Experimental analysis of computer system dependability

This paper reviews an area which has evolved over the past 15 years: experimental analysis of computer system dependability. Methodologies and advances are discussed for three basic approaches used in the area: simulated fault injection, physical fault injection, and measurement-based analysis. The three approaches are suited, respectively, to dependability evaluation in the three phases of a system's life: design phase, prototype phase, and operational phase. Before the discussion of these phases, several statistical techniques used in the area are introduced. For each phase, a classification of research methods or study topics is outlined, followed by discussion of these methods or topics as well as representative studies. The statistical techniques introduced include the estimation of parameters and confidence intervals, probability distribution characterization, and several multivariate analysis methods. Importance sampling, a statistical technique used to accelerate Monte Carlo simulation, is also introduced. The discussion of simulated fault injection covers electrical-level, logic-level, and function-level fault injection methods as well as representative simulation environments such as FOCUS and DEPEND. The discussion of physical fault injection covers hardware, software, and radiation fault injection methods as well as several software and hybrid tools including FIAT, FERARI, HYBRID, and FINE. The discussion of measurement-based analysis covers measurement and data processing techniques, basic error characterization, dependency analysis, Markov reward modeling, software-dependability, and fault diagnosis. The discussion involves several important issues studies in the area, including fault models, fast simulation techniques, workload/failure dependency, correlated failures, and software fault tolerance

An Intelligent System-on-a-Chip for a Real-Time Assessment of Fuel Consumption to Promote Eco-Driving

Pollution that originates from automobiles is a concern in the current world, not only because of global warming, but also due to the harmful effects on people’s health and lives. Despite regulations on exhaust gas emissions being applied, minimizing unsuitable driving habits that cause elevated fuel consumption and emissions would achieve further reductions. For that reason, this work proposes a self-organized map (SOM)-based intelligent system in order to provide drivers with eco-driving-intended driving style (DS) recommendations. The development of the DS advisor uses driving data from the Uyanik instrumented car. The system classifies drivers regarding the underlying causes of non-optimal DSs from the eco-driving viewpoint. When compared with other solutions, the main advantage of this approach is the personalization of the recommendations that are provided to motorists, comprising the handling of the pedals and the gearbox, with potential improvements in both fuel consumption and emissions ranging from the 9.5% to the 31.5%, or even higher for drivers that are strongly engaged with the system. It was successfully implemented using a field-programmable gate array (FPGA) device of the Xilinx ZynQ programmable system-on-a-chip (PSoC) family. This SOM-based system allows for real-time implementation, state-of-the-art timing performances, and low power consumption, which are suitable for developing advanced driving assistance systems (ADASs).This work was supported in part by the Spanish AEI and European FEDER funds under Grant TEC2016-77618-R (AEI/FEDER, UE) and by the University of the Basque Country under Grant GIU18/122

Empirical and Statistical Application Modeling Using on -Chip Performance Monitors.

To analyze the performance of applications and architectures, both programmers and architects desire formal methods to explain anomalous behavior. To this end, we present various methods that utilize non-intrusive, performance-monitoring hardware only recently available on microprocessors to provide further explanations of observed behavior. All the methods attempt to characterize and explain the instruction-level parallelism achieved by codes on different architectures. We also present a prototype tool automating the analysis process to exploit the advantages of the empirical and statistical methods proposed. The empirical, statistical and hybrid methods are discussed and explained with case study results provided. The given methods further the wealth of tools available to programmer\u27s and architects for generally understanding the performance of scientific applications. Specifically, the models and tools presented provide new methods for evaluating and categorizing application performance. The empirical memory model serves to quantify the hierarchical memory performance of applications by inferring the incurred latencies of codes after the effect of latency hiding techniques are realized. The instruction-level model and its extensions model on-chip performance analytically giving insight into inherent performance bottlenecks in superscalar architectures. The statistical model and its hybrid extension provide other methods of categorizing codes via their statistical variations. The PTERA performance tool automates the use of performance counters for use by these methods across platforms making the modeling process easier still. These unique methods provide alternatives to performance modeling and categorizing not available previously in an attempt to utilize the inherent modeling capabilities of performance monitors on commodity processors for scientific applications

A Survey of Phase Classification Techniques for Characterizing Variable Application Behavior

Adaptable computing is an increasingly important paradigm that specializes system resources to variable application requirements, environmental conditions, or user requirements. Adapting computing resources to variable application requirements (or application phases) is otherwise known as phase-based optimization. Phase-based optimization takes advantage of application phases, or execution intervals of an application, that behave similarly, to enable effective and beneficial adaptability. In order for phase-based optimization to be effective, the phases must first be classified to determine when application phases begin and end, and ensure that system resources are accurately specialized. In this paper, we present a survey of phase classification techniques that have been proposed to exploit the advantages of adaptable computing through phase-based optimization. We focus on recent techniques and classify these techniques with respect to several factors in order to highlight their similarities and differences. We divide the techniques by their major defining characteristics---online/offline and serial/parallel. In addition, we discuss other characteristics such as prediction and detection techniques, the characteristics used for prediction, interval type, etc. We also identify gaps in the state-of-the-art and discuss future research directions to enable and fully exploit the benefits of adaptable computing.Comment: To appear in IEEE Transactions on Parallel and Distributed Systems (TPDS

