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检索条件"任意字段=Proceedings of the twenty-third annual ACM symposium on Principles of distributed computing"

共 781 条记录，以下是31-40 订阅

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PODC 2022 - proceedings of the 2022 acm symposium on principles of distributed computing

PODC 2022 - Proceedings of the 2022 ACM Symposium on Princip...

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41st acm symposium on principles of distributed computing, PODC 2022

ISBN: (纸本)9781450392624

The proceedings contain 59 papers. The topics discussed include: node and edge averaged complexities of local graph problems;overcoming congestion in distributed coloring;the landscape of distributed complexities on trees and beyond;brief announcement: on polynomial-time local decision;brief announcement: distributed MST computation in the sleeping model: awake-optimal algorithms and lower bounds;brief announcement: broadcasting time in dynamic rooted trees is linear;a recursive early-stopping phase king protocol;optimal synchronous approximate agreement with asynchronous fallback;perfectly-secure synchronous MPC with asynchronous fallback guarantees;brief announcement: asynchronous randomness and consensus without trusted setup;and brief announcement: deterministic consensus and checkpointing with crashes: time and communication efficiency.

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A Cloud-Scale Characterization of Remote Procedure Calls 23

A Cloud-Scale Characterization of Remote Procedure Calls

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29th acm symposium on Operating Systems principles (SOSP)

作者： Seemakhupt, Korakit Stephens, Brent E. Khan, Samira Liu, Sihang Wassel, Hassan Yeganeh, Soheil Hassas Snoeren, Alex C. Krishnamurthy, Arvind Culler, David E. Levy, Henry M. Univ Virginia Charlottesville VA 22903 USA Google Mountain View CA USA Univ Utah Salt Lake City UT 84112 USA Univ Waterloo Waterloo ON N2L 3G1 Canada Univ Calif San Diego La Jolla CA 92093 USA Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9798400702297

The global scale and challenging requirements of modern cloud applications have led to the development of complex, widely distributed, service-oriented applications. One enabler of such applications is the remote procedure call (RPC), which provides location-independent communication and hides the myriad of cloud communication complexities and requirements within the RPC stack. Understanding RPCs is thus one key to understanding the behavior of cloud applications. While there have been numerous studies of RPCs in distributed systems, as well as attempts to optimize RPC overheads with both software and hardware, there is still a lack of knowledge about the characteristics of RPCs "in the wild" in the modern cloud environment. To address this gap, we present, to the best of our knowledge, the first large-scale fleet-wide study of RPCs. Our study is conducted at Google, where we measured the infrastructure supporting Google's user-facing, billion-user web services, such as Google Search, Gmail, Maps, and YouTube, and the information and data management systems that support them. To carry out the study, we examined over 10,000 different RPC methods sampled from over one billion traces, along with statistics collected every 30 minutes over a period of nearly two years. Among other things, we consider the volume, throughput and growth rate of RPCs in the datacenter, the latency of RPCs and their components (the "RPC latency tax"), and the structure of RPC call chains. Our analysis shows that the characteristics, scope and complexity of RPCs at hyperscale differ significantly from the assumptions made in prior research. Overall, our work provides new insights into RPC usage and characteristics at the largest scale and motivates further research on optimizing the diverse behavior of this crucial communication mechanism.

关键词： Remote procedure call Cloud computing distributed computing Communications systems

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distributed Edge Coloring in Time Polylogarithmic in " 41

Distributed Edge Coloring in Time Polylogarithmic in "

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41st acm symposium on principles of distributed computing, PODC 2022

作者： Balliu, Alkida Brandt, Sebastian Kuhn, Fabian Olivetti, Dennis Gran Sasso Science Institute L'Aquila Italy Cispa Helmholtz Center for Information Security Saarbrücken Germany University of Freiburg Freiburg Germany

ISBN: (纸本)9781450392624

We provide new deterministic algorithms for the edge coloring problem, which is one of the classic and highly studied distributed local symmetry breaking problems. As our main result, we show that a (2Δ- 1)-edge coloring can be computed in time poly log Δ+ O(log∗n) in the LOCAL model. This improves a result of Balliu, Kuhn, and Olivetti [PODC '20], who gave an algorithm with a quasi-polylogarithmic dependency on ". We further show that in the CONGEST model, an (8 + ) "-edge coloring can be computed in poly log Δ+ O(log∗n) rounds. The best previous O( ")-edge coloring algorithm that can be implemented in the CONGEST model is by Barenboim and Elkin [PODC '11] and it computes a 2O(1/) "- edge coloring in time O( "+ log∗n) for any ĝ (0, 1]. © 2022 acm.

