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Design of a high speed logic engine for distributed decision support systems

INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2007年第3期1卷 139-155页

作者： Bhattacharya, Alakananda Konar, Amit Mandal, Ajit K. Jadavpur Univ Dept Elect & Telecommun Engn Kolkata 700032 W Bengal India

The paper provides a new approach to designing a massively parallel logic engine for automated reasoning in a distributed decision support system. The design includes extension of classical linear resolution of first order logic clauses by multi-resolution, where a set of clauses can be resolved concurrently without sacrificing any inference, thereby speeding-up the execution of a logic program. The speed-up and utilization rate of resources are used as the performance evaluation metric to compare the performance of the proposed system with the classical one. A high level logic architecture of the proposed multi-resolution system is presented to explore possible parallelism and pipelining among the tasks, thus determining the execution time of typical logic programs. Possible application of the proposed system in query evaluation of a logic program based database systems is also introduced.

关键词： Concurrent resolution extended petri nets logic programs parallel and distributed processing

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An Incremental Algorithm for Frequent Itemset Mining on Spark 2

An Incremental Algorithm for Frequent Itemset Mining on Spar...

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2017 IEEE 2nd International Conference on Big Data Analysis（ICBDA 2017）

作者： Min Yu Chuang Zuo Yunpeng Yuan Yulu Yang College of Computer and Control Engineering Nankai University China Telecom Corporation Limited Beijing Research Institute

ISBN: (纸本)9781509036202

Frequent Itemset Mining is one of the most investigated fields of data *** is expensive to mine frequent itemsets for a large scale data *** when some data is added into the data set,it is still time-consuming from the scratch to re-compute the complete data set to update the frequent itemsets of the data *** to improve the performance of frequent itemset mining for large scale and dynamic data set,we propose a new incremental Apriori algorithm based on *** reuses existing results from previous computation to modify the frequent itemsets according to the newly added data,which avoid massive *** newly proposed algorithm also takes full advantage of distributed resources with the support of *** in theory ensures that the newly proposed algorithm is *** on the real-world data set demonstrate that the newly proposed algorithm effectively avoids reduplicated computation and improves the performance of frequent itemset mining with no additional storage overhead.

关键词： frequent itemset mining apriori incremental computing parallel and distributed processing spark

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parallel Modularity-based Community Detection on Large-scale Graphs

Parallel Modularity-based Community Detection on Large-scale...

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IEEE International Conference on Cluster Computing (CLUSTER)

作者： Zeng, Jianping Yu, Hongfeng Univ Nebraska Dept Comp Sci & Engn Lincoln NE 68588 USA

ISBN: (纸本)9781467365987

We present a parallel hierarchical graph clustering algorithm that uses modularity as clustering criteria to effectively extract community structures in large graphs of different types. In order to process a large complex graph (whose vertex number and edge number are around 1 billion), we design our algorithm based on the Louvain method by investigating graph partitioning and distribution schemes on distributed memory architectures and conducting clustering in a divide-and-conquer manner. We study the relationship between graph structure property and clustering quality, carefully deal with ghost vertices between graph partitions, and propose a heuristic partition method suitable for the Louvain method. Compared to the existing solutions, our method can achieve nearly well-balanced workload among processors and higher accuracy of graph clustering on real-world large graph datasets.

关键词： large graph community detection graph clustering parallel and distributed processing

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Effectively Unified Optimization for Large-scale Graph Community Detection

Effectively Unified Optimization for Large-scale Graph Commu...

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IEEE International Conference on Big Data (Big Data)

作者： Zeng, Jianping Yu, Hongfeng Univ Nebraska Dept Comp Sci & Engn Lincoln NE 68588 USA

ISBN: (纸本)9781728108582

In this paper, we present a unified graph clustering framework based on an asynchronous approach. We study the similarities among the Louvain algorithm and the Infomap algorithm. Based on their common features, we build an end-to-end optimized distributed framework for implementing both algorithms. By extending the existing asynchronous distributed framework for large-scale graphs traversal, we ensure both workload and communication balanced. Our extensive experiments show that our framework is correct and effective with different large real-world and synthetic datasets using up to 32,768 processors for the Louvain algorithm and 16,384 processors for the Infomap algorithm. The quality and the scalability of our framework are superior to the existing work.

