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检索条件"任意字段=9th International Conference on Parallel and Distributed Computing, Applications and Technologies"

共 1770 条记录，以下是261-270 订阅

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Survey and performance test of python-based libraries for parallel processing 9

Survey and performance test of python-based libraries for pa...

引用

9th international conference on Smart Media and applications, SMA 2020

作者： Kim, Taehong Cha, Yoonseok Shin, Byeonchun Cha, Byungrae GenoTech Inc. Gwangju Metro-city Korea Republic of Chonnam National Univ Gwangju Metro-city Korea Republic of GIST GenoTech Inc. Gwangju Metro-city Korea Republic of

ISBN: (纸本)9781450389259

By the Fourth Industrial Revolution and the 10 strategic technology of the Gartner Group, Artificial Intelligence(AI) technology was important and affected many areas. One of the ways to accelerate AI services is the Python-based parallel processing library. High-level programming languages such as Python are increasingly used to provide intuitive interfaces to libraries written in lower-level languages and for assembling applications from various components. this migration towards orchestration rather than implementation, coupled with the growing need for parallel computing (e.g., due to big data and the end of Moore's law), necessitates rethinking how parallelism is expressed in programs.[1] In this paper, take a look at a Python-based distributed parallel processing library, one of the ways to accelerate AI services, and use it to compare serial and parallel processing times. © 2020 ACM.

关键词： Python

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Decreasing the Learning Cost of Offline parallel Application Optimization Strategies 28

Decreasing the Learning Cost of Offline Parallel Application...

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28th Euromicro international conference on parallel, distributed and Network-Based Processing (PDP)

作者： Berned, Gustavo Rossi, Fabio D. Luizelli, Marcelo C. Beck, Antonio Carlos S. Lorenzon, Arthur F. Fed Inst Educ Sci & Technol Farroupilha Santa Maria RS Brazil Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil Fed Univ Pampa Campus Alegrete Alegrete Brazil

ISBN: (纸本)9781728165820

Many parallel applications do not scale as the number of threads increases, which means that executing them with the maximum possible number of threads will not always deliver the best outcome in performance, energy consumption, or the tradeoff between both (represented by the energy-delay product - EDP). Given that, several strategies, online and offfine, have already been proposed to rightly tune the number of threads according to the application. While the former can capture some behaviors that can only be known at runtime, the latter do not impose any execution overhead and can use more efficient and costly algorithms. However, these learning algorithms in static strategics may take several hours, precluding their use or a smooth migration across different systems. In this scenario, we propose a generic methodology for such offline strategies to significantly decrease the learning time by inferring the execution behavior of parallel applications using smaller input sets than the ones used by the target applications. through the execution of eighteen well-known benchmarks on two multicore processors, we show that our methodology is capable of converging to results that are very close to those that use the regular input set, but converging 84.7% faster, on average. We also show that such a strategy delivers better results than a dynamic one, presenting an EDP 7.7% lower, on average, when executing the applications with the number of threads found during learning. Finally, we also compare our learning methodology with an exhaustive search. It has an average learning cost (i.e., the time spent by our search algorithm to find the best configuration) of only 3.1% to optimize the EDP of the entire benchmark sett.

关键词： parallel computing runtime optimization systems thread-level parallelism exploitation energy-delay product

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Enhanced Microgrid Functions for Topology Reconfiguration and Fault Restoration 2020

Enhanced Microgrid Functions for Topology Reconfiguration an...

引用

the 9th international conference on Smart Media and applications

作者： Xiancheng Wang In-Ho Ra Kunsan National University S. Korea

ISBN: (纸本)9781450389259

the power systems are deeply reforming to meet future power demands. With the continuous emergence of new technologies, the novel power system represented by microgrid has received more attention, and the research on the integration of emerging technologies of microgrid has become more focused. In this paper, a microgrid communication framework based on 5G technology is proposed, which makes full use of the low communication delay of 5G technology and the computation capacity of cloud/edge computing to implement the reconfiguration of microgrid deployed with DG(s). Lastly, we estimate the computing power of the cloud servers to predict the loads, and preprocess the restoration Optimal Configuration Table (OCT) scheme for instant fault restoration in the microgrid.

关键词： distributed Energy Resources

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TASKWORK: A Cloud-aware Runtime System for Elastic Task-parallel HPC applications 9

TASKWORK: A Cloud-aware Runtime System for Elastic Task-para...

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9th international conference on Cloud computing and Services Science (CLOSER)

作者： Kehrer, Stefan Blochinger, Wolfgang Reutlingen Univ Parallel & Distributed Comp Grp Reutlingen Germany

ISBN: (纸本)9789897583650

With the capability of employing virtually unlimited compute resources, the cloud evolved into an attractive execution environment for applications from the High Performance computing (HPC) domain. By means of elastic scaling, compute resources can be provisioned and decommissioned at runtime. this gives rise to a new concept in HPC: Elasticity of parallel computations. However, it is still an open research question to which extent HPC applications can benefit from elastic scaling and how to leverage elasticity of parallel computations. In this paper, we discuss how to address these challenges for HPC applications with dynamic task parallelism and present TASKWORK, a cloud-aware runtime system based on our findings. TASKWORK enables the implementation of elastic HPC applications by means of higher-level development frameworks and solves corresponding coordination problems based on Apache ZooKeeper. For evaluation purposes, we discuss a development framework for parallel branch-and-bound based on TASKWORK, show how to implement an elastic HPC application, and report on measurements with respect to parallel efficiency and elastic scaling.

