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检索条件"任意字段=International Conference on Parallel and Distributed Processing Techniques and Applications"

共 11314 条记录，以下是421-430 订阅

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Searching for a shortest K-element sidon set in parallel

Searching for a shortest K-element sidon set in parallel

引用

2005 international conference on parallel and distributed processing techniques and applications, PDPTA'05

作者： Trono, John A. Bays, Andrew J. Saint Michael's College Colchester VT 05439 United States University of Buffalo Buffalo NY 14261 United States

ISBN: (纸本)9781932415605

A set of integers, S, is called a Sidon set if the sum of any two elements in S is unique. If S has K elements. S produces K* (K + 1) /2 unique sums. This paper describes one approach for finding a shortest Sidon set of size K, where all the elements in the set are greater than zero {except for the smallest element, which equals zero) and the largest element is the smallest possible. This serial algorithm will then be converted to execute on a parallel system that utilizes one master processor, and a collection of slave processors, so that such a set can be found more quickly.

关键词： parallel processing systems

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An Efficient Framework for Target Search with Cooperative UAVs in a FANET 15

An Efficient Framework for Target Search with Cooperative UA...

引用

15th IEEE international Symposium on parallel and distributed processing with applications (ISPA) / 16th IEEE international conference on Ubiquitous Computing and Communications (IUCC)

作者： Li, Xianfeng Chen, Jie Peking Univ Sch Elect & Comp Engn Shenzhen Key Lab Informat Theory & Future Network Shenzhen Peoples R China

ISBN: (纸本)9781538637906

Flying Ad-Hoc Network (FANET) is a special type of Mobile Ad-Hoc Network (MANET) which consists of multiple networked Unmanned Aerial Vehicles (UAVs). Compared to traditional target search using a single UAV, multiple cooperative UAVs within a FANET have the potential to accomplish this work more efficiently, and multi-UAV cooperative target search has attracted increasing research attention in recent years. In this paper, we conduct a comprehensive investigation on efficient and accurate multi-UAV cooperative target search techniques. More specifically, we propose several adaptive mobility models and an innovative information fusion mechanism to improve the search efficiency. Meanwhile, to improve the search accuracy, we introduce a low-overhead decision-making mechanism to reduce chances for false target identifications. The simulation results show that the framework outperforms the baseline framework significantly in terms of both efficiency and accuracy. Our experiments also exhibit how individual mechanisms introduced in our work contribute to the improvements on search efficiency or accuracy, therefore providing more guidance for their adoption in practice.

关键词： Flying Ad-Hoc Network (FANET) Unmanned Aerial Vehicle (UAV) target search mobility model

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DPFS: A distributed parallel File System

DPFS: A Distributed Parallel File System

引用

30th international conference on parallel processing (ICPP 01)

作者： Shen, XH Choudhary, A Northwestern Univ Dept Elect & Comp Engn Ctr Parallel & Distributed Comp Evanston IL 60208 USA

ISBN: (纸本)0769512577;0769512585

One of challenges brought by large-scale scientific applications is how to avoid remote storage access by collectively using enough local storage resources to hold huge amount of data generated by the simulation while providing high performance I/O. DPFS, a distributed parallel File System, is designed and implemented to address this problem. DPFS collects locally distributed unused storage resources as a supplement to the internal storage of parallel computing systems to satisfy the storage capacity requirement of large-scale applications. In addition, like parallel file systems, DPFS provides striping mechanisms that divides a file into small pieces and distributes them across multiple storage devices for parallel data access. The unique feature of DPFS is that it provides three file levels with each file level corresponding to a file striping method. In addition to the traditional linear striping method, DPFS also provides a novel Multidimensional striping method that can solve performance problems of linear striping for many popular access patterns. Other issues such as load-balanceing and user interface are also addressed in DPFS.

关键词： Computational modeling Computer simulation distributed computing distributed power generation Ear File systems Large-scale systems Multidimensional systems parallel processing Spatial databases

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Predicting performance and power consumption of parallel applications 24

Predicting performance and power consumption of parallel app...

引用

24th Euromicro international conference on parallel, distributed, and Network-Based processing (PDP)

作者： De Sensi, Daniele Univ Pisa Dept Comp Sci I-56100 Pisa Italy

ISBN: (纸本)9781467387767

Current architectures provide many control knobs for the reduction of power consumption of applications, like reducing the number of used cores or scaling down their frequency. However, choosing the right values for these knobs in order to satisfy requirements on performance and/or power consumption is a complex task and trying all the possible combinations of these values is an unfeasible solution since it would require too much time. For this reasons, there is the need for techniques that allow an accurate estimation of the performance and power consumption of an application when a specific configuration of the control knobs values is used. Usually, this is done by executing the application with different configurations and by using these information to predict its behaviour when the values of the knobs are changed. However, since this is a time consuming process, we would like to execute the application in the fewest number of configurations possible. In this work, we consider as control knobs the number of cores used by the application and the frequency of these cores. We show that on most PARSEC benchmark programs, by executing the application in 1% of the total possible configurations and by applying a multiple linear regression model we are able to achieve an average accuracy of 96% in predicting its execution time and power consumption in all the other possible knobs combinations.

