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31st International Symposium on High-Performance Parallel and Distributed computing (HPDC)

作者： Narasimhamurthy, Sai Lockwood, Glenn Seagate Syst Marlow Bucks England Lawrence Berkeley Natl Lab Berkeley CA USA

ISBN: (纸本)9781450391993

Exciting changes are coming to the fore in the world of Storage and I/O for High Performance and data intensive computing. Existing data access and data retrieval methods that suitably support the new generation of data intensive applications in the realm of HPC and AI are being re-assessed. These new applications need to support both evolutionary as well as revolutionary approaches to data access and storage. Open source is enabling the adoption of these new techniques. This workshop looks at cutting edge trends in storage systems and solutions for data intensive HPC and AI applications with specific focus primarily on community driven initiatives and commercial products that may inspire the community. The EMOSS'22 workshop proceedings are available at: https://***/***?id=3526061

关键词： HPC Storage data intensive computing Cloud computing Exascale I/O

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The RePhrase Extended Pattern Set for data intensive Parallel computing

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INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING 2019年第1期47卷 74-93页

作者： Danelutto, Marco De Matteis, Tiziano De Sensi, Daniele Mencagli, Gabriele Torquati, Massimo Aldinucci, Marco Kilpatrick, Peter Univ Pisa Pisa Italy Univ Torino Turin Italy Queens Univ Belfast Belfast Antrim North Ireland

We discuss the extended parallel pattern set identified within the EU-funded project RePhrase as a candidate pattern set to support data intensive applications targeting heterogeneous architectures. The set has been designed to include three classes of pattern, namely (1) core patterns, modelling common, not necessarily data intensive parallelism exploitation patterns, usually to be used in composition;(2) high level patterns, modelling common, complex and complete parallelism exploitation patterns;and (3) building block patterns, modelling the single components of data intensive applications, suitable for usein compositionto implement patterns not covered by the core and high level patterns. We discuss the expressive power of the RePhrase extended pattern set and results illustrating the performances that may be achieved with the FastFlow implementation of the high level patterns.

关键词： Parallel design patterns data intensive computing Stream computing Algorithmic skeletons

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Nebula: Distributed Edge Cloud for data intensive computing

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2017年第11期28卷 3229-3242页

作者： Jonathan, Albert Ryden, Mathew Oh, Kwangsung Chandra, Abhishek Weissman, Jon Univ Minnesota Twin Cities Comp Sci & Engn Minneapolis MN 55455 USA

Centralized cloud infrastructures have become the popular platforms for data-intensive computing today. However, they suffer from inefficient data mobility due to the centralization of cloud resources, and hence, are highly unsuited for geo-distributed data-intensive applications where the data may be spread at multiple geographical locations. In this paper, we present Nebula: a dispersed edge cloud infrastructure that explores the use of voluntary resources for both computation and data storage. We describe the lightweight Nebula architecture that enables distributed data-intensive computing through a number of optimization techniques including location-aware data and computation placement, replication, and recovery. We evaluate Nebula performance on an emulated volunteer platform that spans over 50 PlanetLab nodes distributed across Europe, and show how a common data-intensive computing framework, MapReduce, can be easily deployed and run on Nebula. We show Nebula MapReduce is robust to a wide array of failures and substantially outperforms other wide-area versions based on emulated existing systems.

关键词： Distributed Systems cloud computing edge cloud data intensive computing

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SoSE Architecture of data intensive computing for Healthcare Information System 8

SoSE Architecture of Data Intensive Computing for Healthcare...

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2015 Eighth International Conference on Internet computing for Science and Engineering (ICICSE)

作者： Ni, Jun Chen, Ying Sha, Jie Zhang, Minghuan Shanghai Sanda Univ Big Data & Informat Res Lab Shanghai 201209 Peoples R China

ISBN: (纸本)9781509004546

The architecture of healthcare information system (HIS) is important framework. The architecture implements the cutting-edge technologies of cloud computing and big data in parallel and distributed computing manors. The architecture scheme is proposed in SoSE-based systems engineering paradigm. The implementation of such architecture for healthcare data processing is given via multiple layers of big data implementations. This study is a primary work which provides basic insight into future intelligent system of HIS systems.

关键词： healthcare information systems HIS big data cloud computing healthcare services system architecture system of systems SoSE data intensive computing high performance computing HPC parallel computing

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A Heuristic Scheduling Approach to Hybrid Makepsan Problem for data intensive computing

A Heuristic Scheduling Approach to Hybrid Makepsan Problem f...