关键词： Cloud computing High Performance computing Task parallelism Elasticity of parallel Computations

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parallel Computation for Sparse Network Component Analysis 4th

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4th international conference on Advanced Machine Learning technologies and applications, AMLTA 2019

作者： Elsayad, Dina Hamad, Safwat Shedeed, Howida A. Tolba, M.F. Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9783030141172

the Gene regulatory network analysis is one of the gene expression data analysis tasks. Gene regulatory network goal is determining the topological order of genes interactions. Moreover, the regulatory network is a vital for understanding genes influence on each other. However, the main challenge confronting gene regulatory network algorithms is the massive data size. Where, the algorithm runtime is relative to the data size. this paper presents a parallel computation for Sparse Network Component Analysis (PSparseNCA) with application on gene regulatory network. PSparseNCA is a parallel version of SparseNCA. PSparseNCA enhanced the computation of SparseNCA using a distributed computing model. Where, the workload is distributed among P processing nodes, PSparseNCA is more efficient than SparseNCA. It achieved a better performance and its speedup reached 12.33. In addition, PsparseNCA complexity is O(NM2/P) instead of O(NM2) for SparseNCA. © 2020, Springer Nature Switzerland AG.

关键词： Gene expression

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Decentralized and Secure Cooperative Edge Node Grouping to Process IoT applications in Heterogeneous Smart Cyber-Physical Systems 17

Decentralized and Secure Cooperative Edge Node Grouping to P...

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17th international Bhurban conference on Applied Sciences and Technology (IBCAST)

作者： Mudassar, Muhammad Zhai, Yanlong Liao, Lejian Zahid, Muhammad Noaman Afzal, Fatima Beijing Inst Technol Sch Comp Sci Beijing 100081 Peoples R China Beijing Inst Technol Sch Opt & Photon Beijing 100081 Peoples R China Virtual Univ Pakistan Dept Comp Sci & Informat Technol Lahore Pakistan

ISBN: (纸本)9781728146768

Smart Cyber-Physical Systems (SCPS) has enhanced use of smart devices with numerous applications including smart cities, smart traffic management, smart cars, smart health care and smart grids. Core logic behind these applications usually require huge processing or massive data handling which are normally performed at cloud but the application will suffer from latency. Edge computing offers solution for latency aware computations at edge of the network but with limited resources available at edge nodes. this problem can be resolved by leveraging edge resources in a group and executing resource intensive task. Most previous studies deploy centralized methods for clustering but comes with overhead of cluster formation and management. In this article, key idea is to group heterogeneous edge nodes on task arrival in a decentralize way, and handle task allocation and execution in parallel on group devices to achieve its deadline. Our methodology will help to reduce traffic amount travelling towards cloud in case of resource intensive big tasks of SCPS applications. We have proposed an algorithm for decentralized group formation and presented task division and allocation methodology for parallel execution. Our results show that our technique is working while providing desired goals of reducing overall latency, and limiting network traffic as well as achieving higher ratio for number of tasks meeting their deadline.

关键词： Edge computing distributed computing Internet of things Smart Cyber-Physical Systems Collaborative computing

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RISK MODEL of APPLICATION of LIFTING MEthODS 9

RISK MODEL of APPLICATION of LIFTING METHODS

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9th international conference "distributed computing and Grid technologies in Science and Education", GRID 2021

作者： Bogdanov, Alexander Degtyarev, Alexander Shchegoleva, Nadezhda Khvatov, Valery Zaynalov, Nodir Kiyamov, Jasur Dik, Aleksandr Faradzhov, Anar Saint Petersburg State University 7-9 Universitetskaya emb. Saint Petersburg199034 Russia Samarkand Branch Tashkent University of Information Technology Uzbekistan Plekhanov Russian University of Economics 36 Stremyanny lane Moscow117997 Russia DGT Technologies AG

the article discusses the main provisions (methods, risk models, calculation algorithms, etc.) of the issue of organizing the protection of personal data (PD), based on the application of anonymization procedure. the authors reveal the relevance of the studied problem based on the tendency of the general growth of informatization and the further development of the Big Data technology. this circumstance leads to the need to use the so-called risk approach based on calculating the risk of PD as a probabilistic assessment of the amount of possible damage that the owner of the data resource may incur as a result of a successfully carried out information attack. For this purpose, the article describes an algorithm for calculating the risk of PD and proposes a risk model of the depersonalization procedure, which considers confidentiality problems arising both as a result of unauthorized access and as a consequence of planned data processing. To describe the risk model of the anonymization procedure, the types of attacks on the confidentiality of personal data, anonymization metrics and equivalence classes are analyzed, as well as the attacker's profiles and data distribution scenarios. thus, the choice of a risk model for the depersonalization procedure was justified, and calculations for the generated synthetic set of PDs were presented. As a conclusion, it should be noted that the model of anonymization risk assessment proposed and tested on synthetic data makes it possible to abandon the concept of guaranteed anonymized data, introducing certain boundaries for working with risks and building a continuous process for assessing PD threats, taking into account the constantly growing volume of stored and processed information. © 2021 CEUR-WS. All rights reserved.

