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Efficient Management and Intelligent Fault Tolerance for HPC Interconnect Networks

Efficient Management and Intelligent Fault Tolerance for HPC...

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international conference on parallel and distributed systems (ICPADS)

作者： Jijun Cao Mingche Lai Zhang Luo jiaqing Xu Zhengbin Pang College of Computer National Laboratory for Parallel and Distributed Processing ChangSha China

ISBN: (纸本)9781728125848

Interconnect Network is the key component in high performance computing system. With the incoming era of Exa-Scale (10 18 FLOPS) computing, designing large scale interconnect networks is facing with serious challenges in network management and fault tolerance. To construct higher performance and more reliable interconnect networks, we propose an efficient and intelligent network management architecture for indirect interconnect networks. this paper emphatically introduces the network management architecture, the in-band management channels in the Network Interface Chips (NIC) and Network Routing Chips (NRC) respectively, efficient centralized network management approach, distributed intelligent faulttolerant routing management, and so on. Based on the prototype system, the Control and Status Registers (CSR) accessing latency performance of in-band network management is evaluated. Also based on a customized simulation model for indirect interconnect networks, the typical distributed fault-tolerant scenes are tested. the experiment results show the in-band network management can averagely achieves 716 times improvements than the out of-band network management. Moreover, by running heuristic algorithms to automatically reconstruct routing tables for failure links or routers, the intellectual network management Engine can achieve fault-tolerant routing to maximizing network performance.

关键词： High performance computing system Interconnect network Network management Fault tolerate Network management Fault tolerance High Performance computing unified network management architecture Network Network routing

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Hybrid communication topology and protocol for distributed-controlled cascade H-bridge multilevel STATCOM

Hybrid communication topology and protocol for distributed-c...

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international conference on Electrical Machines and systems (ICEMS)

作者： Chao Zhang Hua Geng Shuzhen Li Geng Yang Lei Dong School of Automation Beijing Institute of Technology China Department of Automation Tsinghua University China

ISBN: (纸本)9781479988068

distributed control scheme is preferred in the application of the multiple modules based system due to its simple structure, low maintenance and high scalability. this paper proposed a hybrid communication topology and protocol to realize the distributed control applied in the cascade H-bridge (CHB) multilevel static synchronous compensator (STATCOM) with master and slave controllers. In the same layer of the three-phase modules, the three slave controllers communicate with the master controller by the fundamental ring loop. In different layers of the three-phase modules, communication loops operate in parallel. In order to avoid the slow real-time response of the traditional ring communication structure which is not adaptive to power electronic facilities, this paper proposed a routing switching protocol. By switching the communication between the modified control data and fault signals, the protocol can achieve both quick control and fault response. Moreover, the synchronization among different modules are also dealt with by the proposed protocol. the topology and protocol are evaluated in the FPGA based control platform and the CHB multilevel STATCOM prototype. the experimental results verified the validity, flexibility and reliability of the proposed topology and protocol.

关键词： Decision support systems Protocols Topology Automatic voltage control Decentralized control Switches

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An Implementation of parallel 1-D FFT on the K Computer

An Implementation of Parallel 1-D FFT on the K Computer

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Daisuke Takahashi Atsuya Uno Mitsuo Yokokawa Faculty of Engineering Information and Systems University of Tsukuba Tsukuba Ibaraki Japan Rikagaku Kenkyujo Yokohama Campus Yokohama Kanagawa JP

In this paper, we propose an implementation of a parallel one-dimensional fast Fourier transform (FFT) on the K computer. the proposed algorithm is based on the six-step FFT algorithm, which can be altered into the recursive six-step FFT algorithm to reduce the number of cache misses. the recursive six-step FFT algorithm improves performance by utilizing the cache memory effectively. We use the recursive six-step FFT algorithm to implement the parallel one-dimensional FFT algorithm. the performance results of one-dimensional FFTs on the K computer are reported. We successfully achieved a performance of over 18 TFlops on 8192 nodes of the K computer (82944 nodes, 128 GFlops/node, 10.6 PFlops peak performance) for a 2 41 -point FFT.

关键词： Computers Arrays Registers Equations Indexes Kernel distributed databases

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New grid scheduling and rescheduling methods in the GrADS project

New grid scheduling and rescheduling methods in the GrADS pr...

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international Symposium on parallel and distributed Processing (IPDPS)

作者： K. Cooper A. Dasgupta K. Kennedy C. Koelbel A. Mandal G. Marin M. Mazina J. Mellor-Crummey F. Berman H. Casanova A. Chien H. Dail X. Liu A. Olugbile O. Sievert H. Xia L. Johnsson B. Liu M. Patel D. Reed W. Deng C. Mendes Z. Shi A. YarKhan J. Dongarra Computer Science Department Rice University USA Department of Computer Science University of California San Diego USA Department of Computer Science University of Houston USA Department of Computer Science University of Illinois USA ICL University of Tennessee Knoxville USA

Summary form only given. the goal of the Grid Application Development Software (GrADS) project is to provide programming tools and an execution environment to ease program development for the grid. We present recent extensions to the GrADS software framework: 1. A new approach to scheduling workflow computations, applied to a 3D image reconstruction application; 2. A simple stop/migrate/restart approach to rescheduling grid applications, applied to a QR 3. A process-swapping approach to rescheduling, applied to an N-body simulation. Experiments validating these methods were carried out on both the GrADS MacroGrid (a small but functional grid) and the MicroGrid (a controlled emulation of the grid) and the results were demonstrated at the SC2003 conference.