Applied inference

We propose and apply a new simulation paradigm for microarchitectural design evaluation and optimization. This paradigm enables more comprehensive design studies by combining spatial sampling and statistical inference. Specifically, this paradigm (i) defines a large, comprehensive design space, (ii) samples points from the space for simulation, and (iii) constructs regression models based on sparse simulations. This approach greatly improves the computational efficiency of microarchitectural simulation and enables new capabilities in design space exploration. We illustrate new capabilities in three case studies for a large design space of approximately 260,000 points: (i) Pareto frontier, (ii) pipeline depth, and (iii) multiprocessor heterogeneity analyses. In particular, regression models are exhaustively evaluated to identify Pareto optimal designs that maximize performance for given power budgets. These models enable pipeline depth studies in which all parameters vary simultaneously with depth, thereby more effectively revealing interactions with nondepth parameters. Heterogeneity analysis combines regression-based optimization with clustering heuristics to identify efficient design compromises between similar optimal architectures. These compromises are potential core designs in a heterogeneous multicore architecture. Increasing heterogeneity can improve bips3/w efficiency by as much as 2.4×, a theoretical upper bound on heterogeneity benefits that neglects contention between shared resources as well as design complexity. Collectively these studies demonstrate regression models' ability to expose trends and identify optima in diverse design regions, motivating the application of such models in statistical inference for more effective use of modern simulator infrastructure.Engineering and Applied Science

Design and Electronic Implementation of Machine Learning-based Advanced Driving Assistance Systems

200 p.Esta tesis tiene como objetivo contribuir al desarrollo y perfeccionamiento de sistemas avanzados a la conducción (ADAS). Para ello, basándose en bases de datos de conducción real, se exploran las posibilidades de personalización de los ADAS existentes mediante técnicas de machine learning, tales como las redes neuronales o los sistemas neuro-borrosos. Así, se obtienen parámetros característicos del estilo cada conductor que ayudan a llevar a cabo una personalización automatizada de los ADAS que equipe el vehículo, como puede ser el control de crucero adaptativo. Por otro lado, basándose en esos mismos parámetros de estilo de conducción, se proponen nuevos ADAS que asesoren a los conductores para modificar su estilo de conducción, con el objetivo de mejorar tanto el consumo de combustible y la emisión de gases de efecto invernadero, como el confort de marcha. Además, dado que esta personalización tiene como objetivo que los sistemas automatizados imiten en cierta manera, y siempre dentro de parámetros seguros, el estilo del conductor humano, se espera que contribuya a incrementar la aceptación de estos sistemas, animando a la utilización y, por tanto, contribuyendo positivamente a la mejora de la seguridad, de la eficiencia energética y del confort de marcha. Además, estos sistemas deben ejecutarse en una plataforma que sea apta para ser embarcada en el automóvil, y, por ello, se exploran las posibilidades de implementación HW/SW en dispositivos reconfigurables tipo FPGA. Así, se desarrollan soluciones HW/SW que implementan los ADAS propuestos en este trabajo con un alto grado de exactitud, rendimiento, y en tiempo real

Cooperative agent-based SANET architecture for personalised healthcare monitoring

The application of an software agent-based computational technique that implements Extended Kohonen Maps (EKMs) for the management of Sensor-Actuator networks (SANETs) in health-care facilities. The agent-based model incorporates the BDI (Belief-Desire-Intention) Agent paradigms by Georgeff et al. EKMs perform the quantitative analysis of an algorithmic artificial neural network process by using an indirect-mapping EKM to self-organize. Current results show a combinatorial approach to optimization with EKMs provides an improvement in event trajectory estimation compared to standalone cooperative EKM processes to allow responsive event detection for patient monitoring scenarios. This will allow healthcare professionals to focus less on administrative tasks, and more on improving patient needs, particularly with people who are in need for dedicated care and round-the-clock monitoring. ©2010 IEEE

Measuring program similarity for efficient benchmarking and performance analysis of computer systems

textComputer benchmarking involves running a set of benchmark programs to measure performance of a computer system. Modern benchmarks are developed from real applications. Applications are becoming complex and hence modern benchmarks run for a very long time. These benchmarks are also used for performance evaluation in the early design phase of microprocessors. Due to the size of benchmarks and increase in complexity of microprocessor design, the effort required for performance evaluation has increased significantly. This dissertation proposes methodologies to reduce the effort of benchmarking and performance evaluation of computer systems. Identifying a set of programs that can be used in the process of benchmarking can be very challenging. A solution to this problem can start by identifying similarity between programs to capture the diversity in their behavior before they can be considered for benchmarking. The aim of this methodology is to identify redundancy in the set of benchmarks and find a subset of representative benchmarks with the least possible loss of information. This dissertation proposes the use of program characteristics which capture the performance behavior of programs and identifies representative benchmarks applicable over a wide range of system configurations. The use of benchmark subsetting has not been restricted to academic research. Recently, the SPEC CPU subcommittee used the information derived from measuring similarity based on program behavior characteristics between different benchmark candidates as one of the criteria for selecting the SPEC CPU2006 benchmarks. The information of similarity between programs can also be used to predict performance of an application when it is difficult to port the application on different platforms. This is a common problem when a customer wants to buy the best computer system for his application. Performance of a customer's application on a particular system can be predicted using the performance scores of the standard benchmarks on that system and the similarity information between the application and the benchmarks. Similarity between programs is quantified by the distance between them in the space of the measured characteristics, and is appropriately used to predict performance of a new application using the performance scores of its neighbors in the workload space.Electrical and Computer Engineerin