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Efficient random network coding for distributed storage systems

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19th international conference on parallel Processing Workshops, Euro-Par 2013 - BigDataCloud, DIHC, FedICI, HeteroPar, HiBB, LSDVE, MHPC, OMHI, PADABS, PROPER, Resilience, ROME, and UCHPC 2013

作者： Visegrádi, Ádám Kacsuk, Péter Computer and Automation Research Institute Hungarian Academy of Sciences Hungary

ISBN: (纸本)9783642544194

Making distributed storage systems reliable is an important challenge. Simple replication may cause severe storage overhead when individual components of the system are very unreliable. Using erasure codes is a promising solution for this problem, but it presents its own challenges;it makes the design of such a system very complex, and it presents the problem of reparation. Network coding has been suggested To be used in the communication in these networks To help reduce overhead. However, using random network coding as -not besides -erasure coding would be an even more promising field To investigate;such a system would have a simple design, need little or no centralization, and reparation of the system could be much simpler than it is in other erasure coding schemes. the first step on this path is To investigate whether network coding can achieve such a performance that it is a feasible alternative To other erasure codes. this paper presents our experiences about the realization of random network coding based on the discrete logarithm of the finite field. We discuss possible performance optimizations for such a system, and provide performance measurement results focusing on data storage scenarios. © 2014 Springer-Verlag Berlin Heidelberg.

关键词： Network coding

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Task-based low-rank hybrid parallel Cholesky factorization for distributed memory environment 5

Task-based low-rank hybrid parallel Cholesky factorization f...

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5th international conference on Big-data Service and Intelligent Computation, BDSIC 2023

作者： Jiao, Han Zhang, Jilin Suzuki, Tomohiro Integrated Graduate School of Medicine Engineering and Agricultural Sciences University of Yamanashi Yamanashi Kofu Japan School of Computer Science Hangzhou Dianzi University Zhejiang Hangzhou China Department of Interdisciplinary Reseach Division of Engineering University of Yamanashi Yamanashi Kofu Japan

ISBN: (纸本)9798400708893

the primary targets for improving efficiency for large-scale matrix factorization are reducing synchronization, addressing the overlap in communication and computation, and improving load balance. In recent years, tiled algorithms with task parallelism in multicore shared memory systems have become well-established as efficient methods for conducting fine-grained computations on smaller tiles. Moreover, they provide flexible execution orders for a runtime system in many situations. However, traditional hybrid programs with MPI and OpenMP for distributed memory systems use a fork-join model for multi-threads in each process, which leads to threadparallel computing tasks interchange with sequential communication tasks. In this paper, we incorporate task parallelism and low-rank approximation into a hybrid task-based Cholesky factorization in a distributed environment and propose some low-rank variants. We evaluate the performance of our programs on both full-rank inputs and low-rank inputs and report the pros and cons of the proposed programs. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Application programming interfaces (API)

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distributed and Asynchronous Bees Algorithm Applied to Nuclear Fusion Research

Distributed and Asynchronous Bees Algorithm Applied to Nucle...

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Antonio Gómez-Iglesias Miguel A. Vega-Rodríguez Francisco Castejón Miguel Cárdenas-Montes National Fusion Laboratory CIEMAT Madrid Spain National Fusion Laboratory - CIEMAT. Avda. Complutense Spain Department of Technologies of Computers and Communications University of Extremadura Escuela Politecnica Campus Universitario s/n Cáceres Spain Department of Technologies of Computers and Communications Escuela Politécnica University of Extremadura Caceres Spain Department of Fundamental Research CIEMAT. Avda. Complutense Spain

Recently, there have been several developments in the scientific community to model and solve complex optimization problems by employing natural metaphors. In some cases, due to their distributed schema, these algorithms can be adapted to distributed computing environments. A distributed and asynchronous bees (DAB) grid-based approach is here used to optimise the magnetic configuration in order to reduce the neoclassical transport of particles in a nuclear fusion device. Large-scale problems (as several plasma physics can be classified) present open challenges that need a large computing capacity to be solved. thus, the use of large distributed infrastructures is mandatory in many of these problems. the use of grid computing offers a new paradigm where the distributed nature of foraging can be reproduced and applied to complex optimisation problems.

关键词： Equations Plasmas Optimization Mathematical model Fusion reactors Magnetic devices Electronic mail

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Scientific Workflow Scheduling with Provenance Support in Multisite Cloud 12th

Scientific Workflow Scheduling with Provenance Support in Mu...

