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Can distributed Uniformity Testing Be Local? 19

Can Distributed Uniformity Testing Be Local?

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38th acm symposium on principles of distributed computing (PODC)

作者： Meir, Uri Minzer, Dor Oshman, Rotem Tel Aviv Univ Tel Aviv Israel Inst Adv Study Princeton NJ 08540 USA

ISBN: (纸本)9781450362177

In the distributed uniformity testing problem, k servers draw samples from some unknown distribution, and the goal is to determine whether the unknown distribution is uniform or whether it is epsilon-far from uniform, where epsilon is a proximity parameter. Each server decides whether to accept or reject, and these decisions are sent to a referee, who makes a final decision based on the servers' local decisions. Uniformity testing is a particularly useful building-block, because it is complete for the problem of testing identity to any fixed distribution. It was recently shown that distributing the task of uniformity testing allows each server to draw fewer samples than are needed in the centralized case, but so far the number of samples required for distributed uniformity testing has not been well understood. In this paper we settle this question, and also investigate the cost of using local decision rules, such as rejecting iff at least one server wants to reject (the usual decision rule used in local distributed decision). To answer these questions, we develop a new Fourier-based technique for proving lower bounds on the sample complexity of distribution testing, which lends itself particularly well to the distributed case. Using our technique, we tightly characterize the number of samples required for uniformity testing when the referee can apply any decision function to the servers' local decisions. We also show that if the network rejects whenever one server wants to reject, then the cost of uniformity testing is much higher, and in fact we do not gain compared to the centralized case unless the number of servers is exponential in Omega(1/epsilon). Finally, we apply our lower bound technique to the case where the referee applies a threshold decision rule, and also generalize a lower bound from [1] for learning an unknown input distribution.

关键词： distributed computing uniformity testing boolean analysis

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Code, Connect, Create: The 3C Professional Development Model to Support Computational Thinking Infusion 20

Code, Connect, Create: The 3C Professional Development Model...

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51st acm SIGCSE Technical symposium on Computer Science Education (SIGCSE)

作者： Jocius, Robin Joshi, Deepti Dong, Yihuan Robinson, Richard Catete, Veronica Barnes, Tiffany Albert, Jennifer Andrews, Ashley Lytle, Nicholas Citadel Zucker Family Sch Educ Charleston SC 29409 USA Citadel Sch Sci & Math Charleston SC USA North Carolina State Univ Dept Comp Sci Raleigh NC USA

ISBN: (纸本)9781450367936

Despite the increasing attention to infusing CT into middle and high school content area classrooms, there is a lack of information about the most effective practices and models to support teachers in their efforts to integrate disciplinary content and CT principles. To address this need, this paper proposes the Code, Connect and Create (3C) professional development (PD) model, which was designed to support middle and high school content area teachers in infusing computational thinking into their classrooms. To evaluate the model, we analyzed quantitative and qualitative data collected from Infusing computing PD workshops designed for in-service science, math, English language arts, and social studies teachers located in two Southeastern states. Drawing on findings from our analysis of teacher-created learning segments, surveys, and interviews, we argue that the 3C professional development model supported shifts in teacher understandings of the role of computational thinking in content area classrooms, as well as their self-efficacy and beliefs regarding CT integration into disciplinary content. We conclude by offering implications for the use of this model to increase teacher and student access to computational thinking practices in middle and high school classrooms.

关键词： Computational thinking Professional development K-12 computing Teacher education

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PODC'12 - proceedings of the 2012 acm symposium on principles of distributed computing

PODC'12 - Proceedings of the 2012 ACM Symposium on Principle...

引用

2012 acm symposium on principles of distributed computing, PODC'12

ISBN: (纸本)9781450314503

The proceedings contain 61 papers. The topics discussed include: random walks which prefer unvisited edges: exploring high girth even degree expanders in linear time;coalescing random walks and voting on graphs;the cost of fault tolerance in multi-party communication complexity;the communication complexity of distributed task allocation;collaborative search on the plane without communication;universal constructions that ensure disjoint-access parallelism and wait-freedom;generalized lattice agreement;distributed selfish load balancing with weights and speeds;making evildoers pay: resource-competitive broadcast in sensor networks;distributed public key schemes secure against continual leakage;weak models of distributed computing, with connections to modal logic;aggregation in dynamic networks;leader election in shared spectrum radio networks;and breaking the O(nm) bit barrier, secure multiparty computation with a static adversary.

