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A scalable parallel computing framework for large-scale astrophysical fluid dynamics numerical simulation 20

A scalable parallel computing framework for large-scale astr...

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20th international conference on parallel and distributed computing, applications and technologies, PDCAT 2019

作者： Kulikov, Igor Chernykh, Igor Tchernykh, Andrei Supercomputing Lab ICMMG SB RAS Novosibirsk Russia Computer Science Department CICESE Research Center Ensenada Mexico

ISBN: (纸本)9781728126166

the numerical simulation of complex astrophysical problems requires high-performance computing due to the large size of the problems and variety of simulated physical processes. In this paper, we present a new framework for the numerical simulation of astrophysical fluid dynamics. It is based on the mechanisms of combining distributed and parallel computing techniques, advanced vectorization for KNL, and Skylake-SP CPU architectures. Our new HydroBox3D framework uses large 3D meshes to solve problems such as the dynamics of stars or galaxies. In our framework, we use computational nodes with a large amount of memory (RAM or Intel Optane in memory mode) for mesh processing and typical computational nodes for the numerical simulation of astrophysical problems. We use MPI both for send/receive operations between computational nodes and for sending processed data for calculations from data nodes. For optimization of calculations, memory, and CPU usage, we use data vectorization, FMA3, and AVX-512 instructions for Intel Xeon Phi 72XX and Intel Xeon Scalable processors. Benchmark results on different CPU and MIC devices show the effectiveness of the proposed solution. © 2019 IEEE.

关键词： Numerical models

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Efficient Stochastic Algorithms for the Sensitivity Analysis Problem in the Air Pollution Modelling 12th

Efficient Stochastic Algorithms for the Sensitivity Analysis...

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12th international conference on Large-Scale Scientific Computations (LSSC)

作者： Ostromsky, Tzvetan Todorov, Venelin Dimov, Ivan Zlatev, Zahari Bulgarian Acad Sci IICT BAS Inst Informat & Commun Technol Dept Parallel Algorithms Acad G Bonchev 25 A Sofia 1113 Bulgaria Bulgarian Acad Sci IMI BAS Inst Math & Informat Dept Informat Modelling Acad Georgi Bonchev StBlock 8 Sofia 1113 Bulgaria Aarhus Univ Natl Ctr Environm & Energy Frederiksborgvej 399POB 358 DK-4000 Roskilde Denmark

ISBN: (纸本)9783030410322;9783030410315

Sensitivity analysis of the results of large and complicated mathematical models is rather tuff and time-consuming task. However, this is quite an important problem as far as their critical applications are concerned. there are many such applications in the area of air pollution modelling. On the other hand, there are lots of natural uncertainties in the input data sets and parameters of a large-scale air pollution model. Such a model, the Danish Eulerian Model with its up-to-date high-performance implementations, is under consideration in this work. Its advanced chemical scheme (the Condensed CBM IV) takes into account a large number of chemical species and numerous reactions between them. Four efficient stochastic algorithms have been used and compared by their accuracy in studying the sensitivity of ammonia and ozone concentration results with respect to the input emission levels and some chemical reactions rate parameters. the results of our numerical experiments show that the stochastic algorithms under consideration are quite efficient for the purpose of our sensitivity studies.

关键词： Sensitivity analysis

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parallel tracking and reconstruction of states in heuristic optimization systems on GPUs 20

Parallel tracking and reconstruction of states in heuristic ...

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20th international conference on parallel and distributed computing, applications and technologies, PDCAT 2019

作者： Koster, Marcel Grob, Julian Kruger, Antonio Saarland Informatics Campus Saarbrücken Germany

ISBN: (纸本)9781728126166

Modern heuristic optimization systems leverage the parallel processing power of Graphics Processing Units (GPUs). Many states are maintained and evaluated in parallel to improve runtime by orders of magnitudes in comparison to purely CPUbased approaches. A well known example is the parallel Monte Carlo tree search, which is often used in combination with more advanced machine-learning methods these days. However, all approaches require different optimization states in memory to update or manipulate variables and observe their behavior over time. Large real-world problems often require a large number of states that are typically limited by the amount of available memory. this is particularly challenging in cases in which older states (that are not currently being evaluated) are still required for backtracking purposes. In this paper, we propose a new general high-level approach to track and reconstruct states in the scope of heuristic optimization systems on GPUs. Our method has a considerably lower memory consumption compared to traditional approaches and scales well with the complexity of the optimization problem. © 2019 IEEE.

关键词： Graphics processing unit

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Coordinated and hindsight resources allocation in distributed computing 20

Coordinated and hindsight resources allocation in distribute...

