Revisiting credit distribution algorithms for distributed termination detection

This paper revisits distributed termination detection algorithms in the context of High-Performance Computing (HPC) applications. We introduce an efficient variant of the Credit Distribution Algorithm (CDA) and compare it to the original algorithm (HCDA) as well as to its two primary competitors: the Four Counters algorithm (4C) and the Efficient Delay-Optimal Distributed algorithm (EDOD). We analyze the behavior of each algorithm for some simplified task-based kernels and show the superiority of CDA in terms of the number of control messages.Peer ReviewedPostprint (author's final draft

Revisiting Credit Distribution Algorithms for Distributed Termination Detection

International audienceThis paper revisits distributed termination detection algorithms in the context of High-Performance Computing (HPC) applications. We introduce an efficient variant of the Credit Distribution Algorithm (CDA) and compare it to the original algorithm (HCDA) as well as to its two primary competitors: the Four Counters algorithm (4C) and the Efficient Delay-Optimal Distributed algorithm (EDOD). We analyze the behavior of each algorithm for some simplified task-based kernels and show the superiority of CDA in terms of the number of control messages

Comparing Distributed Termination Detection Algorithms for Task-Based Runtime Systems on HPC platforms

International audienceThis paper revisits distributed termination detection algorithms in the context of High-Performance Computing (HPC) applications. We introduce an efficient variant of the Credit Distribution Algorithm (CDA) and compare it to the original algorithm (HCDA) as well as to its two primary competitors: the Four Counters algorithm (4C) and the Efficient Delay-Optimal Distributed algorithm (EDOD). We analyze the behavior of each algorithm for some simplified task-based kernels and show the superiority of CDA in terms of the number of control messages. We then compare the implementation of these algorithms over a task-based runtime system, PaRSEC and show the advantages and limitations of each approach on a practical implementation

Distributed Termination Detection For Multiagent Protocols

The research conducted in this thesis is on distributed termination detection in multiagent systems. Agents engage in complex interactions by executing behaviour specifications in the form of protocols. This work presents and experiments with a framework for making termination in a multiagent system explicit. As a side effect, the mechanism can be exploited to aid management of agent interactions, by providing visibility of the interaction process and can be extended to drive multiagent system management tasks such as timely garbage collection. Results from previous attempts to deploy agents systems when scaling up, e.g. Agentcities, have shown and exposed a big gap between theory and practice especially in the reliability and availability of deployed systems. In particular more work needs to be done in the area of supporting agent infrastructures as much as in theoretical agent foundations. There are two aspects to this problem of termination detection in multiagent systems, firstly, the formal verification of behaviour at compile-time and secondly, monitoring and control at run-time. Regarding the former, there has been some work on the ver- 13 ification of agent communication languages. But overall verification is difficult and often requires knowledge of internal states of agents at compile time, and as yet has not been satisfactorily solved to be deployed in real systems. The second, the runtime approach is adopted in here. The research is not about protocol engineering but assumes correct protocols, and protocol specifications to be finite state machine graphs. Given these correct verified protocols, the thesis proposes a number of definitions culminating in identification of minimal information in the form of sub-protocols that agents being autonomous, can make available for the termination detection. An off line procedure for deriving these sub-protocols is then presented. The thesis then considers a termination detection model, and within this model, proposes an conversationmodel encompassing protocol executions, with hierarchical conversations modelled as diffusing computation trees and defines a number of predicates to derive termination in centralised and distributed environments. Algorithms that implement these predicates are sketched and some complexity analysis is performed. The thesis then considers a prototype implementation evaluated over some defined detection delays metric. The evaluation approach is heavily empirical, with an experimental approach adopted to evaluate various configurations of the termination detection mechanism. The evaluation employs robust resampling and bootstrapping methods to analyse and obtain distributions and confidence intervals of the detection delays metric for the termination detection mechanism

Locomotor Network Dynamics Governed By Feedback Control In Crayfish Posture And Walking

