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International Conference on Signals and Electronic Systems (ICSES)

作者： Talaska, Tomasz Dlugosz, Rafal UTP Univ Sci & Technol Fac Telecommun Comp Sci & Elect Engn Bydgoszcz Poland Apt Serv Poland SA Krakow Poland

ISBN: (纸本)9781538667682

The paper presents a novel non-linear median filter operating in the current mode. The filter consists of an analog and a digital part. A first layer of the analog part converts particular input signals, represented by currents, into 1-bit digital signals delayed by time periods inversely proportional to the values of the currents. The second block of the analog part of the system is a multi-branch resistive voltage divider, switched over by described digital signals. A digital block of the filter is used to determine the address of the input signal that in a given calculation cycle has a median value among all signal samples. The circuit was designed in the 180 nm CMOS technology and verified by comprehensive HSpice simulations. For the input currents up to 50 nA it offers data rate of 100 kSamples/s, dissipating power of 100 W.

关键词： Nonlinear filters median filter CMOS technology parallel data processing asynchronous circuits current mode

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Meta-dataflows: Efficient Exploratory dataflow Jobs 18

Meta-Dataflows: Efficient Exploratory Dataflow Jobs

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44th ACM SIGMOD International Conference on Management of data

作者： Fernandez, Raul Castro Culhane, William Watcharapichat, Pijika Weidlich, Matthias Morales, Victoria Lopez Pietzuch, Peter MIT Cambridge MA 02139 USA Imperial Coll London London England Humboldt Univ Berlin Germany

ISBN: (纸本)9781450317436

Distributed dataflow systems such as Apache Spark and Apache Flink are used to derive new insights from large datasets. While they efficiently execute concrete data processing workflows, expressed as dataflow graphs, they lack generic support for exploratory workflows: if a user is uncertain about the correct processing pipeline, e.g. in terms of data cleaning strategy or choice of model parameters, they must repeatedly submit modified jobs to the system. This, however, misses out on optimisation opportunities for exploratory workflows, both in terms of scheduling and memory allocation. We describe meta-dataflows (MDFs), a new model to effectively express exploratory workflows and efficiently execute them on compute clusters. With MDFs, users specify a family of dataflows using two primitives: (a) an explore operator automatically considers choices in a dataflow;and (b) a choose operator assesses the result quality of explored dataflow branches and selects a subset of the results. We propose optimisations to execute MDFs: a system can (i) avoid redundant computation when exploring branches by reusing intermediate results and discarding results from underperforming branches;and (ii) consider future data access patterns in the MDF when allocating cluster memory. Our evaluation shows that MDFs improve the runtime of exploratory workflows by up to 90% compared to sequential execution.

关键词： distributed data processing parameter space exploration exploratory workflows parallel data processing dataflow

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BoxBroker: A Policy-Driven Framework for Optimizing Storage Service Federation

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2018年第1期12卷 340-367页

作者： Heinsen, Rene Lopez, Cindy Huh, Eui-Nam Kyung Hee Univ Dept Comp Engn & Sci 1732 Deogyeong Daero Yongin 17104 Gyeonggi Do South Korea

Storage services integration can be done for achieving high availability, improving data access performance and scalability while preventing vendor lock-in. However, multiple services environment management and interoperability have become a critical issue as a result of service architectures and communication interfaces heterogeneity. Storage federation model provides the integration of multiple heterogeneous and self-sufficient storage systems with a single control point and automated decision making about data distribution. In order to integrate diverse heterogeneous storage services into a single storage pool, we are proposing a storage service federation framework named BoxBroker. Moreover, an automated decision model based on a policy-driven data distribution algorithm and a service evaluation method is proposed enabling BoxBroker to make optimal decisions. Finally, a demonstration of our proposal capabilities is presented and discussed.

关键词： Storage federation data distribution Policy-driven Log analysis parallel data processing Service evaluation

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Fast iterative circuits and RAM-based mergers to accelerate data sort in software/hardware systems

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PROCEEDINGS OF THE ESTONIAN ACADEMY OF SCIENCES 2017年第3期66卷 323-335页

作者： Sklyarov, Valery Skliarova, Iouliia Rjabov, Artjom Sudnitson, Alexander Univ Aveiro Dept Elect Telecommun & Informat IEETA Campus Univ Santiago P-3810193 Aveiro Portugal Tallinn Univ Technol Sch Informat Technol Dept Comp Syst Akad Tee 15A EE-12618 Tallinn Estonia