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Elle: Finding Isolation Violations in Real-World Databases 21

Elle: Finding Isolation Violations in Real-World Databases

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proceedings of the 2021 acm symposium on principles of distributed computing

作者： Kyle Kingsbury Jepsen Cincinnati OH USA

ISBN: (纸本)9781450385480

distributed databases are supposed to manipulate data safely, ensuring properties like serializability even when nodes or networks fail. Unfortunately, they often don't. We found consistency violations in 26 systems over the last eight years, ranging from stale reads to catastrophic data loss. We review the Jepsen testing library [1], which combines automated deployment, fault injection, and property-based testing techniques to uncover safety violations and performance characteristics in a broad range of distributed *** then present Elle [2], a new library for analyzing Jepsen histories and finding consistency violations in linear time. We build on Adya's formalism [3] of transactional anomalies as cycles in a dependency graph. Unfortunately these graphs are invisible to clients. However, by carefully choosing the datatypes and operations we submit to a database, we can generate histories whose client-observable structure constrains the dependency graph of every Adya history the database could have *** can identify every anomaly in the Adya formalism (except for predicates) in linear time, allowing us to validate a broad range of isolation properties up to strict serializability. It automatically finds minimal counterexamples, helping an engineer see exactly which transactions were incompatible with (e.g.) snapshot isolation. It is sound, but since some information may be missing from observed histories, not complete. We use Jepsen and Elle to find consistency errors in a broad variety of datastores, including PostgreSQL, Dgraph, and Redis-Raft.

关键词： consistency transactions testing distributed systems

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A deterministic almost-tight distributed algorithm for approximating single-source shortest paths

A deterministic almost-tight distributed algorithm for appro...

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作者： Henzinger, Monika Krinninger, Sebastian Nanongkai, Danupon Faculty of Computer Science University of Vienna Vienna1090 Austria Department of Computer Sciences University of Salzburg Salzburg5020 Austria Department of Theoretical Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden

We present a deterministic (1 + o(1))-approximation (n1/2+o(1) + D1+o(1))-time algorithm for solving the single-source shortest paths problem on distributed weighted networks (the CONGEST model);here n is the number of nodes in the network, D is its (hop) diameter, and edge weights are positive integers from 1 to poly(n). This is the first nontrivial deterministic algorithm for this problem. It also improves (i) the running time of the randomized (1 + o(1))-approximation Õ(√nD1/4 + D)-time algorithm of Nanongkai [in proceedings of STOC, 2014, pp. 565-573] by a factor of as large as n1/8, and (ii) the O(∊-1 log ∊ -1)-approximation factor of Lenzen and Patt-Shamir's Õ(n1/2+∊ + D)-time algorithm [in proceedings of STOC, 2013, pp. 381-390] within the same running time. (Throughout, we use Õ(·) to hide polylogarithmic factors in n.) Our running time matches the known time lower bound of Ω (√n/log n + D) [M. Elkin, SIAM J. Comput., 36 (2006), pp. 433-456], thus essentially settling the status of this problem which was raised at least a decade ago [M. Elkin, SIGACT News, 35 (2004), pp. 40-57]. It also implies a (2 + o(1))-approximation (n1/2+o(1) + D1+o(1))- time algorithm for approximating a network's weighted diameter which almost matches the lower bound by Holzer and Pinsker [in proceedings of OPODIS, 2015, Schloss Dagstuhl. Leibniz-Zent. Inform., Wadern, Germany, 2016, 6]. In achieving this result, we develop two techniques which might be of independent interest and useful in other settings: (i) a deterministic process that replaces the ``hitting set argument"" commonly used for shortest paths computation in various settings, and (ii) a simple, deterministic construction of an (no(1), o(1))-hop set of size n1+o(1). We combine these techniques with many distributed algorithmic techniques, some of which are from problems that are not directly related to shortest paths, e.g., ruling sets [A. V. Goldberg, S. A. Plotkin, and G. E. Shannon, SIAM J. Discrete Math., 1 (1988),

关键词： Approximation algorithms

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FFT-based Gradient Sparsification for the distributed Training of Deep Neural Networks 20

FFT-based Gradient Sparsification for the Distributed Traini...