关键词： Coloring

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Deterministic Near-Optimal distributed Listing of Cliques 22

Deterministic Near-Optimal Distributed Listing of Cliques

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41st acm symposium on principles of distributed computing, PODC 2022

作者： Censor-Hillel, Keren Leitersdorf, Dean Vulakh, David Technion - Israel Institute of Technology Haifa Israel Massachusetts Institute of Technology CambridgeMA United States

ISBN: (纸本)9781450392624

The importance of classifying connections in large graphs has been the motivation for a rich line of work on distributed subgraph finding that has led to exciting recent breakthroughs. A crucial aspect that remained open was whether deterministic algorithms can be as efficient as their randomized counterparts, where the latter are known to be tight up to polylogarithmic factors. We give deterministic distributed algorithms for listing cliques of size p in n1 - 2/p + o(1) rounds in the Congest model. For triangles, our n1/3+o(1) round complexity improves upon the previous state of the art of n2/3+o(1) rounds [Chang and Saranurak, FOCS 2020]. For cliques of size p ≥ 4, ours are the first non-trivial deterministic distributed algorithms. Given known lower bounds, for all values p ≥ 3 our algorithms are tight up to a no(1) subpolynomial factor, which comes from the deterministic routing procedure we use. © 2022 acm.

关键词： congest distributed graph algorithms cliques

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Poster: distributed Inference for Multiple DNN Models in IoT Environments 23

Poster: Distributed Inference for Multiple DNN Models in IoT...

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23rd International symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile computing (MobiHoc)

作者： Jin, YoungHwan Park, HyungBin Lee, SuKyoung Yonsei Univ Seoul South Korea

ISBN: (纸本)9781450391658

Edge computing (EC) has been promising in providing support for Deep Neural Network (DNN) applications in IoT environments. However, resources in each edge are limited as response time increases. To reduce the latency, we propose to distribute the computation of multiple DNN models to nearby IoT devices. In particular, we propose a piece-wise multilevel partitioning and scheduling algorithm to improve the completion time of DNN inference.

关键词： IoT Multiple DNNs Piece-wise partitioning Offloading

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SYNTHESIS: A Semi-Asynchronous Path-Integrated Stochastic Gradient Method for distributed Learning in computing Clusters 22

SYNTHESIS: A Semi-Asynchronous Path-Integrated Stochastic Gr...

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23rd International symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile computing (MobiHoc)

作者： Liu, Zhuqing Zhang, Xin Liu, Jia Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA Iowa State Univ Dept Stat Ames IA USA

ISBN: (纸本)9781450391658

To increase the training speed of distributed learning, recent years have witnessed a significant amount of interest in developing both synchronous and asynchronous distributed stochastic variancereduced optimization methods. However, all existing synchronous and asynchronous distributed training algorithms suffer from various limitations in either convergence speed or implementation complexity. This motivates us to propose an algorithm called SYNTHESIS (semi-asynchronous path-integrated stochastic gradient search), which leverages the special structure of the variance-reduction framework to overcome the limitations of both synchronous and asynchronous distributed learning algorithms, while retaining their salient features. We consider two implementations of SYNTHESIS under distributed and shared memory architectures. We show that our SYNTHESIS algorithms have O(root N epsilon(-2) (Delta+ 1) +N) and O(root N epsilon(- 2) (Delta + 1)d +N) computational complexities for achieving an epsilon-stationary point in non-convex learning under distributed and shared memory architectures, respectively, where N denotes the total number of training samples and Delta represents the maximum delay of the workers. Moreover, we investigate the generalization performance of SYNTHESIS by establishing algorithmic stability bounds for quadratic strongly convex and non-convex optimization. We further conduct extensive numerical experiments to verify our theoretical findings.

关键词： Machine learning asynchronous distributed optimization

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PODC 2021 - proceedings of the 2021 acm symposium on principles of distributed computing

PODC 2021 - Proceedings of the 2021 ACM Symposium on Princip...

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40th acm SIGACT-SIGOPS symposium on principles of distributed computing, PODC 2021

ISBN: (纸本)9781450385480

The proceedings contain 60 papers. The topics discussed include: separating bounded and unbounded asynchrony for autonomous robots: point convergence with limited visibility;on implementing stabilizing leader election with weak assumptions on network dynamics;time-optimal self-stabilizing leader election in population protocols;lower bounds on the state complexity of population protocols;comparison dynamics in population protocols;diversity, fairness and sustainability in population protocols;brief announcement: a time and space optimal stable population protocol solving exact majority;a thin self-stabilizing asynchronous unison algorithm with applications to fault tolerant biological networks;and decision power of weak asynchronous models of distributed computing.

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Decision Power of Weak Asynchronous Models of distributed computing 21

Decision Power of Weak Asynchronous Models of Distributed Co...

