Machine Learning for Architectural Design Space Exploration and Resource Control

Machine learning has enabled significant advancements in diverse fields, yet, with a few exceptions, has had limited impact on computer architecture. Recent work, however, has begun to explore broader application to design, optimization, and simulation. Notably, machine-learning-based strategies often surpass prior state-of-the-art analytical, heuristic, and human-expert approaches. This thesis first reviews existing work applying machine learning to architecture, ranging from simulation and run-time optimization, to individual component design involving the memory system, branch predictors, networks-on-chip, and GPUs. Next, the thesis presents a novel deep-reinforcement-learning framework for design space exploration. Finally, the thesis introduces an innovative strategy for resource optimization with multiple co-scheduled workloads. Taken together, these works present a promising future for machine-learning-based architectural design

Dependable Embedded Systems

This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems

An input centric paradigm for program dynamic optimizations and lifetime evolvement

Accurately predicting program behaviors (e.g., memory locality, method calling frequency) is fundamental for program optimizations and runtime adaptations. Despite decades of remarkable progress, prior studies have not systematically exploited the use of program inputs, a deciding factor of program behaviors, to help in program dynamic optimizations. Triggered by the strong and predictive correlations between program inputs and program behaviors that recent studies have uncovered, the dissertation work aims to bring program inputs into the focus of program behavior analysis and program dynamic optimization, cultivating a new paradigm named input-centric program behavior analysis and dynamic optimization.;The new optimization paradigm consists of three components, forming a three-layer pyramid. at the base is program input characterization, a component for resolving the complexity in program raw inputs and extracting important features. In the middle is input-behavior modeling, a component for recognizing and modeling the correlations between characterized input features and program behaviors. These two components constitute input-centric program behavior analysis, which (ideally) is able to predict the large-scope behaviors of a program\u27s execution as soon as the execution starts. The top layer is input-centric adaptation, which capitalizes on the novel opportunities created by the first two components to facilitate proactive adaptation for program optimizations.;This dissertation aims to develop this paradigm in two stages. In the first stage, we concentrate on exploring the implications of program inputs for program behaviors and dynamic optimization. We construct the basic input-centric optimization framework based on of line training to realize the basic functionalities of the three major components of the paradigm. For the second stage, we focus on making the paradigm practical by addressing multi-facet issues in handling input complexities, transparent training data collection, predictive model evolvement across production runs. The techniques proposed in this stage together cultivate a lifelong continuous optimization scheme with cross-input adaptivity.;Fundamentally the new optimization paradigm provides a brand new solution for program dynamic optimization. The techniques proposed in the dissertation together resolve the adaptivity-proactivity dilemma that has been limiting the effectiveness of existing optimization techniques. its benefits are demonstrated through proactive dynamic optimizations in Jikes RVM and version selection using IBM XL C Compiler, yielding significant performance improvement on a set of Java and C/C++ programs. It may open new opportunities for a broad range of runtime optimizations and adaptations. The evaluation results on both Java and C/C++ applications demonstrate the new paradigm is promising in advancing the current state of program optimizations

Parallel Patterns for Adaptive Data Stream Processing

In recent years our ability to produce information has been growing steadily, driven by an ever increasing computing power, communication rates, hardware and software sensors diffusion. This data is often available in the form of continuous streams and the ability to gather and analyze it to extract insights and detect patterns is a valuable opportunity for many businesses and scientific applications. The topic of Data Stream Processing (DaSP) is a recent and highly active research area dealing with the processing of this streaming data. The development of DaSP applications poses several challenges, from efficient algorithms for the computation to programming and runtime systems to support their execution. In this thesis two main problems will be tackled: * need for high performance: high throughput and low latency are critical requirements for DaSP problems. Applications necessitate taking advantage of parallel hardware and distributed systems, such as multi/manycores or cluster of multicores, in an effective way; * dynamicity: due to their long running nature (24hr/7d), DaSP applications are affected by highly variable arrival rates and changes in their workload characteristics. Adaptivity is a fundamental feature in this context: applications must be able to autonomously scale the used resources to accommodate dynamic requirements and workload while maintaining the desired Quality of Service (QoS) in a cost-effective manner. In the current approaches to the development of DaSP applications are still missing efficient exploitation of intra-operator parallelism as well as adaptations strategies with well known properties of stability, QoS assurance and cost awareness. These are the gaps that this research work tries to fill, resorting to well know approaches such as Structured Parallel Programming and Control Theoretic models. The dissertation runs along these two directions. The first part deals with intra-operator parallelism. A DaSP application can be naturally expressed as a set of operators (i.e. intermediate computations) that cooperate to reach a common goal. If QoS requirements are not met by the current implementation, bottleneck operators must be internally parallelized. We will study recurrent computations in window based stateful operators and propose patterns for their parallel implementation. Windowed operators are the most representative class of stateful data stream operators. Here computations are applied on the most recent received data. Windows are dynamic data structures: they evolve over time in terms of content and, possibly, size. Therefore, with respect to traditional patterns, the DaSP domain requires proper specializations and enhanced features concerning data distribution and management policies for different windowing methods. A structured approach to the problem will reduce the effort and complexity of parallel programming. In addition, it simplifies the reasoning about the performance properties of a parallel solution (e.g. throughput and latency). The proposed patterns exhibit different properties in terms of applicability and profitability that will be discussed and experimentally evaluated. The second part of the thesis is devoted to the proposal and study of predictive strategies and reconfiguration mechanisms for autonomic DaSP operators. Reconfiguration activities can be implemented in a transparent way to the application programmer thanks to the exploitation of parallel paradigms with well known structures. Furthermore, adaptation strategies may take advantage of the QoS predictability of the used parallel solution. Autonomous operators will be driven by means of a Model Predictive Control approach, with the intent of giving QoS assurances in terms of throughput or latency in a resource-aware manner. An experimental section will show the effectiveness of the proposed approach in terms of execution costs reduction as well as the stability degree of a system reconfiguration. The experiments will target shared and distributed memory architectures

