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DSANA: A distributed machine learning acceleration solution based on dynamic scheduling and network acceleration 22

DSANA: A distributed machine learning acceleration solution ...

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22nd IEEE international conference on High Performance computing and Communications, 18th IEEE international conference on Smart City and 6th IEEE international conference on Data Science and systems, HPCC-SmartCity-DSS 2020

作者： Zhang, Runhua Shen, Guowei Gong, Liangyi Guo, Chun Guizhou University China Guiyang Beihang University China Beijing China Guizhou University China Guiyang China Tsinghua University China Beijing China

ISBN: (纸本)9781728176499

distributed machine learning(DML) has become a feasible solution to deal with the growing training data and models. Reviewing the existing architecture of DML, Parametric server(PS) architecture stands out in iterative convergence algorithms and widely deployed in practice, thanks to flexible expansion and so on. Under this architecture, the parameter synchronization mode based on Bulk Synchronous parallel(BSP) has become one of the research hotspots. As for the BSP mode, each iteration efficiency is determined by the slowest node in the cluster, therefore, the straggler problem becomes the main reason for reducing the efficiency of DML training, which is even more prominent in the heterogeneous cloud services. Existing works mainly focus on the straggler problem, and the importance of communication is usually ignored. However, inefficient communication is also one of the reasons for the inefficiency of DML iterations. In this paper, we propose DSANA, which first alleviates certain straggler problems by dynamically scheduling computation tasks. Secondly, DSANA improves the overlap of computation/communication by dividing larger transmission parameters, thus further improving the iteration efficiency of DML training. We conduct comparison experiments with the classic iterative algorithm PageRank on four different-scale data sets in two cloud service scenarios. the experimental results show that DSANA can improve the training efficiency to 36.6%\sim 56.4% compared with the baseline solution. © 2020 IEEE.

关键词： Efficiency

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From Serial Loops to parallel Execution on distributed systems

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18th international conference on Euro-Par parallel Processing

作者： Bosilca, George Bouteiller, Aurelien Danalis, Anthony Herault, thomas Dongarra, Jack Univ Tennessee Knoxville TN 37996 USA

ISBN: (纸本)9783642328206

Programmability and performance portability are two major challenges in today's dynamic environment. Algorithm designers targeting efficient algorithms should focus on designing high-level algorithms exhibiting maximum parallelism, while relying on compilers and run-time systems to discover and exploit this parallelism, delivering sustainable performance on a variety of hardware. the compiler tool presented in this paper can analyze the data flow of serial codes with imperfectly nested, affine loop-nests and if statements, commonly found in scientific applications. this tool operates as the front-end compiler for the DAGuE run-time system by automatically converting serial codes into the symbolic representation of their data flow. We show how the compiler analyzes the data flow, and demonstrate that scientifically important, dense linear algebra operations can benefit from this analysis, and deliver high performance on large scale platforms.

关键词： compiler analysis symbolic data flow distributed computing task scheduling

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the Impact of Importance-Aware Dataset Partitioning on Data-parallel Training of Deep Neural Networks 23rd

The Impact of Importance-Aware Dataset Partitioning on Data-...

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23rd IFIP WG 6.1 international conference on distributed Applications and Interoperable systems (DAIS) Held as Part of the 18th international Federated conference on distributed computing Techniques (DisCoTec)

作者： Sheikholeslami, Sina Payberah, Amir H. Wang, Tianze Dowling, Jim Vlassov, Vladimir KTH Royal Inst Technol Stockholm Sweden Hopsworks AB Stockholm Sweden

ISBN: (纸本)9783031352591;9783031352607

Deep neural networks used for computer vision tasks are typically trained on datasets consisting of thousands of images, called examples. Recent studies have shown that examples in a dataset are not of equal importance for model training and can be categorized based on quantifiable measures reflecting a notion of "hardness" or "importance". In this work, we conduct an empirical study of the impact of importance-aware partitioning of the dataset examples across workers on the performance of data-parallel training of deep neural networks. Our experiments with CIFAR-10 and CIFAR-100 image datasets show that data-parallel training with importance-aware partitioning can perform better than vanilla data-parallel training, which is oblivious to the importance of examples. More specifically, the proper choice of the importance measure, partitioning heuristic, and the number of intervals for dataset repartitioning can improve the best accuracy of the model trained for a fixed number of epochs. We conclude that the parameters related to importance-aware data-parallel training, including the importance measure, number of warmup training epochs, and others defined in the paper, may be considered as hyperparameters of data-parallel model training.

