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Increasing the Throughput of Pipe-and-Filter Architectures by Integrating the Task Farm parallelization Pattern 19

Increasing the Throughput of Pipe-and-Filter Architectures b...

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19th international ACM SIGSOFT symposium on Component-Based Software Engineering (CBSE)

作者： Wulf, Christian Wiechmann, Christian Claus Hasselbring, Wilhelm Univ Kiel Software Engn Grp D-24098 Kiel Germany

ISBN: (纸本)9781509025695

The Pipe-and-Filter style represents a well-known family of component-based architectures. By executing each filter on a dedicated processing unit, it is also possible to leverage contemporary distributed systems and multi-core systems for a high throughput. However, this simple parallelization approach is not very effective when (1) the workload is uneven distributed over all filters and when (2) the number of available processing units exceeds the number of filters. In the first case, parallelizing all filters can lead to a waste of resources since only the slowest filter is responsible for the overall throughput. In the second case, some processing units remain unused. In this paper, we present an automatic parallelization approach providing high throughput and utilizing the available processing units. Our main idea is to provide a composite filter that is wrapped around an existing filter to increase its throughput. We call this composite filter the Task Farm Filter since it implements the Task Farm parallelization pattern. It creates and executes multiple instances of the underlying filter in parallel. Moreover, we present a modular, self-adaptive mechanism that automatically adapts the number of instances at runtime to achieve the highest possible throughput. Finally, we present an extensive experimental evaluation of our self-adaptive task farm filter by employing a CPU-intensive, an I/O-intensive, and a hybrid scenario. The evaluation shows that our task farm automatically parallelize the underlying filter and thus increases the overall throughput. Furthermore, the evaluation shows that our task farm scales well with the workload of the executed Pipe-and-Filter architecture.

关键词： parallel processing Software performance Software architecture

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SDS-sort: Scalable dynamic skew-aware parallel sorting 16

SDS-sort: Scalable dynamic skew-aware parallel sorting

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25th ACM international symposium on High-Performance parallel and distributed Computing, HPDC 2016

作者： Dong, Bin Byna, Surendra Wu, Kesheng Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States

ISBN: (纸本)9781450343145

parallel sorting is an essential algorithm in large-scale data analytics using distributed memory systems. As the number of processes increases, existing parallel sorting algorithms could become inefficient because of the unbalanced work-load. A common cause of load imbalance is the skewness of data, which is common in application data sets from physics, biology, earth and planetary sciences. In this work, we introduce a new scalable dynamic skew-aware parallel sorting algorithm, named SDS-Sort. It uses a skew-aware partition method to guarantee a tighter upper bound on the work-load of each process. To improve load balance among parallel processes, existing algorithms usually add extra variables to the sorting key, which increase the time needed to complete the sorting operation. SDS-Sort allows a user to select any sorting key without sacrificing performance. SDS-Sort also provides optimizations, including adaptive local merging, overlapping of data exchange and data processing, and dynamic selection of data processing algorithms for different hardware configurations and for partially ordered data. SDS-Sort uses local-sampling based partitioning to further reduce its overhead. We tested SDS-Sort extensively on Edison, a Cray XC30 supercomputer. Timing measurements show that SDS-Sort can scale to 130K CPU cores and deliver a sorting throughput of 117TB/min. In tests with real application data from large science projects, SDS-Sort out-performs HykSort, a state-of-art parallel sorting algorithm, by 3:4X. Copyright © 2016 by the Association for Computing Machinery, Inc. (ACM).

关键词： Sorting

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proceedings of the 2016 Spring Simulation Multiconference - TMS/DEVS symposium on Theory of Modeling and Simulation, TMS/DEVS 2016

Proceedings of the 2016 Spring Simulation Multiconference - ...

