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Workload Characteristic Oriented Scheduler for MapReduce

Workload Characteristic Oriented Scheduler for MapReduce

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international conference on parallel and distributed systems (ICPADS)

作者： Peng Lu Young Choon Lee Chen Wang Bing Bing Zhou Junliang Chen Albert Y. Zomaya Center for Distributed and High Performance Computing The University of Sydney NSW Australia CSIRO ICT Center Epping NSW Australia

Applications in many areas are increasingly developed and ported using the Map Reduce framework (more specifically, Hadoop) to exploit (data) parallelism. the application scope of Map Reduce has been extended beyond the original design goal which was large-scale data processing. this extension inherently makes a need for scheduler to explicitly take into account characteristics of job for two main goals of efficient resource use and performance improvement. In this paper, we study Map Reduce scheduling strategies to effectively deal with different workload characteristics CPU intensive and I/O intensive. We present the Workload Characteristic Oriented Scheduler (WCO), which strives for co-locating tasks of possibly different Map Reduce jobs with complementing resource usage characteristics. WCO is characterized by its essentially dynamic and adaptive scheduling decisions using information obtained from its characteristic estimator. Workload characteristics of tasks are primarily estimated by sampling with the help of some static task selection strategies, e.g., Java byte code analysis. Results obtained from extensive experiments using 11 benchmarks in a 4-node local cluster and a 51-node Amazon EC2 cluster show 17% performance improvement on average in terms of throughput in the situation of co-existing diverse workloads.

关键词： Benchmark testing Heart beat throughput Resource management Estimation Dynamic scheduling

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Performance Optimization of a parallel, Two Stage Stochastic Linear Program

Performance Optimization of a Parallel, Two Stage Stochastic...

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international conference on parallel and distributed systems (ICPADS)

作者： Akhil Langer Ramprasad Venkataraman Udatta Palekar Laxmikant Kale Steven Baker Dept of Computer Science University of Illinois at Urbana-Champaign College of Business University of Illinois at Urbana-Champaign MITRE Corporation

Stochastic optimization is used in several high impact contexts to provide optimal solutions in the face of uncertainties. this paper explores the parallelization of two-stage stochastic resource allocation problems that seek an optimal solution in the first stage, while accounting for sudden changes in resource requirements by evaluating multiple possible scenarios in the second stage. Unlike typical scientific computing algorithms, linear programs (which are the individual grains of computation in our parallel design) have unpredictable and long execution times. this confounds both a priori load distribution as well as persistence-based dynamic load balancing techniques. We present a master-worker decomposition coupled with a pull-based work assignment scheme for load balance. We discuss some of the challenges encountered in optimizing both the master and the worker portions of the computations, and techniques to address them. Of note are cut retirement schemes for balancing memory requirements with duplicated worker computation, and scenario clustering for accelerating the evaluation of similar scenarios. We base our work in the context of a real application: the optimization of US military aircraft allocation to various cargo and personnel movement missions in the face of uncertain demands. We demonstrate scaling up to 122 cores of an intel 64 cluster, even for very small, but representative datasets. Our decision to eschew problem-specific decompositions has resulted in a parallel infrastructure that should be easily adapted to other similar problems. Similarly, we believe the techniques developed in this paper will be generally applicable to other contexts that require quick solutions to stochastic optimization problems.

关键词： Resource management Aircraft Optimization Stochastic processes Military aircraft Atmospheric modeling Computational modeling

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Exciter Architecture for Digital Sound and Video Broadcasting Transmitters

Exciter Architecture for Digital Sound and Video Broadcastin...

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international conference on Applied Electromagnetics and Communications (ICECom)

作者： T. Grubesa S. Grubesa H. Domitrovic RIZ Transmitters Company Zagreb Croatia Faculty of Electrical Engineering and Computing University of Zagreb Croatia

Modern radio-communications have begun the transition process from analog to digital radio-communications and broadcasting. During this transition process, main changes are required in exciter units of transmitters. the architecture of transmitters themselves is the same for both digital and analog broadcasting, but the exciters used for digital broadcasting have to create analog RF exciting signals for the transmitters from the digital data stream. the most common modulation procedure used in digital broadcasting systems is OFDM with QPSK or some kind of QAM keying of sub carriers. Apart from RF signal generation, the exciters are often responsible for control of the whole system, because control data is often included in the digital stream distributed from the broadcast center to the transmitters

关键词： Digital video broadcasting Radio transmitters Control systems Radio frequency RF signals Digital modulation OFDM modulation Quadrature phase shift keying Quadrature amplitude modulation Signal generators

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the Deep Data Warehouse: Link-Based Integration and Enrichment of Warehouse Data and Unstructured Content

The Deep Data Warehouse: Link-Based Integration and Enrichme...