Sensorimotor circuits integrate biomechanical feedback with ongoing motor activity to produce behaviors that adapt to unpredictable environments. Reflexes are critical in modulating motor output by facilitating rapid responses. During posture, resistance reflexes generate negative feedback that opposes perturbations to stabilize a body. During walking, assistance reflexes produce positive feedback that facilitates fast transitions between swing and stance of each step cycle. Until recently, sensorimotor networks have been studied using biomechanical feedback based on external perturbations in the presence or absence of intrinsic motor activity. Experiments in which biomechanical feedback driven by intrinsic motor activity is studied in the absence of perturbation have been limited. Thus, it is unclear whether feedback plays a role in facilitating transitions between behavioral states or mediating different features of network activity independent of perturbation. These properties are important to understand because they can elucidate how a circuit coordinates with other neural networks or contributes to adaptable motor output. Computational simulations and mathematical models have been used extensively to characterize interactions of negative and positive feedback with nonlinear oscillators. For example, neuronal action potentials are generated by positive and negative feedback of ionic currents via a membrane potential. While simulations enable manipulation of system parameters that are inaccessible through biological experiments, mathematical models ascertain mechanisms that help to generate biological hypotheses and can be translated across different systems. Here, a three-tiered approach was employed to determine the role of sensory feedback in a crayfish locomotor circuit involved in posture and walking. In vitro experiments using a brain-machine interface illustrated that unperturbed motor output of the circuit was changed by closing the sensory feedback loop. Then, neuromechanical simulations of the in vitro experiments reproduced a similar range of network activity and showed that the balance of sensory feedback determined how the network behaved. Finally, a reduced mathematical model was designed to generate waveforms that emulated simulation results and demonstrated how sensory feedback can control the output of a sensorimotor circuit. Together, these results showed how the strengths of different approaches can complement each other to facilitate an understanding of the mechanisms that mediate sensorimotor integration

Towards Energy-Efficient and Reliable Computing: From Highly-Scaled CMOS Devices to Resistive Memories

The continuous increase in transistor density based on Moore\u27s Law has led us to highly scaled Complementary Metal-Oxide Semiconductor (CMOS) technologies. These transistor-based process technologies offer improved density as well as a reduction in nominal supply voltage. An analysis regarding different aspects of 45nm and 15nm technologies, such as power consumption and cell area to compare these two technologies is proposed on an IEEE 754 Single Precision Floating-Point Unit implementation. Based on the results, using the 15nm technology offers 4-times less energy and 3-fold smaller footprint. New challenges also arise, such as relative proportion of leakage power in standby mode that can be addressed by post-CMOS technologies. Spin-Transfer Torque Random Access Memory (STT-MRAM) has been explored as a post-CMOS technology for embedded and data storage applications seeking non-volatility, near-zero standby energy, and high density. Towards attaining these objectives for practical implementations, various techniques to mitigate the specific reliability challenges associated with STT-MRAM elements are surveyed, classified, and assessed herein. Cost and suitability metrics assessed include the area of nanomagmetic and CMOS components per bit, access time and complexity, Sense Margin (SM), and energy or power consumption costs versus resiliency benefits. In an attempt to further improve the Process Variation (PV) immunity of the Sense Amplifiers (SAs), a new SA has been introduced called Adaptive Sense Amplifier (ASA). ASA can benefit from low Bit Error Rate (BER) and low Energy Delay Product (EDP) by combining the properties of two of the commonly used SAs, Pre-Charge Sense Amplifier (PCSA) and Separated Pre-Charge Sense Amplifier (SPCSA). ASA can operate in either PCSA or SPCSA mode based on the requirements of the circuit such as energy efficiency or reliability. Then, ASA is utilized to propose a novel approach to actually leverage the PV in Non-Volatile Memory (NVM) arrays using Self-Organized Sub-bank (SOS) design. SOS engages the preferred SA alternative based on the intrinsic as-built behavior of the resistive sensing timing margin to reduce the latency and power consumption while maintaining acceptable access time