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2016 TMS/DEVS symposium on Theory of Modeling and Simulation, TMS/DEVS 2016, Part of the 2016 Spring Simulation Multiconference, SpringSim 2016

The proceedings contain 29 papers. The topics discussed include: coupling Petri nets with deterministic formalisms using co-simulation;integration of UML models in FMI-based co-simulation;hybrid co-simulation of FMUS using DEV&DESS in MECSYCO;simulation fidelity distance: a game-theoretic framework;DEVS execution acceleration with machine learning;data assimilation in discrete event simulations - a rollback based sequential Monte Carlo approach;a kernel for embedded systems development and simulation using the boost library;a model driven approach to web-based traffic simulation;towards an efficient high-level modeling of heterogeneous image processing systems;time-parallel multi-delay logic simulation;a quantum of continuous simulated time;an aspect oriented framework to applying Markov chain Monte Carlo methods with dynamic models;scheduling predictability in I-DEVS by schedulability analysis;parsing and model generation for biological processes;and DEVS distributed modeling framework - a parallel DEVS implementation via microservices.

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Doctoral symposium: Design and development of high performance, scalable content based publish subscribe system 10

Doctoral symposium: Design and development of high performan...

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10th ACM international Conference on distributed and Event-Based systems, DEBS 2016

作者： Shah, M.A. Kulkarni, D.B. Walchand College of Engineering Vishrambag Opposite post office Sangli India

ISBN: (纸本)9781450340212

In many application scenarios, content based pub/sub systems are required to provide stringent service guarantees such as reliable delivery, high performance in terms of throughput and low latency for event notification to interested subscribers. Matching algorithm play a critical role in content based pub/sub systems. The aim of our work is design and development of parallel, scalable and high performance content based publish subscribe system. We parallelize event processing using thread based and multi GPU approaches. We achieved low latency and high throughput when pub/sub is deployed on Apache Storm, a real time event processing system. Throughput gain and reduction in matching time is nearly 48% and 40% respectively in multi GPGPU approach of event processing compared to earlier work mentioned in [1]. © 2016 ACM.

关键词： Throughput

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SWAT: A programmable, in-memory, distributed, high-performance computing platform 16

SWAT: A programmable, in-memory, distributed, high-performan...

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25th ACM international symposium on High-Performance parallel and distributed Computing, HPDC 2016

作者： Grossman, Max Sarkar, Vivek Rice University 6100 Main St HoustonTX United States

ISBN: (纸本)9781450343145

The field of data analytics is currently going through a renaissance as a result of ever-increasing dataset sizes, the value of the models that can be trained from those datasets, and a surge in exible, distributed programming models. In particular, the Apache Hadoop [1] and Spark [5] programming systems, as well as their supporting projects (e.g. HDFS, SparkSQL), have greatly simplified the analysis and transformation of datasets whose size exceeds the capacity of a single machine. While these programming models facilitate the use of distributed systems to analyze large datasets, they have been plagued by performance issues. The I/O performance bottlenecks of Hadoop are partially responsible for the creation of Spark. Performance bottlenecks in Spark due to the JVM object model, garbage collection, interpreted/-managed execution, and other abstraction layers are responsible for the creation of additional optimization layers, such as Project Tungsten [4]. Indeed, the Project Tungsten issue tracker states that the "majority of Spark workloads are not bottlenecked by I/O or network, but rather CPU and memory" [20]. In this work, we address the CPU and memory performance bottlenecks that exist in Apache Spark by accelerating user-written computational kernels using accelerators. We refer to our approach as Spark With Accelerated Tasks (SWAT). SWAT is an accelerated data analytics (ADA) framework that enables programmers to natively execute Spark applications on high performance hardware platforms with co-processors, while continuing to write their applications in a JVM-based language like Java or Scala. Runtime code generation creates OpenCL kernels from JVM bytecode, which are then executed on OpenCL accelerators. In our work we emphasize 1) full compatibility with a modern, existing, and accepted data analytics platform, 2) an asynchronous, event-driven, and resource-aware runtime, 3) multi-GPU memory management and caching, and 4) ease-of-use and programmability. Ou