Políticas de Copyright de Publicações Científicas em Repositórios Institucionais: O Caso do INESC TEC

A progressiva transformação das práticas científicas, impulsionada pelo desenvolvimento das novas Tecnologias de Informação e Comunicação (TIC), têm possibilitado aumentar o acesso à informação, caminhando gradualmente para uma abertura do ciclo de pesquisa. Isto permitirá resolver a longo prazo uma adversidade que se tem colocado aos investigadores, que passa pela existência de barreiras que limitam as condições de acesso, sejam estas geográficas ou financeiras. Apesar da produção científica ser dominada, maioritariamente, por grandes editoras comerciais, estando sujeita às regras por estas impostas, o Movimento do Acesso Aberto cuja primeira declaração pública, a Declaração de Budapeste (BOAI), é de 2002, vem propor alterações significativas que beneficiam os autores e os leitores. Este Movimento vem a ganhar importância em Portugal desde 2003, com a constituição do primeiro repositório institucional a nível nacional. Os repositórios institucionais surgiram como uma ferramenta de divulgação da produção científica de uma instituição, com o intuito de permitir abrir aos resultados da investigação, quer antes da publicação e do próprio processo de arbitragem (preprint), quer depois (postprint), e, consequentemente, aumentar a visibilidade do trabalho desenvolvido por um investigador e a respetiva instituição. O estudo apresentado, que passou por uma análise das políticas de copyright das publicações científicas mais relevantes do INESC TEC, permitiu não só perceber que as editoras adotam cada vez mais políticas que possibilitam o auto-arquivo das publicações em repositórios institucionais, como também que existe todo um trabalho de sensibilização a percorrer, não só para os investigadores, como para a instituição e toda a sociedade. A produção de um conjunto de recomendações, que passam pela implementação de uma política institucional que incentive o auto-arquivo das publicações desenvolvidas no âmbito institucional no repositório, serve como mote para uma maior valorização da produção científica do INESC TEC.The progressive transformation of scientific practices, driven by the development of new Information and Communication Technologies (ICT), which made it possible to increase access to information, gradually moving towards an opening of the research cycle. This opening makes it possible to resolve, in the long term, the adversity that has been placed on researchers, which involves the existence of barriers that limit access conditions, whether geographical or financial. Although large commercial publishers predominantly dominate scientific production and subject it to the rules imposed by them, the Open Access movement whose first public declaration, the Budapest Declaration (BOAI), was in 2002, proposes significant changes that benefit the authors and the readers. This Movement has gained importance in Portugal since 2003, with the constitution of the first institutional repository at the national level. Institutional repositories have emerged as a tool for disseminating the scientific production of an institution to open the results of the research, both before publication and the preprint process and postprint, increase the visibility of work done by an investigator and his or her institution. The present study, which underwent an analysis of the copyright policies of INESC TEC most relevant scientific publications, allowed not only to realize that publishers are increasingly adopting policies that make it possible to self-archive publications in institutional repositories, all the work of raising awareness, not only for researchers but also for the institution and the whole society. The production of a set of recommendations, which go through the implementation of an institutional policy that encourages the self-archiving of the publications developed in the institutional scope in the repository, serves as a motto for a greater appreciation of the scientific production of INESC TEC

A holistic approach towards intelligent hotspot prevention in network-on-chip-based multicores

Traffic hotspots, a severe form of network congestion, can be caused unexpectedly in a network-on-chip (NoC) due to the immanent spatio-temporal unevenness of application traffic. Hotspots reduce the NoC's effective throughput, where in the worst-case scenario, network traffic flows can be frozen indefinitely. To alleviate this problematic phenomenon several adaptive routing algorithms employ online load-balancing schemes, aiming to reduce the possibility of hotspots arising. Since most are not explicitly hotspot-agnostic, they cannot completely prevent hotspot formation(s) as their reactive capability to hotspots is merely passive. This paper presents a pro-active Hotspot-Preventive Routing Algorithm (HPRA) which uses the advance knowledge gained from network-embedded artificial neural network-based (ANN) hotspot predictors to guide packet routing in mitigating any unforeseen near-future hotspot occurrences. First, these ANN-based predictors are trained offline and during multicore operation they gather online statistical data to predict about-to-be-formed hotspots, promptly informing HPRA to take appropriate hotspot-preventive action(s). Next, in a holistic approach, additional ANN training is performed with data acquired after HPRA interferes, so as to further improve hotspot prediction accuracy; hence, the ANN mechanism does not only predict hotspots, but is also aware of changes that HPRA imposes upon the interconnect infrastructure. Evaluation results, including utilizing real application traffic traces gathered from parallelized workload executions onto a chip multiprocessor architecture, show that HPRA can improve network throughput up to 81 percent when compared with prior-art. Hardware synthesis results affirm the HPRA mechanism's moderate overhead requisites

XXIII Congreso Argentino de Ciencias de la Computación - CACIC 2017 : Libro de actas

Trabajos presentados en el XXIII Congreso Argentino de Ciencias de la Computación (CACIC), celebrado en la ciudad de La Plata los días 9 al 13 de octubre de 2017, organizado por la Red de Universidades con Carreras en Informática (RedUNCI) y la Facultad de Informática de la Universidad Nacional de La Plata (UNLP).Red de Universidades con Carreras en Informática (RedUNCI