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检索条件"任意字段=Proceedings of the 2016 ACM Symposium on Principles of Distributed Computing"

共 1394 条记录，以下是11-20 订阅

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Helios: Efficient distributed Dynamic Graph Sampling for Online GNN Inference 25

Helios: Efficient Distributed Dynamic Graph Sampling for Onl...

引用

30th symposium on principles and Practice of Parallel Programming

作者： Sun, Jie Shi, Zuocheng Su, Li Shen, Wenting Wang, Zeke Li, Yong Yu, Wenyuan Lin, Wei Wu, Fei He, Bingsheng Zhou, Jingren Zhejiang Univ Hangzhou Peoples R China Alibaba Grp Hangzhou Peoples R China Zhejiang Univ Shanghai Inst Adv Study Hangzhou Peoples R China Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9798400714436

Online GNN inference has been widely explored by applications such as online recommendation and financial fraud detection systems, where even minor delays can result in significant financial impact. Real-time dynamic graph sampling enables online GNN inference to reflect the latest graph updates in real-world graphs. However, online GNN inference typically demands millisecond-level latency Service Level Objectives (SLOs) as its performance guarantees, which poses great challenges for existing dynamic graph sampling approaches based on graph databases. The issues mainly arise from two aspects: long tail latency due to imbalanced data-dependent sampling and large communication overhead incurred by distributed sampling. To address these issues, we propose Helios, an efficient distributed dynamic graph sampling service to meet the stringent latency SLOs. The key ideas of Helios are 1) pre-sampling the dynamic graph in an event-driven approach, and 2) maintaining a query-aware sample cache to build the complete K-hop sampling results locally for inference requests. Experiments on multiple datasets show that Helios achieves up to 67x higher serving throughput and up to 32x lower P99 query latency compared to baselines.

关键词： Graph Neural Network distributed System Online Inference

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All Byzantine Agreement Problems Are Expensive 24

All Byzantine Agreement Problems Are Expensive

引用

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

作者： Civit, Pierre Gilbert, Seth Guerraoui, Rachid Komatovic, Jovan Paramonov, Anton Vidigueira, Manuel Lausanne Switzerland Nus Singapore Singapore Singapore

ISBN: (纸本)9798400706684

Byzantine agreement, arguably the most fundamental problem in distributed computing, operates among n processes, out of which t 2) exchanged messages in the worst case. Since then, it remained unknown whether the quadratic lower bound extends to all variants of Byzantine agreement. This paper answers the question in the affirmative, closing this long-standing open problem. Namely, we prove that any non-trivial agreement problem requires ω(t2) messages to be exchanged in the worst case. To prove the general lower bound, we determine the weakest Byzantine agreement problem and show, via a novel indistinguishability argument, that it incurs ω(t2) exchanged messages even in the general omission model. © 2024 Association for computing Machinery. All rights reserved.

关键词： distributed computer systems

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Adaptive Parallel Training for Graph Neural Networks 25

Adaptive Parallel Training for Graph Neural Networks

引用

30th symposium on principles and Practice of Parallel Programming

作者： Ma, Kaihao Liu, Renjie Yan, Xiao Cai, Zhenkun Song, Xiang Wang, Minjie Li, Yichao Cheng, James Chinese Univ Hong Kong Hong Kong Peoples R China Southern Univ Sci & Technol Shenzhen Peoples R China Ctr Perceptual & Interact Intelligence Hong Kong Peoples R China Amazon Seattle WA USA AWS Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798400714436

There are several strategies to parallelize graph neural network (GNN) training over multiple GPUs. We observe that there is no consistent winner (i.e., with the shortest running time), and the optimal strategy depends on the graph dataset, GNN model, training algorithm, and hardware configurations. As such, we design the APT system to automatically select efficient parallelization strategies for GNN training tasks. To this end, we analyze the trade-offs of the strategies and design simple yet effective cost models to compare their execution time and facilitate strategy selection. Moreover, we also propose a general abstraction of the strategies, which allows to implement a unified execution engine that can be configured to run different strategies. Our experiments show that APT usually chooses the optimal or a close to optimal strategy, and the training time can be reduced by over 2x compared with always using a single strategy. APT is open-source at https://***/kaihaoma/APT.

