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A Decentralized Algorithm For Communication Efficient Distributed Shared Memory
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Book Synopsis A Decentralized Algorithm for Communication Efficient Distributed Shared Memory by : Legand L. Burge
Download or read book A Decentralized Algorithm for Communication Efficient Distributed Shared Memory written by Legand L. Burge and published by . This book was released on 1995 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Communication Efficient Distributed Shared Memories by : Masaaki Mizuno
Download or read book Communication Efficient Distributed Shared Memories written by Masaaki Mizuno and published by . This book was released on 1992 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Recently, distributed shared memory (DSM) systems have received much attention because such an abstraction simplifies programming. An important class of DSM implementations is one which uses cache memories to improve efficiency. In this paper, we present a cache- consistency protocol which uses considerably less communication as compared to previously proposed protocols. This is realized by maintaining state information and capturing causal relations among read and write operations. We prove that the protocol satisfies a formulation of sequential consistency. We also present several modifications to the protocol and compare the classes of execution histories captured by these protocols and several previously proposed protocols."
Book Synopsis A Novel and Efficient Distributed Shared Memory Algorithm for Strict Causal Consistency by : J. Russell Noseworthy
Download or read book A Novel and Efficient Distributed Shared Memory Algorithm for Strict Causal Consistency written by J. Russell Noseworthy and published by . This book was released on 1996 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Efficient Distributed Algorithms by : Yao Li
Download or read book Efficient Distributed Algorithms written by Yao Li and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Large-scale machine learning models are often trained by distributed algorithms over either centralized or decentralized networks. The former uses a central server to aggregate the information of local computing agents and broadcast the averaged parameters in a master-slave architecture. The latter considers a connected network formed by all agents. The information can only be exchanged with accessible neighbors with a mixing matrix of communication weights encoding the network's topology. Compared with centralized optimization, decentralization facilitates data privacy and reduces the communication burden of the single central agent due to model synchronization, but the connectivity of the communication network weakens the theoretical convergence complexity of the decentralized algorithms. Therefore, there are still gaps between decentralized and centralized algorithms in terms of convergence conditions and rates. In the first part of this dissertation, we consider two decentralized algorithms: EXTRA and NIDS, which both converge linearly with strongly convex objective functions and answer two questions regarding them. \extit{What are the optimal upper bounds for their stepsizes?} \extit{Do decentralized algorithms require more properties on the functions for linear convergence than centralized ones?} More specifically, we relax the required conditions for linear convergence of both algorithms. For EXTRA, we show that the stepsize is comparable to that of centralized algorithms. For NIDS, the upper bound of the stepsize is shown to be exactly the same as the centralized ones. In addition, we relax the requirement for the objective functions and the mixing matrices. We provide the linear convergence results for both algorithms under the weakest conditions.As the number of computing agents and the dimension of the model increase, the communication cost of parameter synchronization becomes the major obstacle to efficient learning. Communication compression techniques have exhibited great potential as an antidote to accelerate distributed machine learning by mitigating the communication bottleneck. In the rest of the dissertation, we propose compressed residual communication frameworks for both centralized and decentralized optimization and design different algorithms to achieve efficient communication. For centralized optimization, we propose DORE, a modified parallel stochastic gradient descent method with a bidirectional residual compression, to reduce over $95\\%$ of the overall communication. Our theoretical analysis demonstrates that the proposed strategy has superior convergence properties for both strongly convex and nonconvex objective functions. Existing works mainly focus on smooth problems and compressing DGD-type algorithms for decentralized optimization. The class of smooth objective functions and the sublinear convergence rate under relatively strong assumptions limit these algorithms' application and practical performance. Motivated by primal-dual algorithms, we propose Prox-LEAD, a linear convergent decentralized algorithm with compression, to tackle strongly convex problems with a nonsmooth regularizer. Our theory describes the coupled dynamics of the inexact primal and dual update as well as compression error without assuming bounded gradients. The superiority of the proposed algorithm is demonstrated through the comparison with state-of-the-art algorithms in terms of convergence complexities and numerical experiments. Our algorithmic framework also generally enlightens the compressed communication on other primal-dual algorithms by reducing the impact of inexact iterations.
Book Synopsis Synthesizing Communication-efficient Distributed-memory Parallel Programs for Block Recursive Algorithms by : Sandeep K. S. Gupta
Download or read book Synthesizing Communication-efficient Distributed-memory Parallel Programs for Block Recursive Algorithms written by Sandeep K. S. Gupta and published by . This book was released on 1995 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Distributed Algorithms by : Sam Toueg
Download or read book Distributed Algorithms written by Sam Toueg and published by Springer Science & Business Media. This book was released on 1992-03-11 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the proceedings of the fifth International Workshop on Distributed Algorithms (WDAG '91) held in Delphi, Greece, in October 1991. The workshop provided a forum for researchers and others interested in distributed algorithms, communication networks, and decentralized systems. The aim was to present recent research results, explore directions for future research, and identify common fundamental techniques that serve as building blocks in many distributed algorithms. The volume contains 23 papers selected by the Program Committee from about fifty extended abstracts on the basis of perceived originality and quality and on thematic appropriateness and topical balance. The workshop was organizedby the Computer Technology Institute of Patras University, Greece.