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2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)

作者： Liang, Wei Xiong, Yonghua Wu, Min Jin, Qun Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Waseda Univ Fac Human Sci Tokorozawa Saitama 3591141 Japan

ISBN: (纸本)9781509018932

data intensive computing (DIC) offers an attractive option for business to remotely execute applications and load the computing resources from cloud in a streaming way. A key challenge in such environment is to increase the utilization of cloud cluster for the high throughput processing. One way of achieving this goal is to optimize the execution of computing jobs on the cluster. We observe that the order in which these jobs are executed can have a significant impact on their overall completion time (makespan). Our goal is to design a job scheduler that minimizes the makespan. In this study, a new formalization is introduced to present each job as a pair of disk processing and network transmitting two-stage durations. Due to the streaming processing feature, the two-stage operations are executed in an overlap manner and may lead to both one-stage and two-stage scheduling situations. A novel heuristic scheduling strategy is proposed for this hybrid scheduling problem, and the performance of the method is confirmed by the experimental evaluation.

关键词： data intensive computing Scheduling Makespan NP

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Dynamic power management in energy-aware computer networks and data intensive computing systems

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2014年 37卷 284-296页

作者： Niewiadomska-Szynkiewicz, Ewa Sikora, Andrzej Arabas, Piotr Kamola, Mariusz Mincer, Marcin Kolodziej, Joanna Warsaw Univ Technol Inst Control & Computat Engn Nowowiejska 15-19 PL-06665 Warsaw Poland Res & Acad Comp Network PL-02769 Warsaw Poland Cracow Univ Technol Inst Comp Sci PL-31155 Krakow Poland

Energy awareness is an important aspect of modern network and computing system design and management, especially in the case of internet-scale networks and data intensive large scale distributed computing systems. The main challenge is to design and develop novel technologies, architectures and methods that allow us to reduce energy consumption in such infrastructures, which is also the main reason for reducing the total cost of running a network. Energy-aware network components as well as new control and optimization strategies may save the energy utilized by the whole system through adaptation of network capacity and resources to the actual traffic load and demands, while ensuring end-to-end quality of service. In this paper, we have designed and developed a two-level control framework for reducing power consumption in computer networks. The implementation of this framework provides the local control mechanisms that are implemented at the network device level and network-wide control strategies implemented at the central control level. We also developed network-wide optimization algorithms for calculating the power setting of energy consuming network components and energy-aware routing for the recommended network configuration. The utility and efficiency of our framework have been verified by simulation and by laboratory tests. The test cases were carried out on a number of synthetic as well as on real network topologies, giving encouraging results. Thus, we come up with well justified recommendations for energy-aware computer network design, to conclude the paper. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Energy-aware network Energy-aware routing Traffic engineering Dynamic power management data intensive computing Network simulation Testbed implementation

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data intensive computing of X-ray Computed Tomography Reconstruction at the LSDF

Data Intensive Computing of X-ray Computed Tomography Recons...

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21st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)

作者： Yang, Xiaoli Jejkal, Thomas Pasic, Halil Stotzka, Rainer Streit, Achim van Wezel, Jos Rolo, Tomy dos Santos Karlsruhe Inst Technol Inst Data Proc & Elect IPE D-76021 Karlsruhe Germany Steinbuch Ctr Comp Karlsruhe Germany Inst Synchrotron Radiat Karlsruhe Germany

ISBN: (纸本)9780769549392;9781467353212

In this paper, the method of data intensive computing is studied for large amounts of data in computed tomography (CT). An automatic workflow is built up to connect the tomography beamline of ANKA with the large scale data facility (LSDF), able to enhance the data storage and analysis efficiency. In this workflow, this paper focuses on the parallel computing of 3D computed tomography reconstruction. Different from the existing reconstruction system with filtered back-projection method, an algebraic reconstruction technique based on compressive sampling theory is presented to reconstruct the data from ultrafast computed tomography with fewer projections. Then the connected computing resources at the LSDF are used to implement the 3D CT reconstruction by distributing the whole job into multiple tasks executed in parallel. Promising reconstruction images and high computing performance are reported. For the 3D X-ray CT reconstruction, less than six minutes are actually required. LSDF is not only able to organize data efficiently, but also can provide reconstructed results to users in nearly instantaneous time. After integration into the workflow, this data intensive computing method will largely improve the data processing for ultrafast computed tomography at ANKA.