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12th international conference on High-Performance computing for Computational Science (VECPAR)

作者： Liu, Ji Pacitti, Esther Valduriez, Patrick Mattoso, Marta INRIA Microsoft Inria Joint Ctr LIRMM Montpellier France Univ Montpellier Montpellier France Univ Fed Rio de Janeiro COPPE Rio De Janeiro Brazil

ISBN: (纸本)9783319619828;9783319619811

Recently, some Scientific Workflow Management systems (SWfMSs) with provenance support (e.g. Chiron) have been deployed in the cloud. However, they typically use a single cloud site. In this paper, we consider a multisite cloud, where the data and computing resources are distributed at different sites (possibly in different regions). Based on a multisite architecture of SWfMS, i.e. multisite Chiron, we propose a multisite task scheduling algorithm that considers the time to generate provenance data. We performed an extensive experimental evaluation of our algorithm using Microsoft Azure multisite cloud and two real-life scientific workflows (Buzz and Montage). the results show that our scheduling algorithm is up to 49,6% better than baseline algorithms in terms of total execution time.

关键词： Scientific workflow Scientific workflow management system Scheduling parallel execution Multisite cloud

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Job Scheduling with License Reservation: A Semantic Approach

Job Scheduling with License Reservation: A Semantic Approach

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Jorge Ejarque Andras Micsik Raul Sirvent Peter Pallinger Laszlo Kovacs Rosa M. Badia Computer Architecture Department Universitat Politecnica de Catalunya Barcelona Spain Grid Computing and Clusters Group Barcelona Supercomputing Center Barcelona Spain Distributed Systems Department-Computer and Automation Research Institute Hungarian Academy of Sciences (ATOMKI) Budapest Hungary Artificial Intelligence Research Institute Spanish National Research Council Barcelona Spain

the license management is one of the main concerns when Independent Software Vendors (ISV) try to distribute their software in computing platforms such as Clouds. they want to be sure that customers use their software according to their license terms. the work presented in this paper tries to solve part of this problem extending a semantic resource allocation approach for supporting the scheduling of job taking into account software licenses. this approach defines the licenses as another type of computational resource which is available in the system and must be allocated to the different jobs requested by the users. License terms are modeled as resource properties, which describe the license constraints. A resource ontology has been extended in order to model the relations between customers, providers, jobs, resources and licenses in detail and make them machine processable. the license scheduling has been introduced in a semantic resource allocation process by providing a set of rules, which evaluate the semantic license terms during the job scheduling.

关键词： Licenses Software Semantics Resource management Ontologies Processor scheduling Law

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38th international conference on Application and theory of Petri Nets and Concurrency, PETRI NETS 2017

38th International Conference on Application and Theory of P...

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38th international conference on Application and theory of Petri Nets and Concurrency, PETRI NETS 2017

ISBN: (纸本)9783319578606

the proceedings contain 19 papers. the special focus in this conference is on Application and theory of Petri Nets and Concurrency. the topics include: Complexity made simple;resource equivalences in Petri nets;faster simulation of Petri nets using parallel computing;evaluating and improving SIP Non-INVITE transaction to alleviate the losing race problem;performance and replica consistency simulation for quorum-based NoSQL system Cassandra;an online tool for the synthesis and analysis of Petri nets with final states;an integrated environment for Petri net slicing;a tool to benchmark and debug Petri net tools;extended dependency graphs and efficient distributed fixed-point computation;model checking concurrency and causality;weak observable liveness and infinite games on finite graphs;the complexity of diagnosability and opacity verification for Petri nets;saturation for prioritised Petri nets;modelling and evaluation of a control room application;on liveness and deadlockability in subclasses of weighted Petri nets;restricting HORNETS to support self-adaptive systems;synthesis and analysis of process networks by joint application of P-graphs and Petri nets;parameterized complexity and approximability of coverability problems in weighted Petri nets.

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Building Large-Scale Deep Learning System for Entity Recognition in E-Commerce Search 19

Building Large-Scale Deep Learning System for Entity Recogni...

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12th IEEE/ACM conference on Utility and Cloud computing (UCC) / 6th IEEE/ACM international conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Wen, Musen Vasthimal, Deepak Kumar Lu, Alan Wang, Tian Guo, Aimin eBay Inc San Jose CA 95125 USA

ISBN: (纸本)9781450370165

Named-Entity-Recognition (NER) or Item Aspect Recognition task is fundamental to e-commerce marketplace. A structured listing (i.e. item aspect and catalog) is critical to the success of the marketplace - it helps the seller to put their listings to the right catalog and aspects so that the buyers can easily find the most relevant and accurate listings they are looking for. For e-commerce search engine, item aspects (brand, color, size, texture, etc.) should be automatically recognized from users' shopping queries in order to accurate understand their shopping intent. An accurate query and item aspect understanding helps to match seller's listing to buyer's purchase intend. However, in practice, this still remains challenge in e-commerce marketplace due to a couple reasons, e.g. the sparsity of the data-hundreds of millions of item aspect-name and aspect-value pairs (e.g. brand=Nike);noisy (low quality), not scalable but expensive label data that are obtained via human labeling [12] effort;the lack of context in generally very short search queries;the disperse of context and dis-orderedness in listing title or descriptions, etc. among others. All those imposes a big challenge that affect the performance of aspect recognition for e-commerce practice. In this paper, we introduce an end-to-end machine learning system to build an effective aspect recognition system for search engine that leverages the generated label data from different legacy systems and effort, without any extra human-label effort (and thus no incurred cost). the framework is constructed with multiple machine learning components that are built to optimize the search relevance and conversion as an end goal. We show that the proposed aspect recognition machine learning system improved search results quality and relevance greatly compared to the existing already strong baseline system for e-commerce search.