关键词： Risk assessment

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IMpart: A Partitioning-based parallel Approach to Accelerate Influence Maximization

IMpart: A Partitioning-based Parallel Approach to Accelerate...

引用

international conference on High Performance computing

作者： Reet Barik Marco Minutoli Mahantesh Halappanavar Ananth Kalyanaraman Washington State University Pullman WA Pacific Northwest National Laboratory Richland WA

Influence maximization (IM) is a fundamental operation among graph problems that involve simulating a stochastic diffusion process on real-world networks. Given a graph G(V, E), the objective is to identify a small set of key influential "seeds"— i.e., a fixed-size set of k nodes, which when influenced is likely to lead to the maximum number of nodes in the network getting influenced. the problem has numerous applications including (but not limited to) viral marketing in social networks, epidemic control in contact networks, and in finding influential proteins in molecular networks. Despite its importance, application of influence maximization at scale continues to pose significant challenges. While the problem is NP-hard, efficient approximation algorithms that use greedy hill climbing are used in practice. However those algorithms consume hours of multithreaded execution time even on modest-sized inputs with hundreds of thousands of nodes. In this paper, we present IMpart, a partitioning-based approach to accelerate greedy hill climbing based IM approaches on both shared and distributed memory computers. In particular, we present two parallel algorithms— one that uses graph partitioning (IMpart-metis) and another that uses community-aware partitioning (IMpart-gratis)— with provable guarantees on the quality of approximation. Experimental results show that our approaches are able to deliver two to three orders of magnitude speedup over a state-of-the-art multithreaded hill climbing implementation with negligible loss in quality. For instance, on one of the modest-sized inputs (Slashdot: 73K nodes; 905K edges), our partitioning-based shared memory implementation yields 4610× speedup, reducing the runtime from 9h 36m to 7 seconds on 128 threads. Furthermore, our distributed memory implementation enhances problem size reach to graph inputs with ×10 6 nodes and ×10 8 edges and enables sub-minute computation of IM solutions.

关键词： Proteins Privacy Runtime Social networking (online) Instruction sets Pipelines Memory management

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Survey and Performance Test of Python-based Libraries for parallel Processing 2020

Survey and Performance Test of Python-based Libraries for Pa...

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the 9th international conference on Smart Media and applications

作者： TaeHong Kim YoonSeok Cha ByeonChun Shin ByungRae Cha Gwangju Metro-city S. Korea Chonnam National Uniersity S. Korea

ISBN: (纸本)9781450389259

关键词： Artificial Intelligence

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Optimum parallelism in Spark Framework on Hadoop YARN for Maximum Cluster Resource Utilization 1st

Optimum Parallelism in Spark Framework on Hadoop YARN for Ma...

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1st international conference on Sustainable technologies for Computational Intelligence (ICTSCI)

作者： Janardhanan, P. S. Samuel, Philip SunTec Business Solut Pvt Ltd TechnoPk Trivandrum Kerala India Cochin Univ Sci & Technol Dept Comp Sci Kochi Kerala India

ISBN: (纸本)9789811500299;9789811500282

Spark iswidely used as a distributed computing framework for in-memory parallel processing. It implements distributed computing by splitting the jobs into tasks and deploying them on executors on the nodes of a cluster. Executors are JVMs with dedicated allocation of CPU cores and memory. the number of tasks depends on the partitions of input data. Depending on the number of CPU cores allocated to executors, one or more cores get allocated to one task. Tasks run as independent threads on executors hosted on JVMs dedicated exclusively to the executor. One or more executors are deployed on the nodes of the cluster depending on the resource availability. the performance advantage provided by distributed computing on Spark framework depends on the level of parallelism configured at 3 levels, namely node level, executor level, and task level. the parallelism at each of these levels should be configured to fully utilize the available computing resources. this paper recommends optimum parallelism configuration for Apache Spark framework deployed on Hadoop YARN cluster. the recommendations are based on the results of the experiments conducted to evaluate the dependency of parallelism at each of these levels on the performance of Spark applications. For the purpose of the evaluation, a CPU-intensive job and an I/O-intensive job are used. the performance is measured by varying the parallelism at each of the 3 levels. the results presented in this paper help Spark users in selecting optimum parallelism at each of these levels for achieving maximum performance for Spark jobs by maximum resource utilization.

关键词： distributed computing Apache Spark Hadoop YARN SparkBench Spark configuration Multi-level parallelism Resource optimization

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