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Parallel String Matching for Urdu Language Text 1

1st International Conference on Intelligent Technologies and Applications (INTAP)

作者： Baig, Mirza Baber Li, Taoshen S. Guangxi Univ 100 Daxue Rd Nanning 530004 Guangxi Peoples R China

ISBN: (数字)9789811360527

ISBN: (纸本)9789811360527

String matching is one of the essential problems in computer science. The language used in Pakistan is Urdu. For Urdu language texts, its characters are encoded by utf-8, and the utf-8 is a length-variable encoding. If we implement string matching algorithms for Urdu language texts by ASCII encoding, the correct matched positions may not be obtained. This paper analyzes the characteristics of Urdu language texts and studies the character encoding presentation for Urdu language texts and recognizes that the correct matched positions can be obtained when the wchar_t type and Unicode encoding is used to process Urdu language texts, then, this paper implements parallel algorithms for Boyer-Moore string matching, Knuth-Morris-Pratt string matching, and Sunday string matching for Urdu language texts and evaluate the execution performance of these four string matching algorithms on a large number of Urdu language patterns and text strings via experimental testing.

关键词： Urdu language texts utf-8 encoding String matching multi-thread parallel algorithms multi-core computing

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Performance Engineering for Scientific computing with R

Performance Engineering for Scientific Computing with R

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2018第三届应用数学与数据科学国际会议

作者： Hui Zhang Computer Engineering and Computer Science Department University of Louisville

R has been adopted as a popular data analysis and mining tool in many domain fields over the past *** Big Data overwhelms those fields,the computational needs and workload of existing R solutions increases *** recent hardware and software developments,it is possible to enable massive parallelism with existing R solutions with little to no *** this paper,three different approaches are evaluated to speed up R computations with the utilization of the multiple cores,the Intel Xeon Phi SE10 P Co-processor,and the general purpose graphic processing unit(GPGPU).Performance engineering and evaluation efforts in this study are based on a popular R benchmark *** paper presents preliminary results on running R-benchmark with the above packages and hardware technology combinations.

关键词： Performance Evaluation R Intel Xeon Phi multi-core computing GPGPU

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Automatic parallelization of XQuery programs on multi-core systems

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JOURNAL OF SUPERcomputing 2016年第4期72卷 1517-1548页

作者： Chen, Rongxin Liao, Husheng Wang, Zongyue Su, Hang Jimei Univ Comp Engn Coll Xiamen Peoples R China Beijing Univ Technol Sch Software Engn Beijing Peoples R China Beijing Univ Technol Coll Comp Sci Beijing Peoples R China

The popularity of multi-core systems makes software parallelization become an important way to improve performance. As a mainstream XML query language, XQuery is the core of XML processing. It is critical to take full advantage of multi-core computing to improve XML processing performance through parallelization of XQuery. However, usually it is difficult to parallelize XQuery programs because of the nested style of XQuery expressions. Moreover, implicit parallelism is necessary to simplify the development of parallel XML application. In this paper, we propose an automatic parallelization approach, which can automatically select proper types of parallelism for a specific XQuery query. Specifically, we propose a functional intermediate language called pFXQL (parallel Functional XML Query Language) to describe parallel query plans. pFXQL has parallel semantics and is well complied with XQuery. We propose a cost model to effectively support plan generation and the selection of a preferred plan. The model estimates both computational cost and parallel cost. We implement our approach in XQuery engine and conduct experiments on various multi-core systems. Experimental results verify the effectiveness of our approach.