关键词： name-entity-recognition aspect recognition e-commerce neural networks text mining deep learning big data artificial intelligence parallel processing distributed computation

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Traffic Signal Control Considering Switching Timing via distributed Model Predictive Control

Traffic Signal Control Considering Switching Timing via Dist...

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international conference on Control, Automation and systems ( ICCAS)

作者： S. Sasaki T. Namerikawa Keio University Yokohama Japan

In this paper, we aim to alleviate traffic congestion on each link online by traffic signal control for local road transport network to be controlled. We first propose a transportation model with time delay system considering the travel time of the vehicle and calculate the optimal signal phases with variable cycle length and offset using distributed model predictive control for that model to reduce congestion rate on local roads. We then explain algorithms for distributed control that each intersection solves the optimization problem in parallel sharing information only with adjacent intersection. Finally, the effectiveness of the proposed method is confirmed by numerical simulation.

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A Scalable Barotropic Mode Solver for the parallel Ocean Program

A Scalable Barotropic Mode Solver for the Parallel Ocean Pro...

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19th international conference on Euro-Par

作者： Hu, Yong Huang, Xiaomeng Wang, Xiaoge Fu, Haohuan Xu, Shizhen Ruan, Huabin Xue, Wei Yang, Guangwen Tsinghua Univ Ctr Earth Syst Sci Minist Educ Key Lab Earth Syst Modeling Beijing 100084 Peoples R China Tsinghua Natl Lab Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9783642400476

this paper represents a novel strategy to improve the scalability of the barotropic mode in the parallel Ocean Program (POP), by theoretically analyzing the barotropic communications bottleneck. POP discretizes the elliptic equations of the barotropic mode into a linear system Ax = b and solves it using the Preconditioned Conjugate Gradient (PCG) method. PCG scales poorly on distributed systems because of the time-consuming global reductions needed by the inner products in each iteration. A performance model is developed to quantify the scaling bottleneck of PCG. Based on this model, the classical Stiefel iteration (CSI), which was originally supposed to be less efficient than PCG, is identified as being promising for massive parallelism. In contrast to PCG, the recurrence parameters of CSI are determined by the spectrum of the coefficient matrix A instead of the inner product of the residuals in previous iterations. the Lanczos method is used to resolve the difficulty of estimating the eigenvalues of the large-scale matrix A. It constructs a small-scale tridiagonal matrix that has eigenvalues close to A. By replacing PCG with CSI, global reductions and their inherent poor scalability are eliminated in the barotropic mode. the implementation of CSI in POP with a 0.1 degree resolution can accerlate one barotropic step by five times, from 1.23s to 0.26s, on 15,000 cores.

关键词： Massive parallelism Preconditioned Conjugate Gradient Classical Stiefel Iteration parallel Ocean Program Barotropic Mode

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Perspective of the Large Scale Data Facility (LSDF) Supporting Nuclear Fusion Applications

Perspective of the Large Scale Data Facility (LSDF) Supporti...

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Rainer Stotzka Volker Hartmann thomas Jejkal Michael Sutter Jos van Wezel Marcus Hardt Ariel García Rainer Kupsch Serguei Bourov Karlsruhe Institute of Technology (KIT) Institute for Data Processing and Electronics (IPE) Karlsruhe Germany Karlsruhe Institute of Technology (KIT) Steinbuch Centre for Computing (SCC) Karlsruhe Germany

To cope with the growing requirements of data intensive scientific experiments, models and simulations the Large Scale Data Facility(LSDF) at KIT aims to support many scientific disciplines. the LSDF is a distributed storage facility at Exabyte scale providing storage, archives, data bases and meta data repositories. Open interfaces and APIs support a variety of access methods to the highly available services for high throughput data applications. Tools for an easy and transparent access allow scientists to use the LSDF without bothering with the internal structures and technologies. In close cooperation with the scientific communities the LSDF provides assistance to efficiently organize data and metadata structures, and develops and deploys community specific software on the directly connected computing infrastructure.

关键词： Communities Software Protocols Standards organizations Organizations Microscopy distributed databases

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