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检索条件"任意字段=International Symposium on Databases in Parallel and Distributed Systems"

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NC Algorithms for Popular Matchings in One-Sided Preference systems and Related Problems

NC Algorithms for Popular Matchings in One-Sided Preference ...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Changyong Hu Vijay K. Garg Department of Electrical and Computer Engineering University of Texas Austin

ISBN: (数字)9781728168760

ISBN: (纸本)9781728168777

The popular matching problem is of matching a set of applicants to a set of posts, where each applicant has a preference list, ranking a non-empty subset of posts in the order of preference, possibly with ties. A matching M is popular if there is no other matching M' such that more applicants prefer M' to M. We give the first NC algorithm to solve the popular matching problem without ties. We also give an NC algorithm that solves the maximum-cardinality popular matching problem. No NC or RNC algorithms were known for the matching problem in preference systems prior to this work. Moreover, we give an NC algorithm for a weaker version of the stable matching problem, that is, the problem of finding the "next" stable matching given a stable matching.

关键词： Impedance matching Bipartite graph Computational modeling Resource management Pareto optimization distributed processing Economics

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CSF: An Efficient parallel Deduplication Algorithm by Clustering Scattered Fingerprints 17

CSF: An Efficient Parallel Deduplication Algorithm by Cluste...

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IEEE Int Conf on parallel and distributed Processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking (ISPA/BDCloud/SocialCom/SustainCom)

作者： Hao, Fan Xu, Guangping Zhang, Yi Yuan, Liming Xue, Yanbing Tianjin Univ Technol Sch Comp Sci & Engn Tianjin Peoples R China Minist Educ Key Lab Comp Vis & Syst Tianjin Peoples R China Tianjin Key Lab Intelligence Comp & Novel Softwar Tianjin Peoples R China

ISBN: (纸本)9781728143286

Deduplication is one of the most effective and efficient techniques to save memory space. It is widely used in data centers and cloud storage systems. Multi-stream concurrency is expected to increase the throughput of deduplication. However, multiple data streams hurt the locality of accessed data and weaken the benefit of data concurrency, which forms a challenge for data deduplication. Usually, the ordered index can reshape the locality of data streams, which can improve the cache hit rate during deduplication. In this paper, we first propose an efficient parallel deduplication algorithm by clustering scattered fingerprints, called CSF, to exploit the data locality as much as possible. It tries to improve the utilization rate of the fingerprint page by the clustered fingerprints. Moreover, it retains the scattered fingerprint to next round fingerprint comparison by re-using the fingerprints on the same page. Thus the number of the fingerprint pages to read is reduced. We further optimize the proposed algorithm by a scheduling strategy, which effectively schedules the task of part streams ahead while ensuring the overall performance. Finally, we evaluated the performance of our algorithm with various data sets in experiments. The experimental results show that our proposed algorithm achieves better performance than the state-of-the-art method.

关键词： Data streams Deduplication parallel algorithms Storage systems

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UMR-EC: A Unified and Multi-Rail Erasure Coding Library for High-Performance distributed Storage systems 19

UMR-EC: A Unified and Multi-Rail Erasure Coding Library for ...

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28th international symposium on High-Performance parallel and distributed Computing (ACM HPDC)

作者： Shi, Haiyang Lu, Xiaoyi Shankar, Dipti Panda, Dhabaleswar K. Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA

ISBN: (纸本)9781450366700

distributed storage systems typically need data to be stored redundantly to guarantee data durability and reliability. While the conventional approach towards this objective is to store multiple replicas, today's unprecedented data growth rates encourage modern distributed storage systems to employ Erasure Coding (EC) techniques, which can achieve better storage efficiency. Various hardware-based EC schemes have been proposed in the community to leverage the advanced compute capabilities on modern data center and cloud environments. Currently, there is no unified and easy way for distributed storage systems to fully exploit multiple devices such as CPUs, GPUs, and network devices (i.e., multi-rail support) to perform EC operations in parallel;thus, leading to the under-utilization of the available compute power. In this paper, we first introduce an analytical model to analyze the design scope of efficient EC schemes in distributed storage systems. Guided by the performance model, we propose UMR-EC, a Unified and Multi-Rail Erasure Coding library that can fully exploit heterogeneous EC coders. Our proposed interface is complemented by asynchronous semantics with optimized metadata-free scheme and EC rate-aware task scheduling that can enable a highly-efficient I/O pipeline. To show the benefits and effectiveness of UMR-EC, we re-design HDFS 3.x write/read pipelines based on the guidelines observed in the proposed performance model. Our performance evaluations show that our proposed designs can outperform the write performance of replication schemes and the default HDFS EC coder by 3.7x - 6.1x and 2.4x - 3.3x, respectively, and can improve the performance of read with failure recoveries up to 5.1x compared with the default HDFS EC coder. Compared with the fastest available CPU coder (i.e., ISA-L), our proposed designs have an improvement of up to 66.0% and 19.4% for write and read with failure recoveries, respectively.

