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

共 193 条记录，以下是1-10 订阅

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COMPSO: Optimizing Gradient Compression for distributed Training with Second-Order Optimizers 25

COMPSO: Optimizing Gradient Compression for Distributed Trai...

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30th symposium on principles and Practice of Parallel Programming

作者： Sun, Baixi Liu, Weijin Pauloski, J. Gregory Tian, Jiannan Jia, Jinda Wang, Daoce Zhang, Boyuan Zheng, Mingkai Di, Sheng Jin, Sian Zhang, Zhao Yu, Xiaodong Iskra, Kamil A. Beckman, Pete Tan, Guangming Tao, Dingwen Indiana Univ Bloomington IN USA Stevens Inst Technol Hoboken NJ USA Univ Chicago Chicago IL USA Rutgers State Univ New Brunswick NJ USA Argonne Natl Lab Lemont IL USA Temple Univ Philadelphia PA USA Northwestern Univ Evanston IL USA Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798400714436

Second-order optimization methods have been developed to enhance convergence and generalization in deep neural network (DNN) training compared to first-order methods like Stochastic Gradient Descent (SGD). However, these methods face challenges in distributed settings due to high communication overhead. Gradient compression, a technique commonly used to accelerate communication for first-order approaches, often results in low communication reduction ratios, decreased model accuracy, and/or high compression overhead when applied to second-order methods. To address these limitations, we introduce a novel gradient compression method for second-order optimizers called COMPSO. This method effectively reduces communication costs while preserving the advantages of second-order optimization. COMPSO employs stochastic rounding to maintain accuracy and filters out minor gradients to improve compression ratios. Additionally, we develop GPU optimizations to minimize compression overhead and performance modeling to ensure end-to-end performance gains across various systems. Evaluation of COMPSO on different DNN models shows that it achieves a compression ratio of 22.1x, reduces communication time by 14.2x, and improves overall performance by 1.9x, all without any drop in model accuracy.

关键词： Deep learning distributed training second-order optimization K-FAC data compression

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PODC 2023 - proceedings of the 2023 ACM symposium on principles of distributed computing

PODC 2023 - Proceedings of the 2023 ACM Symposium on Princip...

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42nd ACM SIGACT-SIGOPS symposium on principles of distributed computing, PODC 2023

ISBN: (纸本)9798400701214

The proceedings contain 44 papers. The topics discussed include: a near time-optimal population protocol for self-stabilizing leader election on rings with a poly-logarithmic number of states;fast convergence of k-opinion undecided state dynamics in the population protocol model;brief announcement: efficient collaborative tree exploration with breadth-first depth-next;brief announcement: population protocols decide double-exponential thresholds;the complexity of distributed approximation of packing and covering integer linear programs;efficient distributed decomposition and routing algorithms in minor-free networks and their applications;brief announcement: distributed construction of near-optimal compact routing schemes for planar graphs;brief announcement: the Laplacian paradigm in deterministic congested clique;asynchronous wait-free runtime verification and enforcement of linearizability;and asynchronous wait-free runtime verification and enforcement of linearizability.

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Brief Announcement: Massively Parallel Ruling Set Made Deterministic 24

Brief Announcement: Massively Parallel Ruling Set Made Deter...

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43rd ACM SIGACT-SIGOPS symposium on principles of distributed computing, PODC 2024

作者： Giliberti, Jeff Parsaeian, Zahra University of Maryland United States University of Freiburg Germany

ISBN: (纸本)9798400706684

We study the deterministic complexity of the 2-Ruling Set problem in the model of Massively Parallel Computation (MPC) with linear and strongly sublinear local *** MPC: We present a constant-round deterministic algorithm for the 2-Ruling Set problem that matches the randomized round complexity recently settled by Cambus, Kuhn, Pai, and Uitto [DISC'23], and improves upon the deterministic O(log log n)-round algorithm by Pai and Pemmaraju [PODC'22]. Our main ingredient is a simpler analysis of CKPU's algorithm based solely on bounded independence, which makes its efficient derandomization *** MPC: We present a deterministic algorithm that computes a 2-Ruling Set in [EQUATION] rounds deterministically. Notably, this is the first deterministic ruling set algorithm with sublogarithmic round complexity, improving on the O(log δ+log log∗n)-round complexity that stems from the deterministic MIS algorithm of Czumaj, Davies, and Parter [TALG'21]. Our result is based on a simple and fast randomness-efficient construction that achieves the same sparsification as that of the randomized [EQUATION]-round LOCAL algorithm by Kothapalli and Pemmaraju [FSTTCS'12]. © 2024 Association for computing Machinery. All rights reserved.