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29th International Conference on High-Performance Parallel and distributed computing (HPDC)

作者： Wang, Linnan Wu, Wei Zhang, Junyu Liu, Hang Bosilca, George Herlihy, Maurice Fonseca, Rodrigo Brown Univ Providence RI 02912 USA Los Alamos Natl Lab Los Alamos NM 87545 USA Univ Minnesota Minneapolis MN USA Stevens Inst Technol Hoboken NJ 07030 USA Univ Tennessee Knoxville TN USA

ISBN: (纸本)9781450370523

The performance and efficiency of distributed training of Deep Neural Networks (DNN) highly depend on the performance of gradient averaging among participating processes, a step bound by communication costs. There are two major approaches to reduce communication overhead: overlap communications with computations (lossless), or reduce communications (lossy). The lossless solution works well for linear neural architectures, e.g. VGG, AlexNet, but more recent networks such as ResNet and Inception limit the opportunity for such overlapping. Therefore, approaches that reduce the amount of data ( lossy) become more suitable. In this paper, we present a novel, explainable lossy method that sparsifies gradients in the frequency domain, in addition to a new range-based float point representation to quantize and further compress gradients. These dynamic techniques strike a balance between compression ratio, accuracy, and computational overhead, and are optimized to maximize performance in heterogeneous environments. Unlike existing works that strive for a higher compression ratio, we stress the robustness of our methods, and provide guidance to recover accuracy from failures. To achieve this, we prove how the FFT sparsification affects the convergence and accuracy, and show that our method is guaranteed to converge using a diminishing. in training. Reducing theta can also be used to recover accuracy from the failure. Compared to STOA lossy methods, e.g., QSGD, TernGrad, and Top-k sparsification, our approach incurs less approximation error, thereby better in both the wall-time and accuracy. On an 8 GPUs, InfiniBand interconnected cluster, our techniques effectively accelerate AlexNet training up to 2.26x to the baseline of no compression, and 1.31x to QSGD, 1.25x to Terngrad and 1.47x to Top-K sparsification.

关键词： Neural Networks Machine Learning FFT Gradient Compression Loosy Gradients

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An Automatic Speedup Theorem for distributed Problems 19

An Automatic Speedup Theorem for Distributed Problems

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38th acm symposium on principles of distributed computing (PODC)

作者： Brandt, Sebastian Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781450362177

Recently, Brandt et al. [STOC'16] proved a lower bound for the distributed Lovasz Local Lemma, which has been conjectured to be tight for sufficiently relaxed LLL criteria by Chang and Pettie [FOCS'17]. At the heart of their result lies a speedup technique that, for graphs of girth at least 2t + 2, transforms any t -round algorithm for one specific LLL problem into a (t - 1)-round algorithm for the same problem. We substantially improve on this technique by showing that such a speedup exists for any locally checkable problem Pi, with the difference that the problem Pi(1) the inferred (t - 1)-round algorithm solves is not (necessarily) the same problem as Pi. Our speedup is automatic in the sense that there is a fixed procedure that transforms a description for Pi into a description for Pi(1) and reversible in the sense that any (t - 1)-round algorithm for Pi(1) can be transformed into a t-round algorithm for Pi. In particular, for any locally checkable problem. with exact deterministic time complexity T (n, Delta) <= t on graphs with n nodes, maximum node degree Delta, and girth at least 2t + 2, there is a sequence of problems Pi(1), Pi(2),... with time complexities T (n, Delta) - 1, T (n, Delta) - 2,..., that can be inferred from Pi. As a first application of our generalized speedup, we solve a longstanding open problem of Naor and Stockmeyer [STOC'93]: we show that weak 2-coloring in odd-degree graphs cannot be solved in o(log*Delta) rounds, thereby providing a matching lower bound to their upper bound.