关键词： Grid computing Application software Computer science distributed computing Contracts Processor scheduling Software tools Instruments Usability Runtime

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Efficient and Scalable Network Emulation Using Adaptive Virtual Time

Efficient and Scalable Network Emulation Using Adaptive Virt...

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international conference on Computer Communications and Networks (ICCCN)

作者： Andreas Grau Klaus Herrmann Kurt Rothermel Institute of Parallel and Distributed Systems (IPVS) Universität Stuttgart Stuttgart Germany

Performance analysis and functionality testing are major parts of developing distributed software systems. Since the number of communicating software instances heavily influences the behavior of distributed applications and communication protocols, evaluation scenarios have to consider a large number of nodes. Network emulation provides an infrastructure for running these experiments using real prototype implementations in a controllable and realistic environment. Large-scale experiments, however, have a high resource consumption which often exceeds available physical testbed resources. Time dilation allows for reducing the resource demands of a scenario at the expense of the experiment's runtime. However, current approaches only consider a constant time dilation factor, which wastes a lot of resources in case of scenarios with varying load. We propose a framework for adaptive time virtualization that significantly reduces the runtime of experiments by improving resource utilization in network emulation testbeds. In this framework, resource demands are monitored and the time dilation factor is dynamically adapted to the required level. Our evaluation shows that adaptive virtual time in combination with our lightweight node virtualization architecture allows us to increase the possible scenario sizes by more than an order of magnitude and, at the same time, ensure unbiased emulation results. this represents an important contribution to making network emulation systems highly scalable.

关键词： Emulation Adaptive systems Runtime Performance analysis Software testing System testing Software systems Application software Protocols Software prototyping

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international Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Data Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia conference on Knowledge Discovery and Data Mining, PAKDD 2014

引用

international Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia conference on Knowledge Discovery and Data Mining, PAKDD 2014

ISBN: (纸本)9783319131856

the proceedings contain 73 papers. the special focus in this conference is on Data Analytics for Targeted Healthcare, Biologically Inspired Techniques for Data Mining, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, Data Mining in Social Networks and Pattern Mining and Application of Big Data. the topics include: Conversation intention perception based on knowledge base;joint tree of combinatorial maps;aspect-based sentiment analysis of Amazon reviews for fitness tracking devices;detecting the data group most prone to a specific disguise value;information diffusion pattern mining over online social media;query prediction by currently-browsed web pages and its applications;an emergency system in mobile devices;complementary product selection in E-commerce;mining top-K relevant stay regions from historical trajectories;personalized smart phone wearing behavior analysis;using knowledge graph to handle label imperfection;models for distributed, large scale data cleaning;a scalable data analytics algorithm for mining frequent patterns from uncertain data;local feature selection by formal concept analysis for multi-class classification;ensemble clustering of high dimensional data with fast map projection;a general framework for parallel unary operations on ZDDs;multi-group-based user perceptions for friend recommendation in social networks;toward automatic censorship detection in microblogs;social network community detection using strongly connected components;an author topic analysis of tobacco regulation investigators;toward mining user traversal patterns in the indoor environment;human action recognition in video under clutter and moving background;analyzing personality traits based on user behavoirs of using microblog and a model-based multivariate time series clustering algorithm.

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vSketchDLC: A Sketch on distributed Deep Learning Communication via Fine-grained Tracing Visualization 18th

vSketchDLC: A Sketch on Distributed Deep Learning Communica...

引用

18th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2021

作者： Wang, Yanghai Ouyang, Shuo Dong, Dezun Yu, Enda Liao, Xiangke College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9783030935702

Intensive communication cost for gradients and parameters is becoming the bottleneck of distributed deep learning training. It is crucial for optimizing such communication bottleneck through measuring communication operations effectively. However, many existing communication measurement tools, such as MXNet profiler, still suffer from serious limitations. Specifically, they cannot satisfy two requirements simultaneously, that is, fine-grained collection of low-level communication operations and user-friendly analysis of comprehensive measurement results. In this paper, we make the first attempt to propose an open-sourced, fine-grained and user-friendly communication measurement tool on top of MXNet, called vSketchDLC. vSketchDLC can trace low-level communication events between framework and communication library interface, and capture end-to-end push and pull communications between workers and servers. It supports to generate communication records in standard format, enabling users to analyze the communication traces by merely using standard visualization tools such as Chrome Trace Viewer. Our design exploits in-memory buffers and asynchronous record writes to ensure measurement activities do not impact training performance. We conduct extensive experiments on a public-cloud GPU cluster to verify the effectiveness of vSketchDLC for MXNet. Experimental results show that vSketchDLC can empower users to analyze fine-grained communication records through friendly interactions, and identify potential training bottlenecks from multiple perspectives, including training timeline and iterations, DNN layers, workers or servers, etc. We can observe the relationship between different communications visually, i.e., to highlight a selected period of communication traces, to zoom in or zoom out, such that identifying the root causes of communication bottleneck and seeking to improve training performance. © 2022, IFIP international Federation for Information Processing.