The paper suggests and describes two architectures for parallel data sort. The first architecture is applicable to large data sets and it combines three stages of data processing: data sorting in hardware (in a Field-Programmable Gate Arrays - FPGA), merging preliminary sorted blocks in hardware (in the FPGA), and merging large subsets received from the FPGA in general-purpose software. data exchange between the FPGA and a general-purpose computer is organized through a fast Peripheral Component Interconnect (PCI) express bus. The second architecture is applicable to small data sets and it enables sorting to be done at the time of data acquisition, i.e. as soon as the last data item is received, the sorted items can be transferred immediately. The results of experiments clearly demonstrate the advantages of the proposed architectures that permit the reduction of the required hardware resources and increasing throughput compared to the results reported in publications and software functions targeted to data sorting.

关键词： parallel data processing merging iterative networks communication-time processing Field-Programmable Gate Array (FPGA) Peripheral Component Interconnect (PCI) express bus

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Frequency-Division Multiplexing in Magnonic Logic Networks Based on Caustic-Like Spin-Wave Beams

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PHYSICA STATUS SOLIDI-RAPID RESEARCH LETTERS 2018年第12期12卷 1800409-1800409页

作者： Heussner, Frank Nabinger, Matthias Fischer, Tobias Braecher, Thomas Serga, Alexander A. Hillebrands, Burkard Pirro, Philipp Tech Univ Kaiserslautern Fachbereich Phys & Landesforschungszentrum OPTIMA D-67663 Kaiserslautern Germany

Wave-based data processing by spin waves (SW) and their quanta, magnons, is a promising technique to overcome the challenges which CMOS-based logic networks are facing nowadays. The advantage of these quasi-particles lies in their potential for the realization of energy efficient devices on the micro- to nanometer scale due to their charge-less propagation in magnetic materials. In this paper, the frequency dependence of the propagation direction of caustic-like spin-wave beams in microstructured ferromagnets is studied by micromagnetic simulations. Based on the observed alteration of the propagation angle, an approach to spatially combine and separate spin-wave signals of different frequencies is demonstrated. The presented magnetic structure constitutes a prototype design of a passive circuit enabling frequency-division multiplexing (FDM) in magnonic logic networks. It is verified that spin-wave signals of different frequencies can be transmitted through the device simultaneously without any interaction or creation of spurious signals. Due to the wave-based approach of computing in magnonic networks, the technique of FDM can be the basis for parallel data processing in single magnonic devices, enabling the multiplication of the data throughput.

关键词： frequency-division multiplexing magnonic networks parallel data processing spin-wave caustics wave-based logic

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Components of Artificial Neural Networks Realized in CMOS Technology to be Used in Intelligent Sensors in Wireless Sensor Networks

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SENSORS 2018年第12期18卷 4499-4499页

作者： Talaska, Tomasz UTP Univ Sci & Technol Fac Telecommun Comp Sci & Elect Engn PL-85796 Bydgoszcz Poland Aptiv Serv Poland SA PL-30399 Krakow Poland Al Prof S Kaliskiego 7 PL-85796 Bydgoszcz Poland

The article presents novel hardware solutions for new intelligent sensors that can be used in wireless sensor networks (WSN). A substantial reduction of the amount of data sent by the sensor to the base station in the WSN may extend the possible sensor working time. Miniature integrated artificial neural networks (ANN) applied directly in the sensor can take over the analysis of data collected from the environment, thus reducing amount of data sent over the RF communication block. A prototype specialized chip with components of the ANN was designed in the CMOS 130 nm technology. An adaptation mechanism and a programmable multi-phase clock generator-components of the ANN-are described in more detail. Both simulation and measurement results of selected blocks are presented to demonstrate the correctness of the design.

关键词： wireless sensor networks intelligent sensors artificial neural networks ASIC CMOS technology low power solutions parallel data processing

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Modeling parallel processing of databases on the Central Processor Intel Xeon Phi KNL 41

Modeling Parallel Processing of Databases on the Central Pro...

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41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO)

作者： Rekachinsky, A., I Chulkevich, R. A. Kostenetskiy, P. S. South Ural State Univ Chelyabinsk Russia

ISBN: (纸本)9789532330953

The development of parallel database management systems is an urgent problem due to the rapid information volume growth. Nowadays the basic principles of DBMS performance improvement include the use of multiprocessor systems [8]. At the same time, acceleration could be achieved by using new hardware architectures, such as hybrid clusters with manycore coprocessors. The implementation of such architectures is limited by the high cost of hardware and its configuration. Therefore, the development of models that allow determining several characteristics and comparing different database queries runtime without both using real hardware and taking into account the exact execution details is a highly topical problem. This paper describes the development of a mathematical model that explores the effectiveness of a new manycore accelerator with Intel Xeon Phi Knights Landing hardware architecture in terms of parallel database processing.