关键词： Data-parallel training Example importance distributed deep learning

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DDOS: Taming Nondeterminism in distributed systems 13

DDOS: Taming Nondeterminism in Distributed Systems

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18th international conference on Architectural Support for Programming Languages and Operating systems

作者： Hunt, Nicholas Bergan, Tom Ceze, Luis Gribble, Steven D. Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781450318709

Nondeterminism complicates the development and management of distributed systems, and arises from two main sources: the local behavior of each individual node as well as the behavior of the network connecting them. Taming nondeterminism effectively requires dealing with both sources. this paper proposes DDOS, a system that leverages prior work on deterministic multithreading to offer: 1) space-efficient record/replay of distributed systems;and 2) fully deterministic distributed behavior. Leveraging deterministic behavior at each node makes outgoing messages strictly a function of explicit inputs. this allows us to record the system by logging just message's arrival time, not the contents. Going further, we propose and implement an algorithm that makes all communication between nodes deterministic by scheduling communication onto a global logical timeline. We implement both algorithms in a system called DDOS and evaluate our system with parallel scientific applications, an HTTP/memcached system and a distributed microbenchmark with a high volume of peer-to-peer communication. Our results show up to two orders of magnitude reduction in log size of record/replay, and that distributed systems can be made deterministic with an order of magnitude of overhead.

关键词： distributed systems Reliability Determinism Record-and-Replay

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RDFM: Resilient distributed Factorization Machines 18th

RDFM: Resilient Distributed Factorization Machines

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18th international conference on Artificial Intelligence and Soft computing (ICAISC)

作者： da Silva, Andre Rodrigo Rodrigues, Leonardo M. Rech, Luciana de Oliveira Luiz, Aldelir Fernando Fed Univ Santa Catarina UFSC Dept Informat & Stat INE BR-88040900 Florianopolis SC Brazil

ISBN: (纸本)9783030209155;9783030209148

Factorization Machines algorithms have been successfully applied to recommender systems due to their ability to handle data sparsity and the cold-start problem. their scalability makes it suitable to produce evergrowing complex predictive models, which are based on Big Data without performance degradation. the algorithm has been scaled to contexts of distributed and parallel computation, but in general with the strong assumption that those environments are safe and are not subject to arbitrary errors, malicious attacks, and hardware failures. In this work, we show that a distributed average consensus strategy is capable to deal with unsafe and dynamic learning environments.

关键词： distributed average consensus Factorization Machines Fault tolerance

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AvesTerra: A Framework for global-scale Knowledge Representation and Analytic Interoperability Invited Talk 21

AvesTerra: A Framework for global-scale Knowledge Representa...

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18th ACM international conference on computing Frontiers (CF)

作者： Smart, J. C. Georgetown Univ Washington DC 20057 USA

ISBN: (纸本)9781450384049

AvesTerra is a distributed knowledge representation framework for integrating many large and disparate data systems and analytic components at global scale. this framework allows data created or curated by many different institutions to be linked into a single unified, dynamic knowledge representation structure. the resulting fabric provides participants with a means to engage in multidisciplinary research and collaboration spanning many information systems without requiring a sophisticated computer science understanding of the mechanics of "Big Data" manipulation. Furthermore, AvesTerra enables this integration without the need for centralized data aggregation or local high-performance computational infrastructure, leveraging instead the distributed resources of a diverse and highly distributed analytic *** a core technical level, AvesTerra consists of a system of peer-to-peer servers that collectively form a readily scalable knowledge space. the mathematical structure of this space is that of a generalized, recursive hypergraph, enabling the representation of complex dependency structures often encountered when working towards global scale. the framework incorporates numerous computational constructs including event publication and subscription, parallel threading and timer support, a unique distributed rendezvous mechanism for agent-based organization, privacy isolation, and semantic structure execution. this presentation provides an overview of the full framework and a sampling of the applications currently under development.

关键词： distributed computing knowledge representation analytic interoperability

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DCOSS 2012 Foreword

DCOSS 2012 Foreword

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8th IEEE international conference on distributed computing in Sensor systems, DCOSS 2012

作者： Lu, Dongming Xu, Wenyuan Wang, Zhi Rabbat, Michael Zhejiang University China University of South Carolina United States McGill University Canada

ISBN: (纸本)9780769547077

the 8th IEEE international conference on distributed computing in Sensor systems (DCOSS 2012) is taking place in Hangzhou, China, from Wednesday, May 16, to Friday, May 18, 2012. Previous editions have taken place in Barcelona, Spain (2011), Santa Barbara, USA (2010), Los Angeles, USA (2009), Santorini Island, Greece (2008), Santa Fe, USA (2007), San Francisco, USA (2006), and Marina del Rey, USA (2005). We are pleased to present the conference program to you. this year, the DCOSS program features high-quality research presentations, two keynotes, and a poster and work-inprogress session. the conference program covers several aspects of distributed computing in sensor systems aligned with the three main tracks: algorithms and analysis, signal processing and information theory, and systems and applications. this year we received 100 submissions. Each submitted paper was reviewed by at least three Technical Program Committee experts, and ultimately a total of 34 papers were accepted. We will also feature three interesting workshops and one poster/demonstration/ work-in-progress session. We are proud to feature two excellent keynotes from leading experts on issues currently driving the distributed computing and sensor systems community. We hope that the technical program will provide a stimulating forum for researchers and practitioners throughout all three days. © 2012 IEEE.