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international conference on Enterprise distributed Object computing (EDOC)

作者： Christoph Gröger Holger Schwarz Bernhard Mitschang Institute of Parallel and Distributed Systems University of Stuttgart Stuttgart Germany

ISBN: (纸本)9781479954711

Data warehouses are at the core of enterprise IT and enable the efficient storage and analysis of structured data. Besides, unstructured content, e.g., emails and documents, constitutes more than half of the entire enterprise data and contains a lot of implicit knowledge about warehouse entities. thus, holistic ana-lytics require the integration of structured warehouse data and unstructured content to generate novel insights. these insights can also be used to enrich the integrated data and to create a new basis for further analytics. Existing integration approaches only support a limited range of analytical applications and require the costly adaptation of the warehouse schema. In this paper, we present the Deep Data Warehouse (DeepDWH), a novel type of data warehouse based on the flexible integration and enrichment of warehouse data and unstructured content, addressing the variety challenge of Big Data. It relies on information-rich in-stance-level links between warehouse elements and content items, which are represented in a graph-oriented structure. Neither adaptations of the existing warehouse nor the design of an overall federated schema are required. We design a conceptual linking model and develop a logical schema for links based on a property graph. As a proof of concept, we present a prototypical imple-mentation of the DeepDWH including a link store based on a graph database.

关键词： Data models Joining processes Analytical models Data warehouses Unified modeling language Data mining

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

DSANA: A distributed machine learning acceleration solution ...

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

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

ISBN: (纸本)9781728176505

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.

关键词： Training Machine learning algorithms Processor scheduling Training data Computer architecture Machine learning Dynamic scheduling

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Leveraging Cloud Infrastructure for Troubleshooting Edge computing systems

Leveraging Cloud Infrastructure for Troubleshooting Edge Com...

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international conference on parallel and distributed systems (ICPADS)

作者： Michael Fagan Mohammad Maifi Hasan Khan Bing Wang Department of CSE University of Connecticut USA University of Connecticut Storrs CT US

Modern cloud-based applications (e.g., Face book, Dropbox) serve a wide range of edge clients (e.g., laptops, smart phones). the clients' characteristics vary significantly in terms of hardware (e.g., high end desktop vs. resource constrained smart phones), operating systems (e.g., Linux, Android, Mac OS, Windows), network connections (e.g., wireless vs. wired, 3G vs. 2G), and software versions (e.g., Firefox 12 vs. Firefox 13), just to name a few. Unfortunately, due to misconfiguration, outdated software, faulty hardware, or other reasons, many edge systems operate at suboptimal performance. Poor performance and root cause identification is extremely challenging for the client of the cloud system. To address this challenge, the troubleshooting service presented in this paper leverages such heterogeneity to identify and debug performance problems on edge devices. First, by looking at many runs across many different clients, the service groups clients in different clusters based on performance. Next, the service enables logging on remote clients to collect run time traces, and subsequently identifies the root cause by analyzing logs automatically. We leverage high level features such as machine/OS type along with more low level kernel level statistics such as I/O rate and system calls. To demonstrate our system we first introduce a configuration bug that was artificially injected in a recently built cluster by changing the TCP buffer size. Next, we present two real-life bugs, one I/O inefficiency bug relating to network transfers on Android, and another misconfiguration bug in VirtualBox, that were identified using our tool.

关键词： Software Servers Smart phones Performance evaluation Computer bugs Engines Monitoring

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A General Framework for Broadcasting in Static to Highly Mobile Wireless Ad hoc, Sensor, Robot and Vehicular Networks

A General Framework for Broadcasting in Static to Highly Mob...

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international conference on parallel and distributed systems (ICPADS)

作者： Ivan Stojmenovic SITE University of Ottawa Ontario Canada

In a broadcasting task, source node wants to send the same message to all the other nodes in the network. Existing solutions address specific mobility scenarios, e.g. connected dominating set (CDS) based for static networks, blind flooding for moderate mobility, hyper flooding for highly mobile and frequently partitioned networks. We are interested in designing a unique protocol that will seamlessly (without using any parameter) adjust itself to any mobility scenario, and with capability to address various model assumptions and optimality criteria. Existing approaches for all scenarios are based on some threshold parameters (e.g. speed) to locally select among different algorithms, and therefore different nodes may run different algorithms. Here we describe a novel general BSM (Broadcasting from Static to Mobile) framework, built over several recent algorithms handling special cases. It aims at high delivery rate with low message cost, and addresses intermittent connectivity and delay minimization. Each node activates with respect to broadcast message whenever it identifies one or more neighbors in need of the message. It selects, upon activation, waiting time (dynamically adjusted with reception of any message) depending on the number of such neighbors, distance to their centroid, and its CDS membership. It competes (at MAC layer) to retransmit at timeout expiry if it still believes that a neighbor needs message. We map this algorithm to variety of multi-hop wireless networks scenarios, with and without: positional information, acknowledgments, and time criticality goal. Some existing solutions are derived as special cases, and we also show how to timely deliver warnings in vehicular networks with arbitrary road structure, without using road maps.