关键词： Graphics processing unit

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Non-Intrusive Monitoring of Stream Processing Applications

Non-Intrusive Monitoring of Stream Processing Applications

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10th IEEE international symposium on Service-Oriented System Engineering (IEEE SOSE)

作者： Voegler, Michael Schleicher, Johannes M. Inzinger, Christian Nickel, Bernhard Dustdar, Schahram TU Wien Distributed Syst Grp Vienna Austria Univ Zurich Seal CH-8006 Zurich Switzerland

ISBN: (纸本)9781509022533

Stream processing applications have emerged as a popular way for implementing high-volume data processing tasks. In contrast to traditional data processing models that persist data to databases and then execute queries on the stored data, stream processing applications continuously execute complex queries on incoming data to produce timely results in reaction to events observed in the processed data. To cope with the request load, components of a stream processing application are usually distributed across multiple machines. In this context, performance monitoring and testing are naturally important for stakeholders to understand as well as analyze the runtime characteristics of deployed applications to identify issues and inform decisions. Existing approaches for monitoring the performance of distributed systems, however, do not provide sufficient support for targeted monitoring of stream processing applications, and require changes to the application code to enable the integration of application-specific monitoring data. In this paper we present MOSAIC, a service oriented framework that allows for in-depth analysis of stream processing applications by non-intrusively adding functionality for acquiring and publishing performance measurements at runtime, to the application. Furthermore, MOSAIC provides a flexible mechanism for integrating different stream processing frameworks, which can be used for executing and monitoring applications independent from a specific operator model. Additionally, our framework provides an extensible approach for gathering and analyzing measurement data. In order to evaluate our solution, we developed a scenario application, which we used for testing and monitoring its performance on different stream processing engines.

关键词： AOP Non-intrusive monitoring Service oriented framework Stream processing applications

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Wiera: Towards flexible multi-tiered geo-distributed cloud storage instances 16

Wiera: Towards flexible multi-tiered geo-distributed cloud s...

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25th ACM international symposium on High-Performance parallel and distributed Computing, HPDC 2016

作者： Oh, Kwangsung Chandra, Abhishek Weissman, Jon Department of Computer Science and Engineering University of Minnesota Twin Cities MinneapolisMN55455 United States

ISBN: (纸本)9781450343145

Geo-distributed cloud storage systems must tame complexity at many levels: uniform APIs for storage access, supporting exible storage policies that meet a wide array of application metrics, handling uncertain network dynamics and access dynamism, and operating across many levels of heterogeneity both within and across data-centers. In this paper, we present an integrated solution called Wiera. Wiera extends our earlier cloud storage system, Tiera, that is targeted to multi-tiered policy-based single cloud storage, to the wide-area and multiple data-centers (even across different providers). Wiera enables the specification of global data management policies built on top of local Tiera policies. Such policies enable the user to optimize for cost, performance, reliability, durability, and consistency, both within and across data-centers, and to express their tradeoffs. A key aspect of Wiera is first-class support for dynamism due to network, workload, and access patterns changes. Wiera policies can adapt to changes in user workload, poorly performing data tiers, failures, and changes in user metrics (e.g., cost). Wiera allows unmodified applications to reap the benefits of exible data/storage policies by externalizing the policy specification. As far as we know, Wiera is the first geo-distributed cloud storage system which handles dynamism actively at run-time. We show how Wiera enables a rich specification of dynamic policies using a concise notation and describe the design and implementation of the system. We have implemented a Wiera prototype on multiple cloud environments, AWS and Azure, that illustrates potential benefits from managing dynamics and in using multiple cloud storage tiers both within and across data-centers. Copyright © 2016 by the Association for Computing Machinery, Inc. (ACM).

关键词： distributed cloud

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proceedings of the 42nd international Conference on Very Large Data Bases, VLDB June 2016

Proceedings of the 42nd International Conference on Very Lar...