关键词： distributed computer systems

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Universally Optimal Information Dissemination and Shortest Paths in the HYBRID distributed Model 24

Universally Optimal Information Dissemination and Shortest P...

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43rd ACM SIGACT-SIGOPS symposium on principles of distributed computing, PODC 2024

作者： Chang, Yi-Jun Hecht, Oren Leitersdorf, Dean Schneider, Philipp National University of Singapore Singapore Technion Israel University of Bern Switzerland

ISBN: (纸本)9798400706684

In most modern networks, nodes have access to various modes of communication each with different characteristics. In this work we consider the Hybrid model of distributed computing, introduced recently by Augustine, Hinnenthal, Kuhn, Scheideler, and Schneider (SODA 2020), where nodes have access to two different communication modes: high-bandwidth local communication along the edges of the graph and low-bandwidth all-to-all communication, capturing the non-uniform nature of modern communication networks. It is noteworthy that the Hybrid model in its most general form covers most of the classical distributed models as marginal *** work in Hybrid has focused on showing existentially optimal algorithms, meaning there exists a pathological family of instances on which no algorithm can do better. This neglects the fact that such worst-case instances often do not appear or can be actively avoided in practice. In this work, we focus on the notion of universal optimality, first raised by Garay, Kutten, and Peleg (FOCS 1993). Roughly speaking, a universally optimal algorithm is one that, given any input graph, runs as fast as the best algorithm designed specifically for that *** show the first universally optimal algorithms in Hybrid, up to polylogarithmic factors. We present universally optimal solutions for fundamental tools that solve information dissemination problems, such as broadcasting and unicasting multiple messages in a Hybrid network. Furthermore, we demonstrate these tools for information dissemination can be used to obtain universally optimal solutions for various shortest paths problems in Hybrid. A major conceptual contribution of this work is the conception of a new graph parameter called neighborhood quality that captures the inherent complexity of many fundamental graph problems in the Hybrid model. We also develop new existentially optimal shortest paths algorithms in Hybrid, which are utilized as key subroutines in our universally optimal al

关键词： Overlay networks

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Brief Announcement: distributed Model Checking on Graphs of Bounded Treedepth 24

Brief Announcement: Distributed Model Checking on Graphs of ...

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43rd ACM SIGACT-SIGOPS symposium on principles of distributed computing, PODC 2024

作者： Fomin, Fedor V. Fraigniaud, Pierre Montealegre, Pedro Rapaport, Ivan Todinca, Ioan University of Bergen Bergen Norway Université Paris Cité CNRS Paris France Universidad Adolfo Ibañez Santiago Chile Universidad de Chile Santiago Chile Université d'Orléans Insa Cvl Orléans France

ISBN: (纸本)9798400706684

We establish that every monadic second-order logic (MSO) formula on graphs with bounded treedepth is decidable in a constant number of rounds within the CONGEST model. To our knowledge, this marks the first meta-theorem regarding distributed model-checking. Various optimization problems on graphs are expressible in MSO. Examples include determining whether a graph G has a clique of size k, whether it admits a coloring with k colors, whether it contains a graph H as a subgraph or minor, or whether terminal vertices in G could be connected via vertex-disjoint paths. Our meta-theorem significantly enhances the work of Bousquet et al. [PODC 2022], which was focused on distributed certification of MSO on graphs with bounded treedepth. Moreover, our results can be extended to solving optimization and counting problems expressible in MSO, in graphs of bounded treedepth. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Model checking

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Brief Announcement: On the Limits of Information Spread by Memory-less Agents 24

Brief Announcement: On the Limits of Information Spread by M...