关键词： performance prediction power consumption prediction concurrency throttling DVFS linear regression

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Performance modeling and practical parallel algorithms of cluster computing with applications on distributed platforms

Performance modeling and practical parallel algorithms of cl...

引用

international conference on parallel and distributed processing techniques and applications

作者： Wang, H Wang, H Shen, J Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)1892512459

In this paper, we discuss processes and best practices for using cluster computing to solve business or scientific problems. We assess the impact of cluster computing on speedup and load balance in every step of parallelization, including the formulation of a problem, system modeling, computer modeling, algorithm development and implementation, task partitioning, mapping, and scheduling. We also develop a theoretical performance model for applications in cluster of computers. Our results show that for MPP-based parallel computing, the parallel computation time decreases with increasing number of processors;for cluster-based parallel computing, the parallel computation time first decreases with increasing number of processors;however, with further increase in the number of processors, the parallel computation time increases because of the increasing communication overhead. For MPP-based parallel computing, the speedup increases with increasing number of processors as predicted by John Gustafson who revised Amdahl's law. However, it is different for cluster computing. There exists a maximum speedup for a given problem with a given computation algorithm that occurs at the turning point number of processors in a cluster computing environment. The maximum speedup is less than half of the turning point number of processors, which is related to communication latencies, bandwidths, size of problem, and computation load. The more computation intensive the problem, the higher the turning point number of processors, the higher the speedup. We also examine our theory with our experiments, and the results show that for large domains, the maximum speedup is 19.6 and the turning point number of processors is 105;for small domains, the maximum speedup is 12.4 and the turning point number of processors is 56.

关键词： load balance cluster computing efficiency scalability and high availability optimal clusters distributed computing algorithm design and analyses

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Energy Efficient Resource Management and Task Scheduling for IoT Services in Edge Computing Paradigm 15

Energy Efficient Resource Management and Task Scheduling for...

引用

15th IEEE international Symposium on parallel and distributed processing with applications (ISPA) / 16th IEEE international conference on Ubiquitous Computing and Communications (IUCC)

作者： Li, Songyuan Huang, Jiwei Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

ISBN: (纸本)9781538637906

With the growing popularity of the Internet of Things (IoT), energy efficiency has been a critical concern during the design and development of IoT service systems. Meanwhile, edge computing has drawn significant attention as a burgeoning computing paradigm. This paper studies the energy efficiency issue of IoT systems by proposing a joint scheme of resource allocation and task scheduling under the edge computing paradigm. Specifically, dynamic processes of the IoT services and system are formulated by generalized queueing network models, based on which quantitative analyses of performance and energy consumption are conducted. The resource management and task scheduling are formulated by Markov Decision Process (MDP), which can balance the tradeoff between energy costs and QoS requirements. To attack the challenge of MDP search space explosion due to the large scale of IoT systems, Ordinal Optimization (OO) techniques are applied to the MDP algorithms, which are able to significantly narrow the search of MDP by slightly softening the optimization objective to a good enough subset. Finally, we conduct simulation experiments based on real-world IoT data. Evaluations and comparisons demonstrate that our approach is effective and efficient in practice.

关键词： Internet of Things (IoT) resource management task scheduling energy efficiency Markov Decision Process Ordinal Optimization

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Data integrity in a distributed storage system

Data integrity in a distributed storage system

引用

Proceedings of the international conference on parallel and distributed processing techniques and applications

作者： Bright, Jonathan D. Chandy, John A. Sigma Storage Corporation Emeryville CA United States Dept. of Electrical and Comp. Eng. University of Connecticut Storrs CT United States

ISBN: (纸本)1892512416

distributed storage systems must provide highly available access to data while maintaining high performance and maximum scalability. In addition, reliability in a storage system is of the utmost importance and the correctness and availability of data must be guaranteed. We have designed the Sigma cluster file system to address these goals by distributing data across multiple nodes and keeping parity across these nodes. With data spread across multiple nodes, however, ensuring the consistency of the data requires special techniques. In this paper, we describe fault tolerant algorithms to maintain the consistency and reliability of the file system - both data and metadata. We show how these techniques guarantee data integrity and availability even under failure mode scenarios.