关键词： distributed computer systems

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Privacy-Preserving Multi-Task Learning 18

Privacy-Preserving Multi-Task Learning

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18th IEEE international conference on Data Mining Workshops (ICDMW)

作者： Liu, Kunpeng Uplavikar, Nitish Jiang, Wei Fu, Yanjie Missouri Univ Sci & Technol Rolla MO 65409 USA Univ Missouri Columbia MO 65211 USA

ISBN: (纸本)9781538691595

Multi-task learning (MTL), improving learning performance by transferring information between related tasks, has drawn more and more attention in the data mining field. To tackle tasks whose data are stored at different locations (or nodes), distributed MTL was proposed. It not only enhances the learning performance but also improves the computing efficiency since it transforms the original centralized computing framework into a distributed computing framework under which computations can be done in parallel. the major drawback of the distributed MTL is a potential violation of confidentiality when the data stored at each node contain sensitive information (e.g., medical records). Some distributed MTL algorithms were designed to protect the original by only transferring aggregate information (e.g., supports or gradients) from each node to a server who combines the received information to produce the desired models. However, since aggregate data may still leak sensitive information, the security guarantee of the existing solutions cannot be formally proved or verified. thus, the goal of this paper is to develop a provable privacy-preserving multi-task learning (PP-MTL) protocol that incorporates the state of the art cryptographic techniques to achieve the best security guarantee. We also conducted experiments to demonstrate the efficiency of our proposed method.

关键词： Servers Cryptography Task analysis Protocols distributed databases Mathematical model

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A general performance model of structured and unstructured mesh particle transport computations

A general performance model of structured and unstructured m...

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3rd international Workshop on Performance Modelling, Evaluation, and Optimization of parallel and distributed systems held in Conjunction with the 18th international parallel and distributed Processing Symposium

作者： Mathis, MM Kerbyson, DJ Los Alamos Natl Lab PAL Los Alamos NM 87545 USA

the performance of unstructured mesh applications presents a number of complexities and subtleties that do not arise for dense structured meshes. From a programming point of view, the handling of unstructured meshes has an increased complexity in order to manage the necessary data structures and interactions between mesh-cells. From a performance point of view, there are added difficulties in understanding both the processing time on a single processor and the scaling characteristics when using large-scale parallel systems. In this work we present a general performance model for the calculation of deterministic S-N transport on unstructured meshes that is also applicable to structured meshes. the model captures the key processing characteristics of the calculation and is parametric using both system performance data (latency, bandwidth, processing rate etc.) and application data (mesh size etc.) as input. A single formulation of the model is used to predict the performance of two quite different implementations of the same calculation. It is validated on two clusters (an HP AlphaServer and an Itanium-2 system) showing high prediction accuracy.

关键词： performance modeling performance analysis high performance computing SN transport unstructured meshes parallel processing large-scale systems

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ADABA: An algorithm to improve the parallel search in competitive agents 18th

ADABA: An algorithm to improve the parallel search in compet...

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Joint conferences on 18th international conference on Intelligent systems Design and Applications, ISDA 2018 and 10th World Congress on Nature and Biologically Inspired computing , NaBIC 2018

作者： Tomaz, Lídia Bononi Paiva Julia, Rita Maria Silva Federal University of Uberlândia UberlândiaMinas Gerais Brazil Federal Institute of Triângulo Mineiro UberabaMinas Gerais Brazil

ISBN: (纸本)9783030166564

distributed approaches of the Alpha-Beta search algorithm are commonly used as decision-making resources in agents designed for operating in competitive environments involving elevated state spaces. In this context, the present paper proposes a new master-slave based distributed version of Alpha-Beta, named ADABA (Asynchronous distributed Alpha-Beta Algorithm), which is inspired upon the relevant Alpha-Beta distributed version known as APHID (Asynchronous parallel Hierarchical Iterative Deepening). ADABA aims at enhancing the APHID’s performance in the following ways: establishing a new policy for ordering the tasks that are executed by the slaves;and including a thread pool dedicated to improving the operation of the master processor in regards to the results returned by the slave processors. ADABA’s performance is validated by means of tournaments involving two Checkers agents trained by Reinforcement Learning: the ADABA-based Checkers player named ADABA-Draughts, implemented in this paper, and the well successful APHID-based agent called APHID-Draughts. © Springer Nature Switzerland AG 2020.

关键词： Decision making

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