Book Synopsis Distributed Algorithms for Message-Passing Systems by : Michel Raynal
Download or read book Distributed Algorithms for Message-Passing Systems written by Michel Raynal and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: Distributed computing is at the heart of many applications. It arises as soon as one has to solve a problem in terms of entities -- such as processes, peers, processors, nodes, or agents -- that individually have only a partial knowledge of the many input parameters associated with the problem. In particular each entity cooperating towards the common goal cannot have an instantaneous knowledge of the current state of the other entities. Whereas parallel computing is mainly concerned with 'efficiency', and real-time computing is mainly concerned with 'on-time computing', distributed computing is mainly concerned with 'mastering uncertainty' created by issues such as the multiplicity of control flows, asynchronous communication, unstable behaviors, mobility, and dynamicity. While some distributed algorithms consist of a few lines only, their behavior can be difficult to understand and their properties hard to state and prove. The aim of this book is to present in a comprehensive way the basic notions, concepts, and algorithms of distributed computing when the distributed entities cooperate by sending and receiving messages on top of an asynchronous network. The book is composed of seventeen chapters structured into six parts: distributed graph algorithms, in particular what makes them different from sequential or parallel algorithms; logical time and global states, the core of the book; mutual exclusion and resource allocation; high-level communication abstractions; distributed detection of properties; and distributed shared memory. The author establishes clear objectives per chapter and the content is supported throughout with illustrative examples, summaries, exercises, and annotated bibliographies. This book constitutes an introduction to distributed computing and is suitable for advanced undergraduate students or graduate students in computer science and computer engineering, graduate students in mathematics interested in distributed computing, and practitioners and engineers involved in the design and implementation of distributed applications. The reader should have a basic knowledge of algorithms and operating systems.
Book Synopsis Designing Communication-efficient Matrix Algorithms in Distributed-memory Cilk by : Eyal Baruch
Download or read book Designing Communication-efficient Matrix Algorithms in Distributed-memory Cilk written by Eyal Baruch and published by . This book was released on 2001 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Principles of Transactional Memory by : Rachid Guerraoui
Download or read book Principles of Transactional Memory written by Rachid Guerraoui and published by Morgan & Claypool Publishers. This book was released on 2010-04-04 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transactional memory (TM) is an appealing paradigm for concurrent programming on shared memory architectures. With a TM, threads of an application communicate, and synchronize their actions, via in-memory transactions. Each transaction can perform any number of operations on shared data, and then either commit or abort. When the transaction commits, the effects of all its operations become immediately visible to other transactions; when it aborts, however, those effects are entirely discarded. Transactions are atomic: programmers get the illusion that every transaction executes all its operations instantaneously, at some single and unique point in time. Yet, a TM runs transactions concurrently to leverage the parallelism offered by modern processors. The aim of this book is to provide theoretical foundations for transactional memory. This includes defining a model of a TM, as well as answering precisely when a TM implementation is correct, what kind of properties it can ensure, what are the power and limitations of a TM, and what inherent trade-offs are involved in designing a TM algorithm. While the focus of this book is on the fundamental principles, its goal is to capture the common intuition behind the semantics of TMs and the properties of existing TM implementations. Table of Contents: Introduction / Shared Memory Systems / Transactional Memory: A Primer / TM Correctness Issues / Implementing a TM / Further Reading / Opacity / Proving Opacity: An Example / Opacity vs.\ Atomicity / Further Reading / The Liveness of a TM / Lock-Based TMs / Obstruction-Free TMs / General Liveness of TMs / Further Reading / Conclusions
Book Synopsis Distributed Shared Memory by : Jelica Protic
Download or read book Distributed Shared Memory written by Jelica Protic and published by Wiley-IEEE Computer Society Press. This book was released on 1998 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer Systems Organization -- Parallel architecture.