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20th international conference on parallel and distributed computing, applications and technologies, PDCAT 2019

作者： Toporkov, Victor Yemelyanov, Dmitry Computer Science Department National Research University MPEI Moscow Russia

ISBN: (纸本)9781728126166

In this work, we consider heuristic algorithms for parallel jobs execution and efficient resources allocation in heterogeneous computing environments. Existing modern job-flow execution features and realities impose many restrictions for the resources allocation procedures. Emerging virtual organizations and incorporated economic scheduling models allow users and resource owners to compete for suitable allocations based on market principles and fair scheduling policies. Subject to these features a special dynamic programming scheme is proposed to select resources depending on how they fit a particular job execution duration. Hindsight approach makes it possible to select between several different scenarios obtained with the same base scheduling procedure. Based on a conservative backfilling scheduling procedure we study how different resources allocation heuristics affect integral job-flow scheduling characteristics in a dedicated simulation environment. © 2019 IEEE.

关键词： Resource allocation

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Gaussian Model Trees for Traffic Imputation 8th

Gaussian Model Trees for Traffic Imputation

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8th international conference on Pattern Recognition applications and Methods (ICPRAM)

作者： Buschjaeger, Sebastian Liebig, thomas Morik, Katharina TU Dortmund Univ Artificial Intelligence Unit Dortmund Germany

ISBN: (纸本)9789897583513

Traffic congestion is one of the most pressing issues for smart cities. Information on traffic flow can be used to reduce congestion by predicting vehicle counts at unmonitored locations so that counter-measures can be applied before congestion appears. To do so pricy sensors must be distributed sparsely in the city and at important roads in the city center to collect road and vehicle information throughout the city in real-time. then, Machine Learning models can be applied to predict vehicle counts at unmonitored locations. To be fault-tolerant and increase coverage of the traffic predictions to the suburbs, rural regions, or even neighboring villages, these Machine Learning models should not operate at a central traffic control room but rather be distributed across the city. Gaussian Processes (GP) work well in the context of traffic count prediction, but cannot capitalize on the vast amount of data available in an entire city. Furthermore, Gaussian Processes are a global and centralized model, which requires all measurements to be available at a central computation node. Product of Expert (PoE) models have been proposed as a scalable alternative to Gaussian Processes. A PoE model trains multiple, independent GPs on different subsets of the data and weight individual predictions based on each experts uncertainty. these methods work well, but they assume that experts are independent even though they may share data points. Furthermore, PoE models require exhaustive communication bandwidth between the individual experts to form the final prediction. In this paper we propose a hierarchical Product of Expert model, which consist of multiple layers of small, independent and local GP experts. We view Gaussian Process induction as regularized optimization procedure and utilize this view to derive an efficient algorithm which selects independent regions of the data. then, we train local expert models on these regions, so that each expert is responsible for a given region.

关键词： Gaussian Processes Ensemble Product of Experts parallel

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FPGA-Based parallel Multi-Core GZIP Compressor in HDFS 20

FPGA-Based Parallel Multi-Core GZIP Compressor in HDFS

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20th international conference on parallel and distributed computing, applications and technologies, PDCAT 2019

作者： Luo, Haoxin Cai, Ye Luo, Qiuming Mao, Rui College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781728126166

With the development of Big Data, data storage has been exposed to more challenges. Data compression which can save both storage and network bandwidth, is a very important technology to deal with the challenges. In this paper, we present an end-to-end, complete, high-throughput parallel multi-core GZIP compressor in FPGA for HDFS. the GZIP compressor is designed by the scalable architecture, which supports to increase throughput by expanding multiple compression cores based on systolic array architecture. We implemented and evaluated the hardware compressor in Alpha Data Adm-Pcie-KU3 FPGA board, utilizing RIFFA for data transfers over PCI Express. According to the evaluation results, up to 16-cores compressor can be implemented and the peak compression throughput exceeds 1.1 GB/s. It is 70X speedup compared with the software compression solution. When we load the hardware compressor into HDFS, the performance of HDFS is twice as much as that without loading the compressor. © 2019 IEEE.

关键词： Field programmable gate arrays (FPGA)

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parallel Efficient Data Loading 8

Parallel Efficient Data Loading

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8th international conference on Data Science, Technology and applications (DATA)

作者： Jimenez-Peris, Ricardo Ballesteros, Francisco Azqueta, Ainhoa Kranas, Pavlos Burgos, Diego Martinez, Patricio LeanXcale Campus Montegancedo Madrid Spain Univ Rey Juan Carlos Madrid Spain Univ Politecn Madrid Madrid Spain

ISBN: (纸本)9789897583773

In this paper we discuss how we architected and developed a parallel data loader for LeanXcale database. the loader is characterized for its efficiency and parallelism. LeanXcale can scale up and scale out to very large numbers and loading data in the traditional way it is not exploiting its full potential in terms of the loading rate it can reach. For this reason, we have created a parallel loader that can reach the maximum insertion rate LeanXcale can handle. LeanXcale also exhibits a dual interface, key-value and SQL, that has been exploited by the parallel loader. Basically, the loading leverages the key-value API and results in a highly efficient process that avoids the overhead of SQL processing. Finally, in order to guarantee the parallelism we have developed a data sampler that samples data to generate a histogram of data distribution and use it to pre-split the regions across LeanXcale instances to guarantee that all instances get an even amount of data during loading, thus guaranteeing the peak processing loading capability of the deployment.