关键词： multi-rail erasure coding distributed storage systems high performance

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System Architecture and Data Allocation/Coordination Technologies of Autonomous Decentralized Database System for High Assurance 14

System Architecture and Data Allocation/Coordination Technol...

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14th IEEE international symposium on Autonomous Decentralized systems (ISADS) / 3rd international Workshop on Service Assurance in System Wide Information Management (SASWIM)

作者： Perez-Leguizamo, Carlos Guadalupe Godinez-Borja, J. S. Banco Mexico Cent Bank Operating Syst Dev Div Mexico City DF Mexico

ISBN: (纸本)9781728116723

During the last few years there have been important changes in the way that consumers use the Internet and e-commerce. This exceptional expansion, combined with dynamic interactions with users and providers, is boosting the development of new electronic business models, such as block chain, fintech, bitcoin, Uber, Airbnb, etc. The information systems that support such business processes, usually depend on distributed database systems. These approaches have mostly opted to exploit two important characteristics of this kind of applications: at first, they contain a set of commodity types with a limited inventory. On the other hand, incremental updates are the kind of operations which these items typically involve. In this sense, it is possible to achieve system distribution by using tokens which represent the commodities? instances for sales in e-business. There exist two mainly different mechanisms for distributing tokens: replication and partitioning. Token replication mechanism demands expensive synchronization protocols to provide the consistency of the data. On the other hand, token partitioning, depends on token redistribution strategies which allow dynamic migration of tokens to those servers in which it is needed. Such strategies are developed from the standpoint that the total system can locally be known, and therefore, they fail to address the important problem of how to allocate and coordinate the data to make it adaptive to rapidly changing situations. In this paper, we present Autonomous Decentralized Database System (ADDS) concept and architecture in order to extend the properties of autonomy and decentralization to wide-area distributed database systems. ADDS fundamental technologies confers autonomy and loosely coupling to all the components of the system (databases) and provides a background coordination to adapt the system to evolving situations. Moreover, we present two data allocation and coordination technologies for high response time based on sp

关键词： Autonomous Decentralized systems high assurance distributed databases data consistency coordination mobile agent

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Efficient parallel and Adaptive Partitioning for Load-balancing in Spatial Join

Efficient Parallel and Adaptive Partitioning for Load-balanc...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Jie Yang Satish Puri Computer Science Department Marquette University Milwaukee USA

ISBN: (数字)9781728168760

ISBN: (纸本)9781728168777

Due to the developments of topographic techniques, clear satellite imagery, and various means for collecting information, geospatial datasets are growing in volume, complexity, and heterogeneity. For efficient execution of spatial computations and analytics on large spatial data sets, parallel processing is required. To exploit fine-grained parallel processing in large scale compute clusters, partitioning in a load-balanced way is necessary for skewed datasets. In this work, we focus on spatial join operation where the inputs are two layers of geospatial data. Our partitioning method for spatial join uses Adaptive Partitioning (ADP) technique, which is based on Quadtree partitioning. Unlike existing partitioning techniques, ADP partitions the spatial join workload instead of partitioning the individual datasets separately to provide better load-balancing. Based on our experimental evaluation, ADP partitions spatial data in a more balanced way than Quadtree partitioning and Uniform grid partitioning. ADP uses an output-sensitive duplication avoidance technique which minimizes duplication of geometries that are not part of spatial join output. In a distributed memory environment, this technique can reduce data communication and storage requirements compared to traditional *** improve the performance of ADP, an MPI+Threads based parallelization is presented. With ParADP, a pair of real world datasets, one with 717 million polylines and another with 10 million polygons, is partitioned into 65,536 grid cells within 7 seconds. ParADP performs well with both good weak scaling up to 4,032 CPU cores and good strong scaling up to 4,032 CPU cores.