关键词： Graph Neural Networks distributed and Parallel Training Network Communication

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WeiPipe: Weight Pipeline Parallelism for Communication-Effective Long-Context Large Model Training 25

WeiPipe: Weight Pipeline Parallelism for Communication-Effec...

引用

30th symposium on principles and Practice of Parallel Programming

作者： Lin, Junfeng Liu, Ziming You, Yang Wang, Jun Zhang, Weihao Zhao, Rong Tsinghua Univ Beijing Peoples R China Natl Univ Singapore Singapore Singapore CETHIK Grp Co Ltd Hangzhou Peoples R China Lynxi Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9798400714436

Training large language models (LLMs) has become increasingly expensive due to the rapid expansion in model size. Pipeline parallelism is a widely used distributed training technique. However, as LLMs with larger context become prevalent and memory optimization techniques advance, traditional PP methods encounter greater communication challenges due to the increased size of activations and gradients of activations. To address this issue, we introduce weight-pipeline parallelism (WeiPipe) that transitions from an activation-passing pipeline to a weight-passing pipeline. WeiPipe reduces communication costs and achieves a more balanced utilization by transmitting only weights and their gradients between workers in a pipeline manner. WeiPipe does not rely on collective communication primitives, thus ensuring scalability. We present four variations of WeiPipe parallelism, including WeiPipe-Interleave, which emphasizes communication efficiency, and WeiPipe-zero-bubble, discussing the potential for minimal bubble ratios. Our implementation of WeiPipe-Interleave, performed on up to 32 GPUs and tested in various model configurations, including large-context LLM training, demonstrates a significant improvement in throughput compared to state-of-the-art pipeline parallelism and fully sharded data parallelism with different underlying infrastructures, including NVLink connections within cluster with Ethernet among cluster, and PCIe within cluster and Ethernet among cluster. Additionally, WeiPipe also shows greater scalability in communicationconstrained scenarios compared to state-of-art strategies.

关键词： distributed deep learning pipeline parallelism large-scale training long-context training

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OsirisBFT: Say No to Task Replication for Scalable Byzantine Fault Tolerant Analytics 24

OsirisBFT: Say No to Task Replication for Scalable Byzantine...

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29th acm SIGPLAN Annual symposium on principles and Practice of Parallel Programming (PPoPP)

作者： Jamshidi, Kasra Vora, Keval Simon Fraser Univ Sch Comp Sci Burnaby BC Canada

ISBN: (纸本)9798400704352

We present a verification-based Byzantine Fault Tolerant processing system, called OsirisBFT, for distributed taskparallel applications. OsirisBFT treats computation tasks differently from state update tasks, allowing the application to scale independently from number of expected failures. OsirisBFT captures application-specific verification semantics via generic verification operators and employs lightweight verification strategies with little coordination during graceful execution. Evaluation across multiple applications and workloads shows that OsirisBFT delivers high processing throughput and scalability compared to replicated processing. Importantly, the scalable nature of OsirisBFT enables it to reduce the performance gap compared to baseline with no fault tolerance by simply scaling out.

关键词： distributed computing Byzantine Fault Tolerance Data Processing Systems Resilient Systems.

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computing Minimum Weight Cycle in the CONGEST Model 24

Computing Minimum Weight Cycle in the CONGEST Model

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

作者： Manoharan, Vignesh Ramachandran, Vijaya The University of Texas at Austin AustinTX United States

ISBN: (纸本)9798400706684

Minimum Weight Cycle (MWC) is the problem of finding a simple cycle of minimum weight in a graph G = (V, E). This is a fundamental graph problem with classical sequential algorithms that run in Õ(n3) and Õ(mn) time† where n = |V| and m = |E|. In recent years this problem has received significant attention in the context of fine-grained sequential complexity [3, 50] as well as in the design of faster sequential approximation algorithms [13, 26, 32, 33], though not much is known in the distributed CONGEST *** present near-optimal ω(n) CONGEST lower bounds on the round complexity of computing exact and (2 - ϵ)-approximate MWC in undirected weighted graphs and in directed graphs even if unweighted. We complement these lower bounds with sublinear-round algorithms for computing 2-approximation of MWC. Our algorithms use a variety of techniques in non-trivial ways, such as in our approximate directed unweighted MWC algorithm that efficiently computes BFS from all vertices restricted to certain implicitly computed neighborhoods in sublinear rounds, and in our weighted approximation algorithms that use unweighted MWC algorithms on scaled graphs combined with a fast and streamlined method for computing multiple source approximate SSSP. © 2024 Association for computing Machinery. All rights reserved.