关键词： XQuery Automatic parallelization Intermediate language Cost model multi-core computing

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qcAffin: A Hardware Topology Aware Interrupt Affinitizing and Balancing Scheme for multi-core and multi-Queue Packet Processing Systems

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2016年第6期27卷 1783-1795页

作者： Huang, Nen-Fu Tsai, Wen-Yen Natl Tsing Hua Univ Inst Commun Engn Hsinchu Taiwan Natl Tsing Hua Univ Dept Comp Sci Hsinchu 30043 Taiwan

Interrupt affinitization of multi-queue network interface cards is a fundamental composition that defines how packets from individual queue are processed by which CPU-cores on multi-core platforms. In this paper, we propose qcAffin to attain an optimal queue-to-core affinitization for packet processing systems based on a numerical cost model derived from hardware topology and runtime system workloads. Static architectural characteristics comprising the memory hierarchy and topology of hardware components are first analyzed to calculate static interrupt affinitization costs. Then we attempt dynamic interrupt affinitization to balance workloads on CPU-cores and improve overall performance. Classical networking applications ranging from bridging, routing, access control list (ACL) matching to deep packet inspection (DPI) with different frame sizes are extensively experimented to compare the performance of the proposed scheme and other existing approaches. As demonstrated in the comparison result, qcAffin achieves the similar performance of the best affinitization approach and outperforms the Linux default affinitizer by averages of 102, 278, 248 and 131 percent on 1G NICs for the four applications. On 10G NICs, dramatic boosts of 1,424 and 1,343 percent are measured for the bridging and routing applications, respectively. Moreover, the effectiveness of dynamic interrupt balancing is justified by a maximum of 150 percent higher system utilization and 1.2 Mpps more throughput compared to the fixed affinitization approach in a simulated setup of unbalanced traffic load.

关键词： Interrupts affinitization multi-core computing interrupts balancing

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Parallel multi-core hyper-heuristic GRASP to solve permutation flow-shop problem

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2017年第9期29卷

作者： Alekseeva, Ekaterina Mezmaz, Mohand Tuyttens, Daniel Melab, Nouredine Univ Mons Math & Operat Res Dept MARO Mons Belgium Sobolev Inst Math Novosibirsk Russia Univ Lille 1 CNRS CRIStAL Inria Lille Nord Europe F-59655 Villeneuve Dascq France

In this paper, we aim to propose a parallel multi-core hyper-heuristic based on greedy randomized adaptive search procedure (GRASP) for the permutation flow-shop problem with the makespan criterion. The GRASP is a well-known two-phase metaheuristic. First, a construction phase builds a complete solution iteratively, component by component, by a greedy randomized algorithm. After that, a local search phase improves this solution. The choice of a component and the order in which it is added in a solution mostly depend on its incremental cost. Thus, a basic GRASP configuration is defined by a cost function, a probabilistic parameter of greediness and a neighbourhood structure. We consider five cost functions and seven well-known neighbourhood structures. In this paper a cost function based on a bounding operator is integrated in GRASP for the first time. Mechanisms that investigate automatically algorithm configurations refer to hyper-heuristics. Our hyper-heuristic investigates 315 GRASP configurations and reports which one produces better results. Parallel multi-core computing is used as a way to efficiently implement the hyper-heuristic. Taillard's benchmark instances are used to test the hyper-heuristic for the permutation flow-shop problem. Copyright (c) 2016 John Wiley & Sons, Ltd.

关键词： multi-core computing parallel computing metaheuristics scheduling problems hyper-heuristics

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Parallel Dual Coordinate Descent Method for Large-scale Linear Classification in multi-core Environments 16

Parallel Dual Coordinate Descent Method for Large-scale Line...

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22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Chiang, Wei-Lin Lee, Mu-Chu Lin, Chih-Jen Natl Taiwan Univ Dept Comp Sci Taipei Taiwan

ISBN: (纸本)9781450342322

Dual coordinate descent method is one of the most effective approaches for large-scale linear classification. However, its sequential design makes the parallelization difficult. In this work, we target at the parallelization in a multi-core environment. After pointing out difficulties faced in some existing approaches, we propose a new framework to parallelize the dual coordinate descent method. The key idea is to make the majority of all operations (gradient calculation here) parallelizable. The proposed framework is shown to be theoretically sound. Further, we demonstrate through experiments that the new framework is robust and efficient in a multi-core environment.