关键词： large graph community detection graph clustering parallel and distributed processing scalability accuracy

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parallel Dependable Multi-population Differential Evolutionary Particle Swarm Optimization for Online Optimal Operational Planning of Energy Plants

Parallel Dependable Multi-population Differential Evolutiona...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Nishimura, Norihiro Fukuyama, Yoshikazu Matsui, Tetsuro Meiji Univ Grad Sch Adv Math Sci Tokyo Japan Fuji Elect Co Ltd Corp R&D Headquarters Tokyo Japan

ISBN: (纸本)9781538627266

This paper presents dependable parallel multi-population differential evolutionary particle swarm optimization (DEEPSO) for on-line optimal operational planning of energy plants. The problem can be formulated as a mixed integer nonlinear optimization problem (MINLP). Since Optimal operational planning of numbers of energy plants are calculated simultaneously in a data center, the problem is required to generate optimal operational planning as rapidly as possible considering control intervals and numbers of treated plants. One of the solutions for this challenge is speeding up by parallel and distributed processing (PDP). However, PDP utilizes numbers of processes and countermeasures for various faults of the processes should be considered. The problem requires successive calculation at every control interval for keeping customer services. Therefore, sustainable (dependable) calculation keeping appropriate solution quality are required even if some of the calculation results cannot be returned from distributed processes. Multi-population based evolutionary computation methods have been verified that they can improve solution quality. Using the proposed dependable parallel multi-population DEEPSO based method, it is observed that calculation time becomes about 4.3 times faster than the conventional sequential DEEPSO, and appropriate solution quality can be kept even if some of the calculation results cannot be returned from distributed processes.

关键词： Multi-population Differential Evolutionary Particle Swarm Optimization Dependability Energy Management System Optimal Operational Planning of Energy Plant parallel and distributed processing

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Coalescing idle workstations as a multiprocessor system using JavaSpaces and Java Web Start

Coalescing idle workstations as a multiprocessor system usin...

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8th IASTED International Conference on Internet and Multimedia Systems and Applications

作者： Atkinson, A Malhotra, V Univ Tasmania Sch Comp Hobart Tas 7001 Australia

ISBN: (纸本)0889864209

This paper describes a distributed system which aggregates the unused and usually wasted processing capacity of idle workstations. The aggregation is achieved through the use of now ubiquitous internet infrastructure and web technology. And, it delivers a powerful yet inexpensive execution environment for computationally intensive applications. The prototype system described here makes use of Sun Microsystems Jini technology, particularly JavaSpaces, along with Java Web Start, to produce a dynamic, flexible and reliable system. Two example applications used to evaluate the system are described: (a) the n-Queens. problem and (b) a parallel sorting (shearsort) application. The results of the evaluation clearly show that, for certain classes of applications, the system is capable of delivering significant performance.

关键词： parallel and distributed processing Java-based web applications Jini JavaSpaces

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distributed NSGA-II Sharing Extreme Non-dominated Solutions for Improving Accuracy and Achieving Speed-up

Distributed NSGA-II Sharing Extreme Non-dominated Solutions ...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Sato, Yuji Sato, Mikiko Miyakawa, Minami Hosei Univ Fac Comp & Informat Sci Tokyo Japan Tokai Univ Sch Informat & Telecommun Engn Tokyo Japan Sinshu Univ Fac Engn Dept Elect & Comp Engn Nagano Japan

ISBN: (纸本)9781728121536

A recent trend of multi-objective evolutionary algorithms is to approximate the Pareto optimal front with high accuracy by increasing the size of the population. On the other hand, the cconventional method NSGA-II algorithm widely used in multi-objective optimization ranks solutions using non-dominant sorting that increase in computational complexity proportional to the population size squared. In engineering applications, the increased execution time can be a problem. Also, even if standard techniques for parallel processing of genetic manipulation are applied to multi-objective evolutionary computation, it is difficult to speed up while maintaining the accuracy of the solution search. This paper proposes the distributed NSGA-II which performs a hierarchical non-dominated sorting in a many-core environment, and migration that shares the extreme solutions of the latest Pareto optimal solutions among all cores. In the evaluation, using the test functions provided by the NSGA-II source code and the constrained knapsack problem with two-objective and three-objective, we describe that our proposed method contributes to the improvement of solution diversity search and for enhancing solution accuracy and speeding up.

