GAN with the representation learned from a neural network. We further show that we can make this process unsupervised if the representation learning is a clustering. Finally, we show that our approach works on the task of MNIST to SVHN

Tuning Parallel Applications in Parallel

Auto-tuning has recently received significant attention from the High Performance Computing community. Most auto-tuning approaches are specialized to work either on specific domains such as dense linear algebra and stencil computations, or only at certain stages of program execution such as compile time and runtime. Real scientific applications, however, demand a cohesive environment that can efficiently provide auto-tuning solutions at all stages of application development and deployment. Towards that end, we describe a unified end-to-end approach to auto-tuning scientific applications. Our system, Active Harmony, takes a search-based collaborative approach to auto-tuning. Application programmers, library writers and compilers collaborate to describe and export a set of performance related tunable parameters to the Active Harmony system. These parameters define a tuning search-space. The auto-tuner monitors the program performance and suggests adaptation decisions. The decisions are made by a central controller using a parallel search algorithm. The algorithm leverages parallel architectures to search across a set of optimization parameter values. Different nodes of a parallel system evaluate different configurations at each timestep. Active Harmony supports runtime adaptive code-generation and tuning for parameters that require new code (e.g. unroll factors). Effectively, we merge traditional feedback directed optimization and just-in-time compilation. This feature also enables application developers to write applications once and have the auto-tuner adjust the application behavior automatically when run on new systems. We evaluated our system on multiple large-scale parallel applications and showed that our system can improve the execution time by up to 46% compared to the original version of the program. Finally, we believe that the success of any auto-tuning research depends on how effectively application developers, domain-experts and auto-tuners communicate and work together. To that end, we have developed and released a simple and extensible language that standardizes the parameter space representation. Using this language, developers and researchers can collaborate to export tunable parameters to the tuning frameworks. Relationships (e.g. ordering, dependencies, constraints, ranking) between tunable parameters and search-hints can also be expressed

An Adaptive Modular Redundancy Technique to Self-regulate Availability, Area, and Energy Consumption in Mission-critical Applications

As reconfigurable devices\u27 capacities and the complexity of applications that use them increase, the need for self-reliance of deployed systems becomes increasingly prominent. A Sustainable Modular Adaptive Redundancy Technique (SMART) composed of a dual-layered organic system is proposed, analyzed, implemented, and experimentally evaluated. SMART relies upon a variety of self-regulating properties to control availability, energy consumption, and area used, in dynamically-changing environments that require high degree of adaptation. The hardware layer is implemented on a Xilinx Virtex-4 Field Programmable Gate Array (FPGA) to provide self-repair using a novel approach called a Reconfigurable Adaptive Redundancy System (RARS). The software layer supervises the organic activities within the FPGA and extends the self-healing capabilities through application-independent, intrinsic, evolutionary repair techniques to leverage the benefits of dynamic Partial Reconfiguration (PR). A SMART prototype is evaluated using a Sobel edge detection application. This prototype is shown to provide sustainability for stressful occurrences of transient and permanent fault injection procedures while still reducing energy consumption and area requirements. An Organic Genetic Algorithm (OGA) technique is shown capable of consistently repairing hard faults while maintaining correct edge detector outputs, by exploiting spatial redundancy in the reconfigurable hardware. A Monte Carlo driven Continuous Markov Time Chains (CTMC) simulation is conducted to compare SMART\u27s availability to industry-standard Triple Modular Technique (TMR) techniques. Based on nine use cases, parameterized with realistic fault and repair rates acquired from publically available sources, the results indicate that availability is significantly enhanced by the adoption of fast repair techniques targeting aging-related hard-faults. Under harsh environments, SMART is shown to improve system availability from 36.02% with lengthy repair techniques to 98.84% with fast ones. This value increases to five nines (99.9998%) under relatively more favorable conditions. Lastly, SMART is compared to twenty eight standard TMR benchmarks that are generated by the widely-accepted BL-TMR tools. Results show that in seven out of nine use cases, SMART is the recommended technique, with power savings ranging from 22% to 29%, and area savings ranging from 17% to 24%, while still maintaining the same level of availability

The Efficient Implementation of Correction Procedure via Reconstruction with GPU Computing

Computational fluid dynamics (CFD) has long been a useful tool to model fluid flow problems across many engineering disciplines, and while problem size, complexity, and difficulty continue to expand, the demands for robustness and accuracy grow. Furthermore, generating high-order accurate solutions has escalated the required computational resources, and as problems continue to increase in complexity, so will computational needs such as memory requirements and calculation time for accurate flow field prediction. To improve upon computational time, vast amounts of computational power and resources are employed, but even over dozens to hundreds of central processing units (CPUs), the required computational time to formulate solutions can be weeks, months, or longer, which is particularly true when generating high-order accurate solutions over large computational domains. One response to lower the computational time for CFD problems is to implement graphical processing units (GPUs) with current CFD solvers. GPUs have illustrated the ability to solve problems orders of magnitude faster than their CPU counterparts with identical accuracy. The goal of the presented work is to combine a CFD solver and GPU computing with the intent to solve complex problems at a high-order of accuracy while lowering the computational time required to generate the solution. The CFD solver should have high-order spacial capabilities to evaluate small fluctuations and fluid structures not generally captured by lower-order methods and be efficient for the GPU architecture. This research combines the high-order Correction Procedure via Reconstruction (CPR) method with compute unified device architecture (CUDA) from NVIDIA to reach these goals. In addition, the study demonstrates accuracy of the developed solver by comparing results with other solvers and exact solutions. Solving CFD problems accurately and quickly are two factors to consider for the next generation of solvers. GPU computing is a step forward for the CFD community in solving both current and up-coming problems fast and with high accuracy