关键词： distributed computer systems

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A dynamic priority based algorithm for distributed deadlock detection and resolution

A dynamic priority based algorithm for distributed deadlock ...

引用

international conference on parallel and distributed processing techniques and applications

作者： Wang, XL Mayo, J Michigan Technol Univ Dept Comp Sci Houghton MI 49931 USA

ISBN: (纸本)1932415262

In this paper we develop a distributed algorithm, using probes stamped with dynamic priorities, for detecting deadlocks in the single resource model. The correctness of the algorithm is proven. A dynamic priority is a pair (p, c) where p is a process and c is the counter value in p when p initiates a deadlock detection computation. The pair is called a detector when it is referenced in a process or a resource manager It is maintained in a process and a resource manager updated dynamically upon certain events, and used as the priority of a probe in a deadlock detection computation. Introduction of a dynamic priority to the probes, combined with a dynamically updated detector in a process and resource manager make the algorithm more efficient and less complex to implement than the existing ones. Our algorithm has an O(1) space complexity for each process. In certain cases, the algorithm uses n messages to detect a deadlock and has an O(n) detection delay where n is the number of processes and resource managers that are involved in the deadlock.

关键词： deadlock detection dynamic priority distributed system

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Pipelining/Overlapping Data Transfer for distributed Data-Intensive Job Execution

Pipelining/Overlapping Data Transfer for Distributed Data-In...

引用

42nd Annual international conference on parallel processing (ICPP)

作者： Jung, Eun-Sung Maheshwari, Ketan Kettimuthu, Rajkumar Argonne Natl Lab Div Math & Comp Sci Argonne IL 60439 USA

ISBN: (纸本)9780769551173

Scientific workflows are increasingly gaining attention as both data and compute resources are getting bigger, heterogeneous, and distributed. Many scientific workflows are both compute intensive and data intensive and use distributed resources. This situation poses significant challenges in terms of real-time remote analysis and dissemination of massive datasets to scientists across the community. These challenges will be exacerbated in the exascale era. parallel jobs in scientific workflows are common, and such parallelism can be exploited by scheduling parallel jobs among multiple execution sites for enhanced performance. Previous scheduling algorithms such as heterogeneous earliest finish time (HEFT) did not focus on scheduling thousands of jobs often seen in contemporary applications. Some techniques, such as task clustering, have been proposed to reduce the overhead of scheduling a large number of jobs. However, scheduling massively parallel jobs in distributed environments poses new challenges as data movement becomes a nontrivial factor. We propose efficient parallel execution models through pipelined execution of data transfer, incorporating network bandwidth and reserved resources at an execution site. We formally analyze those models and suggest the best model with the optimal degree of parallelism. We implement our model in the Swift parallel scripting paradigm using GridFTP. Experiments on real distributed computing resources show that our model with optimal degrees of parallelism outperform the current parallel execution model by as much as 50% reduction of total execution time.

关键词： electronic data interchange natural sciences computing parallel processing pipeline processing scheduling workflow management software

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Refactoring the Molecular Docking Simulation for Heterogeneous, Manycore Processors Systems 15

Refactoring the Molecular Docking Simulation for Heterogeneo...

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15th IEEE international Symposium on parallel and distributed processing with applications (ISPA) / 16th IEEE international conference on Ubiquitous Computing and Communications (IUCC)

作者： Chen, Junshi Lin, Han Liang, Weihao Yu, Yang Han, Wenting An, Hong Chen, Yong Liu, Xin Univ Sci & Technol China Sch Comp Sci & Technol Hefei Anhui Peoples R China Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA Natl Res Ctr Parallel Comp Engn & Technol Beijing Peoples R China

ISBN: (纸本)9781538637906

This paper presents a scalable design and implementation of the molecular docking application DOCK for a large-scale high performance computing system, the Sunway TaihuLight supercomputer, which provisions a heterogeneous, manycore processor architecture that consists of management processing elements (MPEs) and clusters of computing processing elements (CPEs). The key innovation is a novel refactoring of DOCK on the CPEs. Optimization techniques for data redundancy minimization to fit data in cache, software-controlled prefetching into scratchpads, memory access coalescing, software caches, vectorization and loop unrolling are employed to improve the exploitation of the computational resources. For a single docking process, the refactored version using both the MPE and CPE cluster achieved 260x to 402x speedup compared against the original ported version using MPE only. To scale the DOCK to the full Sunway Taihulight system with 10,649,600 cores (including all MPE and CPE cores), we present an MPI communication domain partition scheme as well. For docking 9 million small compounds to a Zika virus target protein, we manage to scale to 131,072 MPEs, and 8,388,608 CPEs, with a total of 8,519,680 cores.

关键词： molecular docking Sunway TaihuLight Supercomputer heterogeneous manycore optimization

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