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43rd ACM SIGACT-SIGOPS symposium on principles of distributed computing, PODC 2024

作者： D'Archivio, Niccolò Vacus, Robin Coati Inria d'Université Côte d'Azur Sophia Antipolis France Bocconi Institute for Data Science and Analytics Milan Italy

ISBN: (纸本)9798400706684

We address the self-stabilizing bit-dissemination problem, designed to capture the challenges of spreading information and reaching consensus among entities with minimal cognitive and communication capacities. Specifically, a group of n agents is required to adopt the correct opinion, initially held by a single informed individual, choosing from two possible opinions. In order to make decisions, agents are restricted to observing the opinions of a few randomly sampled agents, and lack the ability to communicate further and to identify the informed individual. Additionally, agents cannot retain any information from one round to the next. According to a recent publication in SODA (2024), a logarithmic convergence time without memory is achievable in the parallel setting (where agents are updated simultaneously), as long as the number of samples is at least(Formula presented.). However, determining the minimal sample size for an efficient protocol to exist remains a challenging open question. As a preliminary step towards an answer, we establish the first lower bound for this problem in the parallel setting. Specifically, we demonstrate that it is impossible for any memory-less protocol with constant sample size, to converge with high probability in less than an almost-linear number of rounds. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： distributed computer systems

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System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models 24

System Optimizations for Enabling Training of Extreme Long S...

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43rd ACM SIGACT-SIGOPS symposium on principles of distributed computing, PODC 2024

作者： Jacobs, Sam Ade Tanaka, Masahiro Zhang, Chengming Zhang, Minjia Aminadabi, Reza Yazdani Song, Shuaiwen Leon Rajbhandari, Samyam He, Yuxiong Microsoft Inc Redmond United States

ISBN: (纸本)9798400706684

Computation in a typical Transformer-based large language model (LLM) can be characterized by batch size, hidden dimension, number of layers, and sequence length. Until now, system works for accelerating LLM training have focused on the first three dimensions: data parallelism for batch size, tensor parallelism for hidden size, and pipeline parallelism for model depth or layers. These widely studied forms of parallelism are not targeted or optimized for long sequence Transformer models. Given practical application needs for long sequence LLM, renewed attentions are being drawn to sequence parallelism. However, existing works in sequence parallelism are constrained by memory-communication inefficiency, limiting their scalability to long sequence large models. In this work, we introduce Ulysses, a novel, portable, and effective methodology for enabling highly efficient and scalable LLM training with extremely long sequence length. Ulysses at its core partitions input data along the sequence dimension and employs an efficient all-to-all collective communication for attention computation. Theoretical communication analysis shows that, whereas other methods incur communication overhead as sequence length increases, Ulysses maintains constant communication volume when sequence length and compute devices are increased proportionally. Furthermore, experimental evaluations show that Ulysses scales to more than 1 million context length and trains 2.5x faster with 4x longer sequence length than the existing method SOTA baseline. © 2024 Association for computing Machinery. All rights reserved.

关键词： AI/ML long context LLM HPC distributed training

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On the Power of Quantum distributed Proofs 24

On the Power of Quantum Distributed Proofs

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43rd ACM SIGACT-SIGOPS symposium on principles of distributed computing, PODC 2024

作者： Hasegawa, Atsuya Kundu, Srijita Nishimura, Harumichi The University of Tokyo Japan The Institute for Quantum Computing University of Waterloo Canada Nagoya University Japan

ISBN: (纸本)9798400706684

Quantum nondeterministic distributed computing was recently introduced as dQMA (distributed quantum Merlin-Arthur) protocols by Fraigniaud, Le Gall, Nishimura and Paz (ITCS 2021). In dQMA protocols, with the help of quantum proofs and local communication, nodes on a network verify some global property of the network. Fraigniaud et al. showed that, when the network size is small, there exists an exponential separation in proof size between distributed classical and quantum verification protocols, for the equality problem, where the verifiers check if all the data owned by a subset of them are identical. In this paper, we further investigate and characterize the power of the dQMA protocols for various decision ***, we give a more efficient dQMA protocol for the equality problem with a simpler analysis. This is done by adding a symmetrization step on each node and exploiting properties of the permutation test, which is a generalization of the SWAP test. We also show a quantum advantage for the equality problem on path networks still persists even when the network size is large, by considering "relay points"between extreme ***, we show that even in a general network, there exist efficient dQMA protocols for the ranking verification problem, the Hamming distance problem, and more problems that derive from efficient quantum one-way communication protocols. Third, in a line network, we construct an efficient dQMA protocol for any problem that has an efficient two-party QMA communication ***, we obtain the first lower bounds on the proof and communication cost of dQMA protocols. To prove a lower bound on the equality problem, we show any dQMA protocol with an entangled proof between nodes can be simulated with a dQMA protocol with a separable proof between nodes by using a QMA communication-complete problem introduced by Raz and Shpilka (CCC 2004). © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Hamming distance

