25,354 research outputs found

    Modeling Network Contention Effects on All-to-All Operations

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    10 pagesOne of the most important collective communication patterns used in scientific applications is the complete exchange, also called All-to-All. Although efficient complete exchange algorithms have been studied for specific networks, general solutions like those available in well-known MPI distributions (e.g. the MPI_Alltoall operation) are strongly influenced by the congestion of network resources. In this paper we present an integrated approach to model the performance of the All-to-All collective operation. Our approach consists in identifying a contention signature that characterizes a given network environment, using it to augment a contention-free communication model. This approach allows an accurate prediction of the performance of the All-to-All operation over different network architectures with a small overhead. This approach is assessed by experimental results using three different network architectures, namely Fast Ethernet, Gigabit Ethernet and Myrinet

    Tuning the Level of Concurrency in Software Transactional Memory: An Overview of Recent Analytical, Machine Learning and Mixed Approaches

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    Synchronization transparency offered by Software Transactional Memory (STM) must not come at the expense of run-time efficiency, thus demanding from the STM-designer the inclusion of mechanisms properly oriented to performance and other quality indexes. Particularly, one core issue to cope with in STM is related to exploiting parallelism while also avoiding thrashing phenomena due to excessive transaction rollbacks, caused by excessively high levels of contention on logical resources, namely concurrently accessed data portions. A means to address run-time efficiency consists in dynamically determining the best-suited level of concurrency (number of threads) to be employed for running the application (or specific application phases) on top of the STM layer. For too low levels of concurrency, parallelism can be hampered. Conversely, over-dimensioning the concurrency level may give rise to the aforementioned thrashing phenomena caused by excessive data contention—an aspect which has reflections also on the side of reduced energy-efficiency. In this chapter we overview a set of recent techniques aimed at building “application-specific” performance models that can be exploited to dynamically tune the level of concurrency to the best-suited value. Although they share some base concepts while modeling the system performance vs the degree of concurrency, these techniques rely on disparate methods, such as machine learning or analytic methods (or combinations of the two), and achieve different tradeoffs in terms of the relation between the precision of the performance model and the latency for model instantiation. Implications of the different tradeoffs in real-life scenarios are also discussed

    NoCo: ILP-based worst-case contention estimation for mesh real-time manycores

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    Manycores are capable of providing the computational demands required by functionally-advanced critical applications in domains such as automotive and avionics. In manycores a network-on-chip (NoC) provides access to shared caches and memories and hence concentrates most of the contention that tasks suffer, with effects on the worst-case contention delay (WCD) of packets and tasks' WCET. While several proposals minimize the impact of individual NoC parameters on WCD, e.g. mapping and routing, there are strong dependences among these NoC parameters. Hence, finding the optimal NoC configurations requires optimizing all parameters simultaneously, which represents a multidimensional optimization problem. In this paper we propose NoCo, a novel approach that combines ILP and stochastic optimization to find NoC configurations in terms of packet routing, application mapping, and arbitration weight allocation. Our results show that NoCo improves other techniques that optimize a subset of NoC parameters.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under grant TIN2015- 65316-P and the HiPEAC Network of Excellence. It also received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (agreement No. 772773). Carles Hernández is jointly supported by the MINECO and FEDER funds through grant TIN2014-60404-JIN. Jaume Abella has been partially supported by the Spanish Ministry of Economy and Competitiveness under Ramon y Cajal postdoctoral fellowship number RYC-2013-14717. Enrico Mezzetti has been partially supported by the Spanish Ministry of Economy and Competitiveness under Juan de la Cierva-Incorporaci®on postdoctoral fellowship number IJCI-2016-27396.Peer ReviewedPostprint (author's final draft

    Analytical/ML Mixed Approach for Concurrency Regulation in Software Transactional Memory

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    In this article we exploit a combination of analytical and Machine Learning (ML) techniques in order to build a performance model allowing to dynamically tune the level of concurrency of applications based on Software Transactional Memory (STM). Our mixed approach has the advantage of reducing the training time of pure machine learning methods, and avoiding approximation errors typically affecting pure analytical approaches. Hence it allows very fast construction of highly reliable performance models, which can be promptly and effectively exploited for optimizing actual application runs. We also present a real implementation of a concurrency regulation architecture, based on the mixed modeling approach, which has been integrated with the open source Tiny STM package, together with experimental data related to runs of applications taken from the STAMP benchmark suite demonstrating the effectiveness of our proposal. © 2014 IEEE

    The International Drivers of Domestic Airline Mergers in Twenty Nations: Integrating Industrial Organization and International Business