关键词： Geometry Spatial databases Partitioning algorithms Heuristic algorithms Clustering algorithms Geospatial analysis parallel processing

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Workshop 16: SNACS Scalable Networks for Advanced Computing systems

Workshop 16: SNACS Scalable Networks for Advanced Computing ...

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

作者： İlkay Altintas Dorian Arnold Martin Schulz Matthew G. F. Dosanjh Ryan E. Grant Taylor Groves San Diego Supercomputing Center Emory University Technical University of Munich Sandia National Laboratories Lawrence Berkeley National Laboratory

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. The Workshop on Scalable Networks for Advanced Computing systems is a venue for discussions in the networks community that encompasses the full networks stack, ranging from high-level tools and interfaces, down to low-level hardware. The goal of this workshop is to bring together researchers to present research on the next-generation of large scale interconnects for scientific applications. In particular, our aim is to provide a venue for discussion of the full network stack from user level interfaces (i.e., MPI, PGAS) to the hardware level (i.e., network offload, hardware support for active messages, routing protocols). Workshop topics include: networks for exascale systems, networks for multi-facility workflows, new communication models and architectures for large scale systems, and network interface and hardware co-design.

关键词： Hardware distributed processing Distance measurement Tools Next generation networking

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Leveraging STARE for Co-aligned Data Locality with netCDF and Python MPI 39

Leveraging STARE for Co-aligned Data Locality with netCDF an...

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IEEE international Geoscience and Remote Sensing symposium (IGARSS)

作者： Kuo, Kwo-Sen Yu, Hongfeng Pan, Yu Rilee, Michael L. NASA Goddard Space Flight Ctr Greenbelt MD 20771 USA Univ Maryland ESSIC College Pk MD 20742 USA Bayesics LLC Bowie MD USA Univ Nebraska Lincoln NE USA Rilee Syst Technol LLC Derwood MD USA

ISBN: (纸本)9781538691540

We have leveraged STARE indexing to package partitioned data chunks from diverse datasets into netCDF files, distributed them on a cluster of 16 lightweight nodes with their placements spatiotemporally co-aligned, and demonstrated a few integrative analyses using netCDF parallel I/O and Python MPI, with single-user performance and scalability comparable to, or even better than, that of a parallel array database management system (ADBMS) such as SciDB. However, records of the node location and STARE index ranges for each data chunk, similar to the chunk maps of SciDB, must be maintained and consulted by the I/O and analysis code for coordinating the analytic operations in parallel, in order to achieve the good performance and scalability.

关键词： Big Data interoperability parallel processing scalability data-intensive analysis

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4th international symposium on Emerging Technologies for Education, SETE 2019, was held in conjunction with the 18th international Conference on Web-based Learning, ICWL 2019

4th International Symposium on Emerging Technologies for Edu...

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4th international symposium on Emerging Technologies for Education, SETE 2019, was held in conjunction with the 18th international Conference on Web-based Learning, ICWL 2019

ISBN: (纸本)9783030387778

The proceedings contain 40 papers. The special focus in this conference is on Emerging Technologies for Education, Web-based Learning. The topics include: Influence of the type of organizations on their readiness for implementing industry 4.0;semantic competency directory for constructive alignment in digital learning designs and systems;Education needs in context of migration and industry 4.0 in selected EU countries;students’ language needs analysis as a motivation mover - Czech and Taiwanese case study;uncovering the potential of the google search engine for L2 learning and L2 translator training;the effect of gamification in user satisfaction, the approval rate and academic performance;Topic detection for online course feedback using LDA;an educational model for integrating game-based and problem-based learning in data-driven flipped classrooms;distributed student team work in challenge-based innovation and entrepreneurship (I&E) course;semantic recommendations and topic modeling based on the chronology of romanian literary life;applying the teach-back method and mobile technology to support elementary students’ mathematics problem-solving strategies;A web-based platform for building PBL competences among students;reconstructing scanned documents for full-text indexing to empower digital library services;curating educational resources for homework management: A support prototype;the analysis of worldwide research on artificial intelligence assisted user modeling;analyzing the influence of academic papers based on improved PageRank;a systematic review of frameworks for coding towards classroom dialogue;application of parallel corpus to teaching style and translation;the design and application of an web-based online examination system;a text mining application in operation management course teaching.