关键词： distributed graph algorithms locally checkable problems weak coloring

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Taming Unbalanced Training Workloads in Deep Learning with Partial Collective Operations 20

Taming Unbalanced Training Workloads in Deep Learning with P...

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25th acm SIGPLAN symposium on principles and Practice of Parallel Programming (PPoPP)

作者： Li, Shigang Ben-Nun, Tal Di Girolamo, Salvatore Alistarh, Dan Hoefler, Torsten Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland IST Austria Klosterneuburg Austria

ISBN: (纸本)9781450368186

Load imbalance pervasively exists in distributed deep learning training systems, either caused by the inherent imbalance in learned tasks or by the system itself. Traditional synchronous Stochastic Gradient Descent (SGD) achieves good accuracy for a wide variety of tasks, but relies on global synchronization to accumulate the gradients at every training step. In this paper, we propose eager-SGD, which relaxes the global synchronization for decentralized accumulation. To implement eager-SGD, we propose to use two partial collectives: solo and majority. With solo allreduce, the faster processes contribute their gradients eagerly without waiting for the slower processes, whereas with majority allreduce, at least half of the participants must contribute gradients before continuing, all without using a central parameter server. We theoretically prove the convergence of the algorithms and describe the partial collectives in detail. Experiments are conducted on a variety of neural networks and datasets. The results on load-imbalanced environments show that eager-SGD achieves 2.64 x speedup (ResNet-50 on ImageNet) over the asynchronous centralized SGD, and achieves 1.29 x speedup (ResNet-50 on ImageNet) and 1.27x speedup (LSTM on UCF101) over the state-of-the-art synchronous decentralized SGDs, without losing accuracy.

关键词： stochastic gradient descent distributed deep learning eager-SGD workload imbalance collective operations

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Towards a Library for Deterministic Failure Testing of distributed Systems 10

Towards a Library for Deterministic Failure Testing of Distr...

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10th acm symposium on Cloud computing (SoCC)

作者： Balalaie, Armin Jones, James A. Univ Calif Irvine Irvine CA 92717 USA

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The Consensus Number of a Cryptocurrency 19

The Consensus Number of a Cryptocurrency

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38th acm symposium on principles of distributed computing (PODC)

作者： Guerraoui, Rachid Kuznetsov, Petr Monti, Matteo Pavlovic, Matej Seredinschi, Dragos-Adrian Ecole Polytech Fed Lausanne Lausanne Switzerland Telecom Paris IP Paris LTCI Paris France

ISBN: (纸本)9781450362177

Many blockchain-based algorithms, such as Bitcoin, implement a decentralized asset transfer system, often referred to as a cryptocurrency. As stated in the original paper by Nakamoto, at the heart of these systems lies the problem of preventing double-spending;this is usually solved by achieving consensus on the order of transfers among the participants. By treating the asset transfer problem as a concurrent object and determining its consensus number, we show that consensus is not necessary to prevent double-spending. We first consider the problem as defined by Nakamoto, where only a single process-the account owner-can withdraw from each account. Safety and liveness need to be ensured for correct account owners, whereas misbehaving account owners might be unable to perform transfers. We show that the consensus number of an asset transfer object is 1. We then consider a more general k-shared asset transfer object where up to k processes can atomically withdraw from the same account, and show that this object has consensus number k. We first establish these these results in the context of shared memory with benign faults, in order to properly understand the level of difficulty of the asset transfer problem. Then, we translate our result in the more practically relevant message passing setting with Byzantine players. We describe an asynchronous Byzantine fault-tolerant asset transfer implementation that is both simpler and more efficient than state-of-the-art consensus-based solutions. Our results are applicable to both the permissioned (private) and permissionless (public) setting, as normally their differentiation is hidden by the abstractions on top of which our algorithms are based.

关键词： distributed computing distributed asset transfer blockchain consensus

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