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    The domestic airline merger phenomenon of the late 1980s and early 1990s sparked a great deal of Industrial Organization (IO) literature; yet, that literature neglected non-US domestic mergers and potential for international competitive gains. Using an International Business perspective to complement an IO analysis, I argue that factoring international competitive incentives helps explain domestic airline merger activity. A Cournot model of airline competition illustrates that domestic mergers, via enhanced domestic networks and reduced domestic competition, generate international competitive gains. Further, empirical tests - using a structural-equations approach on panel data covering international city-pair market segments - support domestic mergers improving international competitiveness. ZUSAMMENFASSUNG - (Was die inlĂ€ndischen Fluglinien in 20 LĂ€ndern zur Fusion getrieben hat. Eine integrierte industrieökonomische und betriebswirtschaftliche Analyse des internationalen Wettbewerbs) In der Industrieökonomik hat das PhĂ€nomen von Fusionen inlĂ€ndischer Fluggesellschaften in den spĂ€ten 1980er und frĂŒhen 1990er Jahren viel wissenschaftliche Literatur angeregt. Die einschlĂ€gigen Forschungsarbeiten behandeln jedoch ausschließlich inlĂ€ndische Fusionen in den U.S.A. und lassen damit den Aspekt des internationalen Wettbewerbs außer Acht. In dieser Untersuchung, die die industrieökonomische Analyse um die Perspektive der internationalen Betriebswirtschaft ergĂ€nzt, wird gezeigt, dass die Anreize des internationalen Wettbewerb in das ErklĂ€rungsmodell fĂŒr nationale Fusionen von Fluglinien integriert werden können. Anhand eines Cournot-Modells des Wettbewerbs zwischen Fluggesellschaften kann dargelegt werden, wie Fusionen zu einem erweiterten inlĂ€ndischen Netz an Flugverbindungen und verminderten Konkurrenzdruck im Inland fĂŒhren und so die Position der fusionierten Fluglinie im internationalen Wettbewerb stĂ€rken. Dieses Ergebnis wird von empirischen Tests, die eine Struckturvergleichsmethode fĂŒr Paneldaten ĂŒber einen internationalen StĂ€dtevergleich der Marktsegmente verwenden, gestĂŒtzt.airline-mergers, imperfect-competition, international-determinants

    A Cross-Layer Approach for Minimizing Interference and Latency of Medium Access in Wireless Sensor Networks

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    In low power wireless sensor networks, MAC protocols usually employ periodic sleep/wake schedule to reduce idle listening time. Even though this mechanism is simple and efficient, it results in high end-to-end latency and low throughput. On the other hand, the previously proposed CSMA/CA-based MAC protocols have tried to reduce inter-node interference at the cost of increased latency and lower network capacity. In this paper we propose IAMAC, a CSMA/CA sleep/wake MAC protocol that minimizes inter-node interference, while also reduces per-hop delay through cross-layer interactions with the network layer. Furthermore, we show that IAMAC can be integrated into the SP architecture to perform its inter-layer interactions. Through simulation, we have extensively evaluated the performance of IAMAC in terms of different performance metrics. Simulation results confirm that IAMAC reduces energy consumption per node and leads to higher network lifetime compared to S-MAC and Adaptive S-MAC, while it also provides lower latency than S-MAC. Throughout our evaluations we have considered IAMAC in conjunction with two error recovery methods, i.e., ARQ and Seda. It is shown that using Seda as the error recovery mechanism of IAMAC results in higher throughput and lifetime compared to ARQ.Comment: 17 pages, 16 figure

    Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

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    Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

    Improvement to efficient counter-based broadcast scheme through random assessment delay adaptation for MANETs

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    Flooding, the process in which each node retransmits every uniquely received packet exactly once is the simplest and most commonly used mechanism for broadcasting in mobile ad hoc networks (MANETs). Despite its simplicity, it can result in high redundant retransmission, contention and collision, a phenomenon collectively referred to as broadcast storm problem. To mitigate this problem, several broadcast schemes have been proposed which are commonly divided into two categories; deterministic schemes and probabilistic schemes. Probabilistic methods are quite promising because they can reduce the number of redundant rebroadcast without any control overhead. In this paper, we investigate the performance of our earlier proposed efficient counter-based broadcast scheme by adapting its random assessment delay (RAD) mechanism to network congestion. Simulation results revealed that this simple adaptation achieves superior performance in terms of saved rebroadcast, end-to-end delay and reachability

    Adaptive Transactional Memories: Performance and Energy Consumption Tradeoffs

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    Energy efficiency is becoming a pressing issue, especially in large data centers where it entails, at the same time, a non-negligible management cost, an enhancement of hardware fault probability, and a significant environmental footprint. In this paper, we study how Software Transactional Memories (STM) can provide benefits on both power saving and the overall applications’ execution performance. This is related to the fact that encapsulating shared-data accesses within transactions gives the freedom to the STM middleware to both ensure consistency and reduce the actual data contention, the latter having been shown to affect the overall power needed to complete the application’s execution. We have selected a set of self-adaptive extensions to existing STM middlewares (namely, TinySTM and R-STM) to prove how self-adapting computation can capture the actual degree of parallelism and/or logical contention on shared data in a better way, enhancing even more the intrinsic benefits provided by STM. Of course, this benefit comes at a cost, which is the actual execution time required by the proposed approaches to precisely tune the execution parameters for reducing power consumption and enhancing execution performance. Nevertheless, the results hereby provided show that adaptivity is a strictly necessary requirement to reduce energy consumption in STM systems: Without it, it is not possible to reach any acceptable level of energy efficiency at all
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