关键词： Protocols Broadcasting Mobile communication Reliability Floods Vehicles Mobile computing

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Implementing Molecular Dynamics Simulation on Sunway TaihuLight System

Implementing Molecular Dynamics Simulation on Sunway TaihuLi...

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IEEE international conference on High Performance computing and Communications

作者： Wenqian Dong Letian Kang Zhe Quan Kenli Li Keqin Li Ziyu Hao Xiang-Hui Xie College of Information Science and Engineering Hunan University Changsha Hunan 410082 China Department of Computer Science State University of New York New Paltz NY 12561 USA Jiangnan Institute of Computing Technology Wuxi Jiangsu 214000 China

ISBN: (纸本)9781509042982

Molecular dynamics (MD) simulation is a common tool to study the physical movements of atoms and molecules in many research fields. However, it is an extremely time-consuming application which takes researchers weeks or months to run a single simulation when simulation size scales up and computing demands keep growing. In this paper, an improved MD implementation on Sunway TaihuLight supercomputer is developed to solve the above mentioned issues. the new implementation is extended from an existing implementation (i.e., LAMMPS) which widely uses the MD application. Sunway TaihuLight is a heterogeneous supercomputer with a fully customized integration approach and a brand new many-core processor SW26010. the Sunway TaihuLight takes the world's first place with peak performance over 100PFlops. We propose an optimization method of the MD simulation in three steps: paralleled extensions to SW26010, memory-access optimizations, and vectorization. After the optimization process, an 8x speedup is achieved on a single computing node. Superiorly, we also scale up to 95 thousands computing nodes (6 millions cores) with an almost leaner speedup. Besides, the proposed methods can also be applied to many other molecular dynamics codes, or similar applications.

关键词： Sunway TaihuLignt Molecular Dynamics Simulation High performance computing parallel distributed computing

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Performance of the Modern parallel Programming Approaches: A Case Study

Performance of the Modern Parallel Programming Approaches: A...

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international Computer Sciences and Information Technologies (CSIT)

作者： Volodymyr Fedynyak Oleksa Hryniv Bohdan Vey Oleg Farenyuk Faculty of Applied Sciences Ukrainian Catholic University L’viv Ukraine Institute for Condensed Matter Physics of the National Academy of Sciences of Ukraine L’viv Ukraine

this research is devoted to a quantitative comparison of the performance of several parallel programming approaches and compares their computational performance. Comparison is performed for the Computational Dynamics Problem solved by the MacCormack scheme. parallel computation properties of this sample problem task are well-understood. the parallel programming techniques were chosen considering the recent trends in high-performance computing. Both high-level framework-based implementations (using OneAPI DPC++ and ArrayFire) and low-level implementations (based on CUDA C++) are reviewed and their performance is compared. Additionally, single SMP systems with multiple CUDA-capable GPUs were studied using GPUDirect and Unified Memory technologies. Wall-time was used as a performance metric. the comparison was performed using Student’s t-test for the Gauss-distributed experimental results and the non-parametric Wilcoxon signed-rank test – for other distributions. the results show that CUDA-based solutions outperform other approaches though development time considerations often can favor more high-level approaches.

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Two-Level Result Caching for Web Search Queries on Structured P2P Networks

Two-Level Result Caching for Web Search Queries on Structure...

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international conference on parallel and distributed systems (ICPADS)

作者： Erika Rosas Nicolas Hidalgo Mauricio Marin Yahoo! Labs Santiago Chile

this paper proposes a two-level caching strategy for Web search queries which is devised to operate on P2P networks. the aim is to significantly reduce query traffic going from a large community of users to commercial search engines by placing between them a P2P caching service capable of storing and efficiently distributing frequent queries among users. the proposed design takes into consideration the highly dynamic nature of user queries both in traffic intensity and drastic shifts in user interest which are both usually driven by unpredictable world-wide events. Each peer maintains a LRU result cache (RCache) used to keep the answers for queries originated in the peer itself and queries for which the peer is responsible for by contacting on-demand a Web search engine to get the query answers. When query traffic is predominantly routed to a few responsible peers our strategy replicates the role of ``being responsible for" to neighboring peers so that they can absorb part of the traffic to restore load balance. this is a fairly slow and adaptive process that we call mid-term load balancing. To achieve a short-term fair distribution of queries we introduce in each peer a location cache (LCache) which keeps pointers to peers that have already requested the same queries in the very recent past. this lets these peers share their query answers with newly requesting peers. this process is fast as these popular queries are usually cached in the first DHT hop of a requesting peer which quickly tends to redistribute load among more and more peers. A comparative study shows that the proposed strategy achieves better load balance, significantly smaller communication volume among peers, and larger cache hit ratios than previous strategies.

关键词： Peer to peer computing Web search Engines Routing Search engines Load management Communities

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