CONCURRENT DIAGNOSTICS IN MULTIPROCESSOR SYSTEMS

The paper presents a survey of diagnostic methods for multiprocessor systems. The diagnostic means known so far are first summarized and evaluated from the point of view of their applicability to systems with distributed control and specifically to the multiprocessor systems. A combination of different diagnostic means is then suggested in order to achieve the maximum diagnostic coverage with minimum overhead

Testing the bus guardian unit of the FTMP

Fault-tolerant multiprocessor (FTMP) operation is discussed. Fault-modeling in the bus guardian units (BGUs) is covered. Testing the BGU is discussed. A testing algorithm is proposed

The content and delivery of psychological interventions for perinatal depression by non-specialist health workers in low and middle income countries: a systematic review.

Psychological interventions delivered by non-specialist health workers are effective for the treatment of perinatal depression in low- and middle-income countries. In this systematic review, we describe the content and delivery of such interventions. Nine studies were identified. The interventions shared a number of key features, such as delivery provided within the context of routine maternal and child health care beginning in the antenatal period and extending postnatally; focus of the intervention beyond the mother to include the child and involving other family members; and attention to social problems and a focus on empowerment of women. All the interventions were adapted for contextual and cultural relevance; for example, in domains of language, metaphors and content. Although the competence and quality of non-specialist health workers delivered interventions was expected to be achieved through structured training and ongoing supervision, empirical evaluations of these were scarce. Scalability of these interventions also remains a challenge and needs further attention

Constraint Based Diagnosis Algorithms For Multiprocessors

Constraint-based diagnosis algorithms for multiprocessors A. Petri, P. Urban, J. Altmann, M. Dal Cin, E. Selenyi, K. Tilly, A. Pataricza In the latest years, new ideas appeared in system level diagnosis of multiprocessor systems. In contrary to the traditional diagnosis models (like PMC, BGM, etc.) which use strictly graph-oriented methods to determine the faulty components in a system, these new theories prefer AI-based algorithms, especially CSP methods. Syndrome decoding, the basic problem of self-diagnosis, can be easily transformed into constraints between the state of the tester and the tested components. Therefore, the diagnosis algorithm can be derived from a special constraint solving algorithm. The "benign" nature of the constraints (all their variables, representing the fault states of the components, have a very limited domain; the constraints are simple and similar to each other) reduces the algorithm's complexity so it can be converted to a powerful distributed diagnosis method with a minimal overhead. Experimental algorithms (using both centralized and distributed approach) were implemented for a Parsytec GC massively parallel multiprocessor system

Fault diagnosis of distributed systems : analysis, simulation and performance measurement.

Fault diagnosis forms an essential component in the design of highly reliable distributed computing systems. Early models for diagnosis require a global observer, whereas the diagnosis is shared between the systems nodes in later models. These models are reviewed and their different diagnosability properties reconciled. The design of improved fault diagnosis algorithms for systems without a global observer provides the main motivation for the thesis. The modified algorithm SELF3 [Hoss88] is taken as a starting point. A number of communication architectures used in distributed systems are reviewed. The properties of diagnosis algorithms depend strongly on the testing graph. A general class of testing graphs, designated as H-graphs, (which are a generalization of Dꞩṭ graphs introduced in [Prep67]), are investigated and their diagnostic properties determined. A software simulator for distributed systems has been written as the main investigative tool for diagnosis algorithms. The design and structure of the simulator are described. The diagnosis process is measured in terms of diagnostic time and number of messages produced, and the factors upon which these quantities depend are identified. The results of simulation of a number of systems are given under various fault conditions. A modified way of routing diagnosis messages, which, especially in large system s, results in a reduction in both the number of diagnosis messages and the time required to perform diagnosis, is presented. The thesis also contains a number of specific recommendations for improving existing self-diagnosis algorithms

Constraint Based System-Level Diagnosis of Multiprocessors

Massively parallel multiprocessors induce new requirements for system-level fault diagnosis, like handling a huge number of processing elements in an inhomogeneous system. Traditional diagnostic models (like PMC, BGM, etc.) are insufficient to fulfill all of these requirements. This paper presents a novel modelling technique, based on a special area of artificial intelligence (AI) methods: constraint satisfaction (CS). The constraint based approach is able to handle functional faults in a similar way to the Russel-Kime model. Moreover, it can use multiple-valued logic to deal with system components having multiple fault modes. The resolution of the produced models can be adjusted to fit the actual diagnostic goal. Consequently, constrint based methods are applicable to a much wider range of multiprocessor architectures than earlier models. The basic problem of system-level diagnosis, syndrome decoding, can be easily transformed into a constraint satisfaction problem (CSP). Thus, the diagnosis algorithm can be derived from the related constraint solving algorithm. Different abstraction leveles can be used for the various diagnosis resolutions, employing the same methodology. As examples, two algorithms are described in the paper; both of them is intended for the Parsytec GCel massively parallel system. The centralized method uses a more elaborate system model, and provides detailed diagnostic information, suitable for off-line evaluation. The distributed method makes fast decisions for reconfiguration control, using a simplified model. Keywords system-level self-diagnosis, massively parallel computing systems, constraint satisfaction, diagnostic models, centralized and distributed diagnostic algorithms