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40th acm SIGACT-SIGOPS symposium on principles of distributed computing (PODC)

作者： Czerner, Philipp Guttenberg, Roland Helfrich, Martin Esparza, Javier Tech Univ Munich Munich Germany

ISBN: (纸本)9781450385480

Esparza and Reiter have recently conducted a systematic comparative study of models of distributed computing consisting of a network of identical finite-state automata that cooperate to decide if the underlying graph of the network satisfies a given property. The study classifies models according to four criteria, and shows that twenty-four initially possible combinations collapse into seven equivalence classes with respect to their decision power, i.e. the properties that the automata of each class can decide. However, Esparza and Reiter only show (proper) inclusions between the classes, and so do not characterise their decision power. In this paper we do so for labelling properties, i.e. properties that depend only on the labels of the nodes, but not on the structure of the graph. In particular, majority (whether more nodes carry label a than b) is a labelling property. Our results show that only one of the seven equivalence classes identified by Esparza and Reiter can decide majority for arbitrary networks. We then study the expressive power of the classes on bounded-degree networks, and show that three classes can. In particular, we present an algorithm for majority that works for all bounded-degree networks under adversarial schedulers, i.e. even if the scheduler must only satisfy that every node makes a move infinitely often, and prove that no such algorithm can work for arbitrary networks.

关键词： distributed computing graph automata weak asynchronous models labelling properties communication graphs decision power

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distributed Randomness from Approximate Agreement 36

Distributed Randomness from Approximate Agreement

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36th International symposium on distributed computing, DISC 2022

作者： Freitas, Luciano Kuznetsov, Petr Tonkikh, Andrei LTCI Télécom Paris Institut Polytechnique de Paris France

ISBN: (纸本)9783959772556

Randomisation is a critical tool in designing distributed systems. The common coin primitive, enabling the system members to agree on an unpredictable random number, has proven to be particularly useful. We observe, however, that it is impossible to implement a truly random common coin protocol in a fault-prone asynchronous system. To circumvent this impossibility, we introduce two relaxations of the perfect common coin: (1) approximate common coin generating random numbers that are close to each other;and (2) Monte Carlo common coin generating a common random number with an arbitrarily small, but non-zero, probability of failure. Building atop the approximate agreement primitive, we obtain efficient asynchronous implementations of the two abstractions, tolerating up to one third of Byzantine processes. Our protocols do not assume trusted setup or public key infrastructure and converge to the perfect coin exponentially fast in the protocol running time. By plugging one of our protocols for Monte Carlo common coin in a well-known consensus algorithm, we manage to get a binary Byzantine agreement protocol with O(n3 log n) communication complexity, resilient against an adaptive adversary, and tolerating the optimal number f 4). We also show how the approximate common coin, combined with a variant of Gray code, can be used to solve an interesting problem of Intersecting Random Subsets, which we introduce in this paper. © Luciano Freitas, Petr Kuznetsov, and Andrei Tonkikh.

关键词： Monte Carlo methods

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On Scheduling Ring-All-Reduce Learning Jobs in Multi-Tenant GPU Clusters with Communication Contention 22

On Scheduling Ring-All-Reduce Learning Jobs in Multi-Tenant ...

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23rd International symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile computing (MobiHoc)

作者： Yu, Menglu Ji, Bo Rajan, Hridesh Liu, Jia Iowa State Univ Dept Comp Sci Ames IA 50011 USA Virginia Tech Dept Comp Sci Blacksburg VA USA Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA

ISBN: (纸本)9781450391658

Powered by advances in deep learning (DL) techniques, machine learning and artificial intelligence have achieved astonishing successes. However, the rapidly growing needs for DL also led to communication- and resource-intensive distributed training jobs for large-scale DL training, which are typically deployed over GPU clusters. To sustain the ever-increasing demand for DL training, the so-called "ring-all-reduce" (RAR) technologies have recently emerged as a favorable computing architecture to efficiently process network communication and computation load in GPU clusters. The most salient feature of RAR is that it removes the need for dedicated parameter servers, thus alleviating the potential communication bottleneck. However, when multiple RAR-based DL training jobs are deployed over GPU clusters, communication bottlenecks could still occur due to contentions between DL training jobs. So far, there remains a lack of theoretical understanding on how to design contention-aware resource scheduling algorithms for RAR-based DL training jobs, which motivates us to fill this gap in this work. Our main contributions are three-fold: i) We develop a new analytical model that characterizes both communication overhead related to the worker distribution of the job and communication contention related to the co-location of different jobs;ii) Based on the proposed analytical model, we formulate the problem as a non-convex integer program to minimize the makespan of all RAR-based DL training jobs. To address the unique structure in this problem that is not amenable for optimization algorithm design, we reformulate the problem into an integer linear program that enables provable approximation algorithm design called SJF-BCO (Smallest Job First with Balanced Contention and Overhead);and iii) We conduct extensive experiments to show the superiority of SJF-BCO over existing schedulers. Collectively, our results contribute to the state-of-the-art of distributed GPU system optimizatio

关键词： distributed machine learning systems communication contention resource scheduling algorithm design

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