关键词： Directed graphs

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Brief Announcement: distributed Construction of Near-Optimal Compact Routing Schemes for Planar Graphs 23

Brief Announcement: Distributed Construction of Near-Optimal...

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42nd acm SIGACT-SIGOPS symposium on principles of distributed computing (PODC)

作者： Dou, Jinfeng Goette, Thorsten Hillebrandt, Henning Scheideler, Christian Werthmann, Julian Paderborn Univ Paderborn Germany

ISBN: (纸本)9798400701214

We consider the problem of computing a compact routing scheme for a weighted undirected planar graph G := (V,E,w) in several models. For a given parameter is an element of > 0, we compute a routing scheme with stretch 1 + is an element of and labels and routing tables of size (O) over tilde (is an element of -1). In CONGEST, the construction takes (O) over tilde(is an element of-3 center dot HD) time, where HD denotes the network's hop-diameter. Further, it takes (O) over tilde(is an element of-3) time in a PRAM with O(n) processors and the novel HYBRID model. Thus, our algorithms are almost optimal in all relevant parameters. To achieve these results, we extend the divide-and-conquer framework of Li and Parter [STOC '19] and combine it with state-of-the-art distributed distance approximation algorithms [STOC '22].

关键词： Compact Routing Planar Graph distributed Algorithm

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On the Amortized Communication Complexity of Byzantine Broadcast 23

On the Amortized Communication Complexity of Byzantine Broad...

引用

42nd acm SIGACT-SIGOPS symposium on principles of distributed computing (PODC)

作者： Wan, Jun Xiang, Zhuolun Momose, Atsuki Ren, Ling Shi, Elaine MIT Cambridge MA 02139 USA Aptos Palo Alto CA USA Univ Illinois Champaign IL USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798400701214

Designing an efficient solution for Byzantine broadcast is an important problem for many distributed computing and cryptographic tasks. There have been many attempts to achieve sub-quadratic communication complexity in several directions, both in theory and practice, all with pros and cons. This paper initiates the study of another attempt: improving the amortized communication complexity of multi-shot Byzantine broadcast. Namely, we try to improve the average cost when we have sequential multiple broadcast instances. We present a protocol that achieves optimal amortized linear complexity under an honest majority. Our core technique is to efficiently form a network for disseminating the sender's message by keeping track of dishonest behaviors over multiple instances. We also generalize the technique for the dishonest majority to achieve amortized quadratic communication complexity.

关键词： distributed System Consensus Byzantine Broadcast Communication Complexity

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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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Brief Announcement: Local Problems in the SUPPORTED Model 23

Brief Announcement: Local Problems in the SUPPORTED Model

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42nd acm SIGACT-SIGOPS symposium on principles of distributed computing (PODC)

作者： Agrawal, Akanksha Augustine, John Peleg, David Ramachandran, Srikkanth Indian Inst Technol Madras Chennai Tamil Nadu India Weizmann Inst Sci Rehovot Israel

ISBN: (纸本)9798400701214

We study the SUPPORTED model of distributed computing introduced by Schmid and Suomela [15], generalizing the LOCAL and CONGEST models. In this framework, multiple instances of the same problem, differing from each other by some problem specific input, recur over time, and need to be solved efficiently online. To do that, one may rely on an initial preprocessing phase for computing some useful information. This preprocessing phase makes it possible, in some cases, to obtain improved distributed algorithms, overcoming locality-based time lower bounds. We propose some natural recurrent variants of the dominating set problem and the coloring problem that are of interest particularly in the distributed setting. For these problems, we show that information about the topology can be used to overcome locality-based lower bounds. We also categorize the round complexity of Locally Checkable Labellings in the SUPPORTED model for the simple case of paths. Finally we present some interesting open problems and some partial results towards resolving them.

关键词： distributed Algorithms SUPPORTED Model LOCAL Model

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