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42nd international Conference on Very Large Data Bases, VLDB 2016

This proceedings contains 11 papers. The VLDB 2016 conference covers issues in data management, database and information systems research, since they are the technological cornerstones of the emerging applications. Topics in this conference include: distributed Data Platforms, including Cloud Data systems, Key Value stores, Big Data systems;Transaction Processing, Query Processing, Indexing and Storage, Meta-data management, Self-Tuning technology;Specialized Data Architectures including Data Streams and IOT architectures, Cold Storage, and Hardware-Software Co-design;Data Cleaning, Data Analytics, and Data Integration;Information Retrieval and Data Mining;Applications including Social Networks, Crowdsourcing, and E-commerce systems;User and Application Programming Interfaces to Data Platforms, including keyword queries, declarative and imperative languages;Benchmarking, Tuning, and Testing, etc. It also provides definitive evaluations of an established corpus of solutions to industrial-strength problems [Experiments and Analyses], which also focuses in the principled empirical evaluation of key data systems and their components, including that of specific database algorithms, and describes novel systems and applications [Innovative systems and Applications]. The key terms of this proceedings include timestamps, multi-tenant parallel databases, WarpLDA, functional dependencies and keys, Rewired User-space Memory Access (RUMA), WiSeDB, RDF querying, syntactic string transformations, method for error-tolerant autocompletion, multi-tenant workloads.

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A Neural Network-based Appliance Scheduling Methodology for Smart Homes and Buildings with Multiple Power Sources 2

A Neural Network-based Appliance Scheduling Methodology for ...

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2nd IEEE international symposium on Nanoelectronic and Information systems (IEEE iNIS)

作者： Shukla, Raj Mani Kansakar, Prasanna Munir, Arslan Univ Nevada Dept Comp Sci & Engn Parallel & Distributed Comp & Embedded Syst Lab Reno NV 89557 USA

ISBN: (纸本)9781509061709

The increased production of electrical energy from various power sources, such as solar, wind, and nuclear, allows smart buildings to be connected with multiple power sources. In an effort to conserve environment, electrical energy usage is gradually shifting towards renewable and green energy sources, such as wind, hydro, and solar. Regardless of power sources, a user demands for continuous energy supply and also desires to minimize the electricity bill. Further, in a dynamic pricing environment, the price of electricity varies throughout the day. In such a dynamic pricing environment, appliance scheduling with multiple power sources tied to a smart home/building is an important research problem. In this paper, we propose a methodology to abet green environment by prioritizing green energy sources and to minimize the electricity cost for the user. Our proposed methodology leverages a smart grid architecture which employs a greedy strategy to select the most feasible power source amongst the available power sources tied to a smart home/building. Our proposed methodology further leverages a neural network-based approach for appliance scheduling that optimizes the use of power sources in a dynamic pricing environment to minimize the total cost of electricity usage.

关键词： Smart grid renewable and non-renewable energy sources appliance scheduling Boltzmann machine

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GraQL: A Query Language for High-Performance Attributed Graph databases

GraQL: A Query Language for High-Performance Attributed Grap...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Daniel Chavarría-Miranda Vito Giovanni Castellana Alessandro Morari David Haglin John Feo Physical and Computational Sciences Directorate Pacific Northwest National Laboratory (PNNL) Richland WA

ISBN: (纸本)9781509036837

Graph databases are becoming a critical tool for the analysis of graph-structured data in the context of multiple scientific and technical domains, including cybersecurity and computational biology. In particular, the storage, analysis and querying of attributed graphs is a very important capability. Attributed graphs contain properties attached to the vertices and edges of the graph structure. Queries over attributed graphs do not only include structural pattern matching, but also conditions over the values of the attributes. In this work, we present GraQL, a query language designed for high-performance attributed graph databases hosted on a high memory capacity cluster. GraQL is designed to be the front-end language for the attributed graph data model for the GEMS database system.

关键词： Data models distributed databases Database languages Database systems Computer security Biological system modeling

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