关键词： NSGA-II non-dominated sorting parallel and distributed processing many-core environment multi-objective evolutionary algorithms

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Efficient Anytime Anywhere Algorithms for Closeness Centrality in Large and Dynamic Graphs 30

Efficient Anytime Anywhere Algorithms for Closeness Centrali...

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30th IEEE International parallel and distributed processing Symposium (IPDPS)

作者： Santos, Eunice E. Korah, John Murugappan, Vairavan Subramanian, Suresh IIT Dept Comp Sci Chicago IL 60616 USA

ISBN: (纸本)9781509036820

Recent advances in social network analysis methodologies for large (millions of nodes and billions of edges) and dynamic (evolving at different rates) networks have focused on leveraging new high performance architectures, parallel/distributed tools and novel data structures. However, there has been less focus on designing scalable and efficient algorithms to handle the challenges of dynamism in large-scale networks. In our previous work, we presented an overarching anytime anywhere framework for designing parallel and distributed social network analysis algorithms that are scalable to large network sizes and can handle dynamism. A key contribution of our work is to leverage the anytime and anywhere properties of graph analysis problems to design algorithms that can efficiently handle network dynamism by reusing partial results, and by reducing re-computations. In this paper, we present an algorithm for closeness centrality analysis that can handle changes in the network in the form of edge deletions. Using both theoretical analysis and experimental evaluations, we examine the performance of our algorithm with different network sizes and dynamism rates.

关键词： social network analysis parallel and distributed processing centrality analysis dynamic graphs anytime algorithms

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Superlinear Speedup in HPC Systems: why and when?

Superlinear Speedup in HPC Systems: why and when?

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Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Ristov, Sasko Prodan, Radu Gusev, Marjan Skala, Karolj Univ Innsbruck Innsbruck Austria Univ Ss Cyril & Methodius FCSE Skopje Macedonia Rudjer Boskovic Inst Zagreb Croatia

ISBN: (纸本)9788360810903

The speedup is usually limited by two main laws in high-performance computing, that is, the Amdahl's and Gustafson's laws. However, the speedup sometimes can reach far beyond the limited linear speedup, known as superlinear speedup, which means that the speedup is greater than the number of processors that are used. Although the superlinear speedup is not a new concept and many authors have already reported its existence, most of them reported it as a side effect, without explaining why and how it is happening. In this paper, we analyze several different superlinear speedup types and define a taxonomy for them. Additionally, we present several explanations and cases of superlinearity existence for different types of granular algorithms (tasks), which means that they can be divided into many sub-tasks and scattered to the processors for execution. Apart from frequent explanation that having more cache memory in parallel execution is the main reason, we summarize other different effects that cause the superlinearity, including the superlinear speedup in cloud virtual environment for both vertical and horizontal scaling.

关键词： Cache memory load parallel and distributed processing performance

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A practical demonstration of network traffic generation

A practical demonstration of network traffic generation

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8th IASTED International Conference on Internet and Multimedia Systems and Applications

作者： Avallone, S Emma, D Pescapè, A Ventre, G Univ Naples Federico II Dipartimento Informat & Sistemist Naples Italy

ISBN: (纸本)0889864209

Simulation modeling of computer networks is an effective technique for evaluating, the performance of network, transport, and application-level protocols. Traffic generation is one of the key challenges in modeling and simulating the Internet. In this paper we present a real simulation tool for controlled traffic generation over real networks. This work presents a platform, named distributed Internet Traffic Generator (D-ITG), which allows to simulate Internet Traffic and accurately replicate appropriate stochastic processes for both IDT (Inter Departure Time) and PS (Packet Size) random variables over real IP networks. We believe that D-ITG shows interesting properties when compared to other traffic generators and that it permits to accurately analyze network performance.

关键词： parallel and distributed processing Internet tools modeling and simulation

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