Robust low-power digital circuit design in nano-CMOS technologies

Device scaling has resulted in large scale integrated, high performance, low-power, and low cost systems. However the move towards sub-100 nm technology nodes has increased variability in device characteristics due to large process variations. Variability has severe implications on digital circuit design by causing timing uncertainties in combinational circuits, degrading yield and reliability of memory elements, and increasing power density due to slow scaling of supply voltage. Conventional design methods add large pessimistic safety margins to mitigate increased variability, however, they incur large power and performance loss as the combination of worst cases occurs very rarely. In-situ monitoring of timing failures provides an opportunity to dynamically tune safety margins in proportion to on-chip variability that can significantly minimize power and performance losses. We demonstrated by simulations two delay sensor designs to detect timing failures in advance that can be coupled with different compensation techniques such as voltage scaling, body biasing, or frequency scaling to avoid actual timing failures. Our simulation results using 45 nm and 32 nm technology BSIM4 models indicate significant reduction in total power consumption under temperature and statistical variations. Future work involves using dual sensing to avoid useless voltage scaling that incurs a speed loss. SRAM cache is the first victim of increased process variations that requires handcrafted design to meet area, power, and performance requirements. We have proposed novel 6 transistors (6T), 7 transistors (7T), and 8 transistors (8T)-SRAM cells that enable variability tolerant and low-power SRAM cache designs. Increased sense-amplifier offset voltage due to device mismatch arising from high variability increases delay and power consumption of SRAM design. We have proposed two novel design techniques to reduce offset voltage dependent delays providing a high speed low-power SRAM design. Increasing leakage currents in nano-CMOS technologies pose a major challenge to a low-power reliable design. We have investigated novel segmented supply voltage architecture to reduce leakage power of the SRAM caches since they occupy bulk of the total chip area and power. Future work involves developing leakage reduction methods for the combination logic designs including SRAM peripherals

GA-Based fault diagnosis algorithms for distributed systems

Distributed Systems are becoming very popular day-by-day due to their applications in various fields such as electronic automotives, remote environment control like underwater sensor network, K-connected networks. Faults may aect the nodes of the system at any time. So diagnosing the faulty nodes in the distributed system is an worst necessity to make the system more reliable and ecient. This thesis describes about dierent types of faults, system and fault model, those are already in literature. As the evolutionary approaches give optimum outcome than probabilistic approaches, we have developed Genetic algorithm based fault diagnosis algorithm which provides better result than other fault diagnosis algorithms. The GA-based fault diagnosis algorithm has worked upon dierent types of faults like permanent as well as intermittent faults in a K-connected system. Simulation results demonstrate that the proposed Genetic Algorithm Based Permanent Fault Diagnosis Algorithm(GAPFDA) and Genetic Algorithm Based Intermittent Fault Diagnosis Algorithm (GAIFDA) decreases the number of messages transferred and the time needed to diagnose the faulty nodes in a K-connected distributed system. The decrease in CPU time and number of steps are due to the application of supervised mutation in the fault diagnosis algorithms. The time complexity and message complexity of GAPFDA are analyzed as O(n*P*K*ng) and O(n*K) respectively. The time complexity and message complexity of GAIFDA are O(r*n*P*K*ng) and O(r*n*K) respectively, where ’n’ is the number of nodes, ’P’ is the population size, ’K’ is the connectivity of the network, ’ng’ is the number of generations (steps), ’r’ is the number of rounds. Along with the design of fault diagnosis algorithm of O(r*k) for diagnosing the transient-leading-to-permanent faults in the actuators of a k-fault tolerant Fly-by-wire(FBW) system, an ecient scheduling algorithm has been developed to schedule dierent tasks of a FBW system, here ’r’ denotes the number of rounds. The proposed algorithm for scheduling the task graphs of a multi-rate FBW system demonstrates that, maximization in microcontroller’s execution period reduces the number of microcontrollers needed for performing diagnosis

Dynamic Fault Diagnosis in Mobile Ad Hoc Networks

Fault diagnosis in Mobile Ad-hoc Networks (MANETs) is very challenging task. Diagnosis algorithm should be efficient enough to find the status (either faulty or fault free) of each mobile in the network. The models in the literature are either for static fault or dynamic fault. Dynamic fault identification is more complex and difficult than static fault. In this thesis, we proposed Dynamic Distributed Diagnosis Model to identify dynamic faults arising during the testing phase of the diagnosis session. The model assumes that each node has fixed and same set of neighbours i.e. the MANET topology is static throughout the diagnosis session. Our model works on a network with $n$ number of nodes, which is $\sigma$-diagnosable. Where $\sigma$ is one less than the minimum degree of a node in the network. It has two variation based on dissemination method, first is simple flooding approach and second is based on spanning tree. The flooding based model consists of two phases; a testing phase and a dissemination phase. The spanning tree based model has three phase; a testing phase, a building phase and a dissemination phase. In testing phase, we have used the concept of heartbeat, where every mobile broadcasts a response message at fixed interval, so that a node can correctly be diagnosed by at least one fault free neighbour. Building phase constructs a spanning tree with fault-free mobiles. Dissemination phase, with the help of spanning tree, disseminates the local diagnostic views through the fault-free mobiles. After aggregating the entire views, initiator node disseminates the global diagnostic view to the fault free mobiles down the spanning tree. In this way, all fault free units reach to an agreement about the status of other nodes in the network. Further, we have given the proof of correctness and completeness of our model and found the time complexity, and compared the simulation results with the existing fault diagnosis protocols