关键词： data intensive computing Large Scale data Facility Computed Tomography Algebraic Reconstruction Technique Compressive Sampling

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Applications of machine learning in supercritical fluids research

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JOURNAL OF SUPERCRITICAL FLUIDS 2023年第1期202卷

作者： Roach, Lucien Rignanese, Gian-Marco Erriguible, Arnaud Aymonier, Cyril Univ Bordeaux CNRS Bordeaux INP ICMCBUMR 5026 F-33600 Pessac France UCLouvain Inst Condensed Matter & Nanosci Chemin Etoiles 8 B-1348 Louvain La Neuve Belgium Univ Bordeaux I2M UMR5295 Bordeaux INP CNRS Site ENSCBP16 Ave Pey Berland Pessac France

Machine learning has seen increasing implementation as a predictive tool in the chemical and physical sciences in recent years. It offers a route to accelerate the process of scientific discovery through a computational datadriven approach. Whilst machine learning is well established in other fields, such as pharmaceutical research, it is still in its infancy in supercritical fluids research, but will likely accelerate dramatically in coming years. In this review, we present a basic introduction to machine learning and discuss its current uses by supercritical fluids researchers. In particular, we focus on the most common machine learning applications;including: (1) The estimation of the thermodynamic properties of supercritical fluids. (2) The estimation of solubilities, miscibilities, and extraction yields. (3) Chemical reaction optimization. (4) Materials synthesis optimization. (5) Supercritical power systems. (6) Fluid dynamics simulations of supercritical fluids. (7) Molecular simulation of supercritical fluids and (8) Geosequestration of CO2 using supercritical fluids.

关键词： Machine learning Optimization Supercritical fluids data intensive computing Regression

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Bayesian Cognitive Model in Scheduling Algorithm for data intensive computing

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JOURNAL OF GRID computing 2012年第1期10卷 173-184页

作者： Wang, Wei Zeng, Guosun Tongji Univ Dept Comp Sci & Engn Shanghai 200092 Peoples R China Tongji Branch Natl Engn & Technol Ctr High Performance Comp Shanghai 200092 Peoples R China

Science is increasingly becoming more and more data-driven. The ability of a geographically distributed community of scientists to access and analyze large amounts of data has emerged as a significant requirement for furthering science. In data intensive computing environment with uncountable numeric nodes, resource is inevitably unreliable, which has a great effect on task execution and scheduling. Novel algorithms are needed to schedule the jobs on the trusty nodes to execute, assure the high speed of communication, reduce the jobs execution time, lower the ratio of failure execution, and improve the security of execution environment of important data. In this paper, a kind of trust mechanism-based task scheduling model was presented. Referring to the trust relationship models of social persons, trust relationship is built among computing nodes, and the trustworthiness of nodes is evaluated by utilizing the Bayesian cognitive method. Integrating the trustworthiness of nodes into a Dynamic Level Scheduling (DLS) algorithm, the Trust-Dynamic Level Scheduling (Trust-DLS) algorithm is proposed. Moreover, a benchmark is structured to span a range of data intensive computing characteristics for evaluation the proposed method. Theoretical analysis and simulations prove that the Trust-DLS algorithm can efficiently meet the requirement of data intensive workloads in trust, sacrificing fewer time costs, and assuring the execution of tasks in a security way in large-scale data intensive computing environment.

关键词： data intensive computing Scheduling Bayesian Trust

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Segmented In-Advance data Analytics for Fast Scientific Discovery

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IEEE TRANSACTIONS ON CLOUD computing 2020年第2期8卷 432-442页

作者： Liu, Jialin Chen, Yong Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA

Scientific discovery usually involves data generation, data preprocessing, data storage and data analysis. As the data volume exceeds a few terabytes (TB) in a single simulation run, the data movement, which happens during each cycle of the scientific discovery, continues to be the bottleneck in most scientific big data applications. A lot of research works have been conducted on reducing the data movement. Among the existing efforts and based on our previous research, reusing the analysis results shows a significant potential in optimizing the data movement between analysis operations. In this work, we propose the Segmented In-Advance (SIA) data analytics approach for optimizing the data movement and we also provide a cloud-based elastic distributed in-memory database to manage the intermediate analysis results. The fundamental idea of this Segmented In-Advance approach is to analyze the history operations and to predict the future interesting analytics operations. The predicted analysis operation is in-advance performed on the finer segmented dataset and the segmented results are distributed in an in-memory key-value store for future reuse. The evaluation shows that the segmented in-advance data analytics approach achieves 1.2X-6.1X speedup. The evaluation also shows a good scalability of the in-memory distributed data store. The proposed Segmented In-Advance data analytics approach is a promising data movement reduction solution for scientific big data applications and fast scientific discovery.

关键词： Segmented in-advance data analytics big data data intensive computing scientific computing

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