关键词： Loading Extract-Transform-Load (ETL) Scalable Databases NUMA Architectures Database Appliance Scalable Transactional Management

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Converging HPC, Big Data and Cloud technologies for Precision Agriculture Data Analytics on Supercomputers 35th

Converging HPC, Big Data and Cloud Technologies for Precisio...

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35th international conference on High Performance computing , ISC High Performance 2020

作者： Georgiou, Yiannis Zhou, Naweiluo Zhong, Li Hoppe, Dennis Pospieszny, Marcin Papadopoulou, Nikela Nikas, Kostis Nikolos, Orestis Lagkas Kranas, Pavlos Karagiorgou, Sophia Pascolo, Eric Mercier, Michael Velho, Pedro Ryax Technologies Saint-Fons France HLRS Stuttgart Germany PSNC Poznań Poland ICCS Athens Greece Leanxscale Madrid Spain Ubitech Athens Greece CINECA Casalecchio di Reno Italy

ISBN: (纸本)9783030598501

the convergence of HPC and Big Data along with the influence of Cloud are playing an important role in the democratization of HPC. the increasing needs of Data Analytics in computational power has added new fields of interest for the HPC facilities but also new problematics such as interoperability with Cloud and ease of use. Besides the typical HPC applications, these infrastructures are now asked to handle more complex workflows combining Machine Learning, Big Data and HPC. this brings challenges on the resource management, scheduling and environment deployment layers. Hence, enhancements are needed to allow multiple frameworks to be deployed under common system management while providing the right abstraction to facilitate adoption. this paper presents the architecture adopted for the parallel and distributed execution management software stack of Cybele EU funded project which is put in place on production HPC centers to execute hybrid data analytics workflows in the context of precision agriculture and livestock farming applications. the design is based on: Kubernetes as a higher level orchestrator of Big Data components, hybrid workflows and a common interface to submit HPC or Big Data jobs;Slurm or Torque for HPC resource management;and Singularity containerization platform for the dynamic deployment of the different Data Analytics frameworks on HPC. the paper showcases precision agriculture workflows being executed upon the architecture and provides some initial performance evaluation results and insights for the whole prototype design. © 2020, Springer Nature Switzerland AG.

关键词： Big data

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Architecture Design of distributed Medical Big Data Platform Based on Spark 8

Architecture Design of Distributed Medical Big Data Platform...

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IEEE 8th Joint international Information Technology and Artificial Intelligence conference (ITAIC)

作者： Tu, Yongqiu Lu, Yiqiang Chen, Guohua Zhao, Jie Yi, Faling Guangdong Pharmaceut Univ Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538681787

An Apache Spark based distributed computing and storage system designed for large-scale health data. the system provides the solution for health data digitalization and analysis, while enabling high-throughput data processing, real-time processing and messaging capabilities. the design of the system has the potential to provide many health-related services to medical professionals, such as data retrieving/processing, real-time alerts, data mining. the article described key considerations throughout the system designing process, including comparison of different component, finding the optimal data flow mechanics for specific tasks, etc. During the design and implementation, these core technologies are involved: (1) Java, Scala, programming languages, with IntelliJ IDEA IDE. (2)Apache Spark, general purpose distributed data processing engine. (3)Apache Hadoop: HDFS and YARN, distributed storage system and computational resource manager, respectively. (4)MySQL, relational database engine.(5)Apache Hive, distributed relational database engine.(6)Apache Kafka, distributed messaging system.

关键词： MapReduce Spark Streaming Hadoop Big Health data

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Reliability oriented probe deployment strategy for power cellular internet of things 8th

Reliability oriented probe deployment strategy for power cel...

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8th international conference on Computer Engineering and Networks, CENet 2018

作者： Wei, Lei Zhu, Daohua Chen, Wenwei Zhu, Yukun Peng, Lin State Grid Jiangsu Electric Power Company Ltd. Nanjing China Electric Power Research Institute State Grid Jiangsu Electric Power Company Ltd. Nanjing China State Grid Information and Telecommunication Group Ltd. Beijing China Beijing Vectinfo Technologies Company Ltd. Beijing China

ISBN: (纸本)9783030146795

With the power cellular Internet of things (IoT) communication system playing a more and more important role for information transmission, exchange, and processing in the smart grid, higher requirements for the maintenance management and monitoring technologies need to be satisfied. However, at present, the monitoring capability of the power cellular IoT communication network is relatively scarce, resulting in grid data transmission has to rely on the public network. the security, reliability, and real-time performance cannot be guaranteed. therefore, this paper focuses on the characteristics of power wireless networks, studies the technology of distributed service quality monitoring, and designs a deployment strategy for Power Cellular Internet of things to monitor performance nodes. We first introduce the loop rate of deployment probes as the evaluation index of network reliability, and model the minimum weighted vertex coverage problem of probe deployment as the largest weighted independent set problem. then we propose a fast heuristic algorithm to find its minimum deployment overhead with satisfying reliability requirement. the simulation results show that the proposed algorithm can improve the ability of the smart grid for locating faults quickly and effectively, as well as the comprehensive monitoring and management capability, improving network performance. © Springer Nature Switzerland AG 2020.

关键词： Probes

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