Using min-sum loopy belief propagation for decentralised supply chain formation

Modern business trends such as agile manufacturing and virtual corporations require high levels of flexibility and responsiveness to consumer demand, and require the ability to quickly and efficiently select trading partners. Automated computational techniques for supply chain formation have the potential to provide significant advantages in terms of speed and efficiency over the traditional manual approach to partner selection. Automated supply chain formation is the process of determining the participants within a supply chain and the terms of the exchanges made between these participants. In this thesis we present an automated technique for supply chain formation based upon the min-sum loopy belief propagation algorithm (LBP). LBP is a decentralised and distributed message-passing algorithm which allows participants to share their beliefs about the optimal structure of the supply chain based upon their costs, capabilities and requirements. We propose a novel framework for the application of LBP to the existing state-of-the-art case of the decentralised supply chain formation problem, and extend this framework to allow for application to further novel and established problem cases. Specifically, the contributions made by this thesis are: • A novel framework to allow for the application of LBP to the decentralised supply chain formation scenario investigated using the current state-of-the-art approach. Our experimental analysis indicates that LBP is able to match or outperform this approach for the vast majority of problem instances tested. • A new solution goal for supply chain formation in which economically motivated producers aim to maximise their profits by intelligently altering their profit margins. We propose a rational pricing strategy that allows producers to earn significantly greater profits than a comparable LBP-based profitmaking approach. • An LBP-based framework which allows the algorithm to be used to solve supply chain formation problems in which goods are exchanged in multiple units, a first for a fully decentralised technique. As well as multiple-unit exchanges, we also model in this scenario realistic constraints such as factory capacities and input-to-output ratios. LBP continues to be able to match or outperform an extended version of the existing state-of-the-art approach in this scenario. • Introduction of a dynamic supply chain formation scenario in which participants are able to alter their properties or to enter or leave the process at any time. Our results suggest that LBP is able to deal easily with individual occurences of these alterations and that performance degrades gracefully when they occur in larger numbers

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Tanenbaum, A.S. [Promotor

Using min-sum loopy belief propagation for decentralised supply chain formation

Modern business trends such as agile manufacturing and virtual corporations require high levels of flexibility and responsiveness to consumer demand, and require the ability to quickly and efficiently select trading partners. Automated computational techniques for supply chain formation have the potential to provide significant advantages in terms of speed and efficiency over the traditional manual approach to partner selection. Automated supply chain formation is the process of determining the participants within a supply chain and the terms of the exchanges made between these participants. In this thesis we present an automated technique for supply chain formation based upon the min-sum loopy belief propagation algorithm (LBP). LBP is a decentralised and distributed message-passing algorithm which allows participants to share their beliefs about the optimal structure of the supply chain based upon their costs, capabilities and requirements. We propose a novel framework for the application of LBP to the existing state-of-the-art case of the decentralised supply chain formation problem, and extend this framework to allow for application to further novel and established problem cases. Specifically, the contributions made by this thesis are: • A novel framework to allow for the application of LBP to the decentralised supply chain formation scenario investigated using the current state-of-the-art approach. Our experimental analysis indicates that LBP is able to match or outperform this approach for the vast majority of problem instances tested. • A new solution goal for supply chain formation in which economically motivated producers aim to maximise their profits by intelligently altering their profit margins. We propose a rational pricing strategy that allows producers to earn significantly greater profits than a comparable LBP-based profitmaking approach. • An LBP-based framework which allows the algorithm to be used to solve supply chain formation problems in which goods are exchanged in multiple units, a first for a fully decentralised technique. As well as multiple-unit exchanges, we also model in this scenario realistic constraints such as factory capacities and input-to-output ratios. LBP continues to be able to match or outperform an extended version of the existing state-of-the-art approach in this scenario. • Introduction of a dynamic supply chain formation scenario in which participants are able to alter their properties or to enter or leave the process at any time. Our results suggest that LBP is able to deal easily with individual occurences of these alterations and that performance degrades gracefully when they occur in larger numbers.EThOS - Electronic Theses Online ServiceGBUnited Kingdo