关键词： Deep learning

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Application of Ontologies in Peer-to-Peer Database with Routing Queries Based on Ant Colony Algorithm

Application of Ontologies in Peer-to-Peer Database with Rout...

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IEEE international conference on parallel and distributed computing, Applications and Technologies (PDCAT)

作者： Carlos Roberto Valêncio Leandro Rincon Costa Carlos Henrique El Hetti Laurenti Adriano Mauro Cansian Depto. de Ciências de Computação e Estatística Universidade Estadual Paulista-Unesp São José do Rio Preto Brazil

A significant set of information stored in different databases around the world, can be shared through peer-to-peer databases. With that, is obtained a large base of knowledge, without the need for large investments because they are used existing databases, as well as the infrastructure in place. However, the structural characteristics of peer-to-peer, makes complex the process of finding such information. On the other side, these databases are often heterogeneous in their schemas, but semantically similar in their content. A good peer-to-peer databases systems should allow the user access information from databases scattered across the network and receive only the information really relate to your topic of interest. this paper proposes to use ontologies in peer-to-peer database queries to represent the semantics inherent to the data. the main contribution of this work is enable integration between heterogeneous databases, improve the performance of such queries and use the algorithm of optimization Ant Colony to solve the problem of locating information on peer-to-peer networks, which presents an improve of 18% in results.

关键词： Ontologies Peer to peer computing Semantics Routing distributed databases Cancer

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A Spark-based distributed Whale Optimization Algorithm for Feature Selection

A Spark-based Distributed Whale Optimization Algorithm for F...

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IEEE international conference on Intelligent Data Acquisition and Advanced computing systems

作者： Hongwei Chen Zhou Hu Lin Han Qiao Hou Zhiwei Ye Jiansen Yuan Jun Zeng School of Computer Science Hubei University of Technology Wuhan Fiberhome Technical Services Co. Ltd.

ISBN: (纸本)9781728140704

With the rapid development of the Internet and big data technologies, high-dimensional data generated in various fields has increased dramatically. Feature selection is an effective way to solve data processing problems caused by high dimensionality and high computational complexity. the traditional feature selection method shows the problem of insufficient classification accuracy and low processing efficiency when dealing with high-dimensional and large-scale data. the traditional feature selection method shows low classification accuracy and low processing efficiency when dealing with high-dimensional and large-scale data. this paper proposed a feature selection method based on Whale Optimization Algorithm to learn mining feature selection rules, then improve the accuracy of feature selection. However, when the data size is very large, the efficiency of single node execution is low. therefore, this paper combined the Whale Optimization Algorithm with the parallel computing model of the Spark platform, and proposed a feature selection method based on the Spark platform for distributed Whale Optimization Algorithm. the results showed that the excellent result search ability of the Whale Optimization Algorithm combined with the distributed and efficient calculation speed can realize the efficient solution of the feature selection optimization model.

关键词： Feature selection Whale optimization algorithm Spark platform parallel computing Feature extraction selection methods parallel PROCESSING (COMPUTERS) Optimization algorithms Whale data size Distribute

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Scalable Data parallel Approaches to Anomaly Detection in Climate Data using Gaussian Processes

Scalable Data Parallel Approaches to Anomaly Detection in Cl...

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international conference on Machine Learning and Applications (ICMLA)

作者： Krishna Karthik Gadiraju Bharathkumar Ramachandra Ashwin Shashidharan Benjamin Dutton Ranga Raju Vatsavai Department of Computer Science North Carolina State University

Anomaly detection on large scale spatio-temporal data such as climate data is a challenging task depending on the spatial and temporal resolution and autocorrelation of the data. When considering global gridded daily temperature data, the number of locations and the length of time period considered makes anomaly detection a big data problem. Gaussian Process (GP) Learning is a method that is well-suited to identify the complex spatial and temporal autocorrelation properties of spatio-temporal data. One of the primary challenges with using GP is the computational complexity associated with inverting a covariance matrix. this is further compounded when considering data on a national/global scale and performing anomaly detection using such methods often requires dedicated high performance computing platforms. In this paper, we describe a purely temporal scalable anomaly detection technique for gridded temperature data based on GP Learning that ignore the spatial autocorrelation between neighboring grids and perform anomaly detection on each of the grids in parallel, thereby reducing the execution time. We introduce three methods: a standalone data parallel approach using a single GPU, a distributed memory version on multi-node clusters using MPI, and a mixed parallel approach using multiple GPUs. In comparison to a sequential approach, they are 17.2x, 47.1x, and 88.9x faster, respectively.

关键词： Time series analysis Anomaly detection Graphics processing units Scalability Ground penetrating radar Temperature distribution Covariance matrices

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