Book Synopsis An Analysis of Distributed Shared Memory Algorithms by : University of Wisconsin--Madison. Computer Sciences Dept
Download or read book An Analysis of Distributed Shared Memory Algorithms written by University of Wisconsin--Madison. Computer Sciences Dept and published by . This book was released on 1989 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Communication-efficient Algorithms for Distributed Optimization by : João F. C. Mota
Download or read book Communication-efficient Algorithms for Distributed Optimization written by João F. C. Mota and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis First-Order Algorithms for Communication Efficient Distributed Learning by : Sarit Khirirat
Download or read book First-Order Algorithms for Communication Efficient Distributed Learning written by Sarit Khirirat and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Efficient Distributed Shared Memory Using Mapped Segmentation and Reusable Single-assignment Variables by : Manas Mandal
Download or read book Efficient Distributed Shared Memory Using Mapped Segmentation and Reusable Single-assignment Variables written by Manas Mandal and published by . This book was released on 1995 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :University of Oregon. Department of Computer and Information Science Publisher : ISBN 13 : Total Pages :24 pages Book Rating :4.:/5 (123 download)
Book Synopsis Distributed Shared Memory: a Survey of Issues and Algorithms by : University of Oregon. Department of Computer and Information Science
Download or read book Distributed Shared Memory: a Survey of Issues and Algorithms written by University of Oregon. Department of Computer and Information Science and published by . This book was released on 1991 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Parallel and Distributed Processing by : José D. P. Rolim
Download or read book Parallel and Distributed Processing written by José D. P. Rolim and published by Springer Science & Business Media. This book was released on 1999-03-30 with total page 1474 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of 11 IPPS/SPDP '98 Workshops held in conjunction with the 13th International Parallel Processing Symposium and the 10th Symposium on Parallel and Distributed Processing in San Juan, Puerto Rico, USA in April 1999. The 126 revised papers presented were carefully selected from a wealth of papers submitted. The papers are organised in topical sections on biologically inspired solutions to parallel processing problems: High-Level Parallel Programming Models and Supportive Environments; Biologically Inspired Solutions to Parallel Processing; Parallel and Distributed Real-Time Systems; Run-Time Systems for Parallel Programming; Reconfigurable Architectures; Java for Parallel and Distributed Computing; Optics and Computer Science; Solving Irregularly Structured Problems in Parallel; Personal Computer Based Workstation Networks; Formal Methods for Parallel Programming; Embedded HPC Systems and Applications.
Book Synopsis Provably Efficient Algorithms for Decentralized Optimization by : Changxin Liu
Download or read book Provably Efficient Algorithms for Decentralized Optimization written by Changxin Liu and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Decentralized multi-agent optimization has emerged as a powerful paradigm that finds broad applications in engineering design including federated machine learning and control of networked systems. In these setups, a group of agents are connected via a network with general topology. Under the communication constraint, they aim to solving a global optimization problem that is characterized collectively by their individual interests. Of particular importance are the computation and communication efficiency of decentralized optimization algorithms. Due to the heterogeneity of local objective functions, fostering cooperation across the agents over a possibly time-varying network is challenging yet necessary to achieve fast convergence to the global optimum. Furthermore, real-world communication networks are subject to congestion and bandwidth limit. To relieve the difficulty, it is highly desirable to design communication-efficient algorithms that proactively reduce the utilization of network resources. This dissertation tackles four concrete settings in decentralized optimization, and develops four provably efficient algorithms for solving them, respectively. Chapter 1 presents an overview of decentralized optimization, where some preliminaries, problem settings, and the state-of-the-art algorithms are introduced. Chapter 2 introduces the notation and reviews some key concepts that are useful throughout this dissertation. In Chapter 3, we investigate the non-smooth cost-coupled decentralized optimization and a special instance, that is, the dual form of constraint-coupled decentralized optimization. We develop a decentralized subgradient method with double averaging that guarantees the last iterate convergence, which is crucial to solving decentralized dual Lagrangian problems with convergence rate guarantee. Chapter 4 studies the composite cost-coupled decentralized optimization in stochastic networks, for which existing algorithms do not guarantee linear convergence. We propose a new decentralized dual averaging (DDA) algorithm to solve this problem. Under a rather mild condition on stochastic networks, we show that the proposed DDA attains an $\mathcal{O}(1/t)$ rate of convergence in the general case and a global linear rate of convergence if each local objective function is strongly convex. Chapter 5 tackles the smooth cost-coupled decentralized constrained optimization problem. We leverage the extrapolation technique and the average consensus protocol to develop an accelerated DDA algorithm. The rate of convergence is proved to be $\mathcal{O}\left( \frac{1}{t^2}+ \frac{1}{t(1-\beta)^2} \right)$, where $\beta$ denotes the second largest singular value of the mixing matrix. To proactively reduce the utilization of network resources, a communication-efficient decentralized primal-dual algorithm is developed based on the event-triggered broadcasting strategy in Chapter 6. In this algorithm, each agent locally determines whether to generate network transmissions by comparing a pre-defined threshold with the deviation between the iterates at present and lastly broadcast. Provided that the threshold sequence is summable over time, we prove an $\mathcal{O}(1/t)$ rate of convergence for convex composite objectives. For strongly convex and smooth problems, linear convergence is guaranteed if the threshold sequence is diminishing geometrically. Finally, Chapter 7 provides some concluding remarks and research directions for future study.