关键词： mathematical model parallel database system parallel data processing Intel Xeon Phi KNL

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Platform for parallel processing of intense experimental data flow on remote supercomputers 4th

Platform for parallel processing of intense experimental dat...

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4th International Young Scientists Conference on Computational Science (YSC)

作者： Shchapov, Vladislav Masich, Grigoriy Masich, Alexey Perm Natl Res Polytech Univ Perm Russia Inst Continuous Media Mech UB RAS Perm Russia

Modern experimental facilities generate large amounts of data that should be processed, saved to hard disk and presented to the user as fast as possible. However, in-situ data analysis requires technical resources which are often not available. The existence of accessible high-speed networks allows to forward data processing and storage to a remote supercomputer centers and datacenters. These capabilities can be realized through the development of architectural solutions for effective data transmission trough a long-distance high-speed networks, data input/ output and data distribution over computers and data storage systems. In this paper, we describe the results of investigations into the development of a software platform for parallel processing of intense experimental data-streams on ICMM UB RAS (Perm) and IMM UB RAS (Yekaterinburg) supercomputers, interconnected by a high-speed network. The reported studies was partially supported by RFBR, research project No. 1407- 96001-r_ural_a and by Program of UD RAS, project No 15-7-1-25.

关键词： Long fat network Supercomputer parallel data processing Middleware Distributed system

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Platform for parallel processing of Intense Experimental data Flow on Remote Supercomputers

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Procedia Computer Science 2015年 66卷 515-524页

作者： Vladislav Shchapov Grigoriy Masich Alexey Masich Perm National Research Polytechnic University Perm Russia Institute of Continuous Media Mechanics UB RAS Perm Russia

Modern experimental facilities generate large amounts of data that should be processed, saved to hard disk and presented to the user as fast as possible. However, in-situ data analysis requires technical resources which are often not available. The existence of accessible high-speed networks allows to forward data processing and storage to a remote supercomputer centers and datacenters. These capabilities can be realized through the development of architectural solutions for effective data transmission trough a long-distance high-speed networks, data input/output and data distribution over computers and data storage systems. In this paper, we describe the results of investigations into the development of a software platform for parallel processing of intense experimental data-streams on ICMM UB RAS (Perm) and IMM UB RAS (Yekaterinburg) supercomputers, interconnected by a high-speed network. The reported studies was partially supported by RFBR, research project No. 14-07-96001-r_ural_a and by Program of UD RAS, project No 15-7-1-25.

关键词： Long fat network Supercomputer parallel data processing Middleware Distributed system

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FP-Hadoop: Efficient processing of skewed MapReduce jobs

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INFORMATION SYSTEMS 2016年第Aug./Sep.期60卷 69-84页

作者： Liroz-Gistau, Miguel Akbarinia, Reza Agrawal, Divyakant Valduriez, Patrick INRIA Montpellier Montpellier France Univ Calif Santa Barbara Dept Comp Sci Santa Barbara CA 93106 USA

Nowadays, we are witnessing the fast production of very large amount of data, particularly by the users of online systems on the Web. However, processing this big data is very challenging since both space and computational requirements are hard to satisfy. One solution for dealing with such requirements is to take advantage of parallel frameworks, such as MapReduce or Spark, that allow to make powerful computing and storage units on top of ordinary machines. Although these key-based frameworks have been praised for their high scalability and fault tolerance, they show poor performance in the case of data skew. There are important cases where a high percentage of processing in the reduce side ends up being done by only one node. In this paper, we present FP-Hadoop, a Hadoop-based system that renders the reduce side of MapReduce more parallel by efficiently tackling the problem of reduce data skew. FP-Hadoop introduces a new phase, denoted intermediate reduce (IR), where blocks of intermediate values are processed by intermediate reduce workers in parallel. With this approach, even when all intermediate values are associated to the same key, the main part of the reducing work can be performed in parallel taking benefit of the computing power of all available workers. We implemented a prototype of FP-Hadoop, and conducted extensive experiments over synthetic and real datasets. We achieved excellent performance gains compared to native Hadoop, e.g. more than 10 times in reduce time and 5 times in total execution time. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： MapReduce data skew parallel data processing

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