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distributed Deep Learning of ResNet50 and VGG16 with Pipeline parallelism

Distributed Deep Learning of ResNet50 and VGG16 with Pipelin...

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international symposium on Computing and Networking Workshops (CANDARW)

作者： Naoki Takisawa Syunji Yazaki Hiroaki Ishihata School of Computer Science Tokyo University of Technology Hachioji Tokyo Japan Information Technology Center The University of Electro-Communications Chofu Tokyo Japan

ISBN: (数字)9781728199191

ISBN: (纸本)9781728189314

Data parallel distributed deep learning has been used to accelerate the learning speed. The communication is becoming a bottleneck as the computation time is accelerated. Therefore, the communication time is needed to be reduced. As a new acceleration method, research activity for pipeline parallelism has increased since 2018. However, the existing methods generally combine data parallelism and pipeline parallelism, and there has been a little analysis of speed improvement in pipeline parallelism alone. This paper analyzes the effect of speed improvement by pipeline parallelism. We compared the execution time of data parallelism and pipeline parallelism with VGG16 and ResNet50, respectively. VGG16 includes more parameters comparing to ResNet50 when the size of input data is the same. A comparison of the execution time shows that data parallelism is 3.3 times faster than pipeline parallelism for ResNet50. Pipeline parallelism is 2.45 times faster than data parallelism for VGG16. The speed-up of pipeline parallelism was significant in a model with a large number of parameters. Therefore, pipeline parallelism is suitable for large-scale models that have a large number of parameters.

关键词： Deep learning Pipelines distributed databases Training data parallel processing Data models Acceleration

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distributed MCMC Inference in Dirichlet Process Mixture Models Using Julia 19

Distributed MCMC Inference in Dirichlet Process Mixture Mode...

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19th Annual IEEE/ACM international symposium on Cluster, Cloud, and Grid Computing (CCGRID)

作者： Dinari, Or Yu, Angel Freifeld, Oren Fisher, John W., III Ben Gurion Univ Negev Dept Comp Sci Beer Sheva Israel MIT CSAIL 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9781728109121

Due to the increasing availability of large data sets, the need for general-purpose massively-parallel analysis tools become ever greater. In unsupervised learning, Bayesian nonparametric mixture models, exemplified by the Dirichlet-Process Mixture Model (DPMM), provide a principled Bayesian approach to adapt model complexity to the data. Despite their potential, however, DPMMs have yet to become a popular tool. This is partly due to the lack of friendly software tools that can handle large datasets efficiently. Here we show how, using Julia, one can achieve efficient and easily-modifiable implementation of distributed inference in DPMMs. Particularly, we show how a recent parallel MCMC inference algorithm originally implemented in C++ for a single multi-core machine can be distributed efficiently across multiple multi-core machines using a distributed-memory model. This leads to speedups, alleviates memory and storage limitations, and lets us learn DPMMs from significantly larger datasets and of higher dimensionality. It also turned out that even on a single machine the proposed Julia implementation handles higher dimensions more gracefully (at least for Gaussians) than the original C++ implementation. Finally, we use the proposed implementation to learn a model of image patches and apply the learned model for image denoising. While we speculate that a highly-optimized distributed implementation in, say, C++ could have been faster than the proposed implementation in Julia, from our perspective as machine-learning researchers (as opposed to HPC researchers), the latter also offers a practical and monetary value due to the ease of development and abstraction level.

关键词： Bayes methods image denoising Markov processes mixture models multiprocessing systems nonparametric statistics object detection pattern clustering sampling methods unsupervised learning image denoising Multiprocessing systems Unsupervised learning Bayes methods Nonparametric Statistics Object detection sampling methods Markov chain Cluster Analysis Mixture models image patch

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