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Even-Cycle Detection in the Randomized and Quantum CONGEST Model 24

Even-Cycle Detection in the Randomized and Quantum CONGEST M...

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43rd ACM SIGACT-SIGOPS symposium on principles of distributed computing, PODC 2024

作者： Fraigniaud, Pierre Luce, Maël Magniez, Frederic Todinca, Ioan Université Paris Cité CNRS IRIF Paris France Université d'Orléans INSA-Centre Val de Loire LIFO Orléans France

ISBN: (纸本)9798400706684

We show that, for every k ≥ 2, C2k-freeness can be decided in O(n1 - 1/k) rounds in the CONGEST model by a randomized Monte-Carlo distributed algorithm with one-sided error probability 1/3. This matches the best round-complexities of previously known algorithms for k ϵ {2, 3, 4, 5} by Drucker et al. [PODC'14] and Censor-Hillel et al. [DISC'20], but improves the complexities of the known algorithms for k > 5 by Eden et al. [DISC'19], which were essentially of the form Õ (n1 - 2/k2). Our algorithm uses colored BFS-explorations with threshold, but with an original global approach that enables to overcome a recent impossibility result by Fraigniaud et al. [SIROCCO'23] about using colored BFS-exploration with local threshold for detecting *** also show how to quantize our algorithm for achieving a round-complexity Õ(n1/2 - 1/2k) in the quantum setting for deciding C2k freeness. Furthermore, this allows us to improve the known quantum complexities of the simpler problem of detecting cycles of length at most 2k by van Apeldoorn and de Vos [PODC'22]. Our quantization is in two steps. first, the congestion of our randomized algorithm is reduced, to the cost of reducing its success probability too. Second, the success probability is boosted using a new quantum framework derived from sequential algorithms, namely Monte-Carlo quantum amplification. © 2024 Association for computing Machinery. All rights reserved.

关键词： distributed computer systems

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Completing the Node-Averaged Complexity Landscape of LCLs on Trees 24

Completing the Node-Averaged Complexity Landscape of LCLs on...

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43rd ACM SIGACT-SIGOPS symposium on principles of distributed computing, PODC 2024

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

ISBN: (纸本)9798400706684

The node-averaged complexity of a problem captures the number of rounds nodes of a graph have to spend on average to solve the problem in the LOCAL model. A challenging line of research with regards to this new complexity measure is to understand the complexity landscape of locally checkable labelings (LCLs) on families of bounded-degree graphs. Particularly interesting in this context is the family of bounded-degree trees as there, for the worst-case complexity, we know a complete characterization of the possible complexities and structures of LCL problems. A first step for the node-averaged complexity case has been achieved recently [DISC '23], where the authors in particular showed that in bounded-degree trees, there is a large complexity gap: There are no LCL problems with a deterministic node-averaged complexity between ω(log∗n) and no(1). For randomized algorithms, they even showed that the node-averaged complexity is either O(1) or nω(1). In this work we fill in the remaining gaps and give a complete description of the node-averaged complexity landscape of LCLs on bounded-degree trees. Our contributions are threefold.•On bounded-degree trees, there is no LCL with a node-averaged complexity between ω(1) and (log∗n)o(1).•For any constants 0 1 2 ≤ 1 and ϵ > 0, there exists a constant c and an LCL problem with node-averaged complexity between ω((log∗n)c) and O((log∗n)c+ϵ).•For any constants 0 0, there exists an LCL problem with node-averaged complexity Φ(nx) for some x ϵ [α, α + ϵ]. © 2024 Association for computing Machinery. All rights reserved.

关键词： locally checkable labelings node-averaged complexity distributed complexity theory

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