关键词： dual coordinate descent linear classification multi-core computing

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Towards a heterogeneous and adaptive parallel Branch-and-Bound algorithm

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JOURNAL OF COMPUTER AND SYSTEM SCIENCES 2015年第1期81卷 72-84页

作者： Chakroun, I. Melab, N. Univ Lille 1 LIFL UMR CNRS 8022 INRIA Lille Nord Europe F-59655 Villeneuve Dascq France

In this work, we revisit the design and implementation of the Branch-and-Bound (B&B) algorithm for heterogeneous environments combining multi-core processors with GPU accelerators. The challenge is to automatically identify the best mapping of computations to the target heterogeneous platform and to adjust the problem, algorithmic and platform parameters. In the proposed meta-algorithm, four hardware configuration scenarios have been considered: 1CPU-1GPU, nCPU-OGPU, nCPU-1GPU, nCPU-nGPU. Over a serial B&B, accelerations up to x222 have been achieved for large problem instances of the Flow-Shop scheduling problem. The reported results show that: (1) the GPU-accelerated algorithm performs better than its multi-core CPU-based version;(2) combining multi-core and GPU allows improvement up to 36% over a single CPU-GPU execution;(3) the more GPU devices are used, the better the speedups are whatever is the considered problem instance. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Heterogeneous computing multi-core computing GPU computing Parallel Branch-and-Bound algorithms

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Towards a heterogeneous and adaptive parallel Branch-and-Bound algorithm

Towards a heterogeneous and adaptive parallel Branch-and-Bou...

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14th IEEE International Conference on High Performance computing and Communications (HPCC) / IEEE 9th International Conference on Embedded Software and Systems (ICESS)

作者： Chakroun, I. Melab, N. Univ Lille 1 LIFL UMR CNRS 8022 INRIA Lille Nord Europe F-59655 Villeneuve Dascq France

关键词： Heterogeneous computing multi-core computing GPU computing Parallel Branch-and-Bound algorithms

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Parallel Tools for Simulating the Depolarization Block on a Neural Model

Parallel Tools for Simulating the Depolarization Block on a ...

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15th Annual International Conference on Computational Science (ICCS)

作者： Cuomo, Salvatore De Michele, Pasquale Galletti, Ardelio Ponti, Giovanni Univ Naples Federico II Naples Italy ENEA Res Ctr Naples Italy Univ Naples Parthenope Naples Italy

The prototyping and the development of computational codes for biological models, in terms of reliability, efficient and portable building blocks allow to simulate real cerebral behaviours and to validate theories and experiments. A critical issue is the tuning of a model by means of several numerical simulations with the aim to reproduce real scenarios. This requires a huge amount of computational resources to assess the impact of parameters that influence the neuronal response. In this paper, we describe how parallel tools are adopted to simulate the so-called depolarization block of a CA1 pyramidal cell of hippocampus. Here, the high performance computing techniques are adopted in order to achieve a more efficient model simulation. Finally, we analyse the performance of this neural model, investigating the scalability and benefits on multi-core and on parallel and distributed architectures.

关键词： Parallel computing Computational Neuroscience multi-core computing

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A Modified Ant Colony Optimization Algorithm for Implementation on multi-core Robots

A Modified Ant Colony Optimization Algorithm for Implementat...

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Swarm/Human Blended Intelligence Workshop (SHBI 2015)

作者： Krentz, Timothy Greenhagen, Chase Roggow, Aaron Desmond, Danielle Khorbotly, Sami Valparaiso Univ Dept Elect & Comp Engn Valparaiso IN 46383 USA

ISBN: (纸本)9781467365222

The Ant Colony Optimization (ACO) algorithm is an evolutionary algorithm that bio-mimics the behavior of ants in finding the shortest path between an origin and a destination within a set of pre-determined constraints. The goal of this work is to create a small-scale application of the ACO using a swarm of small autonomous robots. We investigate the practical applicability of the algorithm in real-life situations by addressing the issues and challenges encountered in the transition from the modeling/simulation level to the real-life application of the algorithm. We also suggest some modifications that will make feasible the implementation of the algorithm on the robots' limited computing systems. The results show that the suggested modified algorithm, when implemented on the robotic swarm, enables them to successfully identify the shortest path between two points. These results open the door to a wide variety of applications like search & rescue, path planning, and mass transportation.

关键词： swarm robots ant colony optimization multi-core computing

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