Advances in Grid and Pervasive Computing: Third by Kai Hwang (auth.), Song Wu, Laurence T. Yang, Tony Li Xu

By Kai Hwang (auth.), Song Wu, Laurence T. Yang, Tony Li Xu (eds.)

This e-book constitutes the refereed court cases of the 3rd overseas convention on Grid and Pervasive Computing, GPC 2008, held in Kunming, China, in may well 2008.

The forty five revised complete papers offered including 2 keynote lectures have been rigorously reviewed and chosen from 184 submissions. The papers hide all present problems with grid and pervasive computing and concentrate on issues corresponding to cluster computing, grid computing, excessive functionality computing, community garage, peer-to-peer computing, pervasive computing, the Semantic net and the Semantic Grid, and service-oriented computing.

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Execution time of various thresholds Fig. 5. Execution time of each processor Tidset-Based Parallel FP-tree Algorithm for the Frequent Pattern Mining Problem Table 4. 02 5 Conclusions Mining frequent patterns form a transaction-oriented database is important in data mining research. Many methods have been proposed to solve this problem, and some of them have been developed for a parallel-distributed computing system. However, the execution time increases significantly with an increase in database size and a decrease in the given threshold.

MN performs the MPI All-Reduce function to collect the frequent pattern from . Fig. 1. An example of DB partitioning into 4 processors and the given threshold is 4 Experimental Results In order to evaluate the performance of the proposed algorithm, the TPFP-tree was implemented along with the PFP-tree algorithm with Message Passing Library 2 (MPICH2). The program was executed in a PC cluster with 16 computing nodes. Table 2 gives the hardware and software specifications. Synthesized datasets generated by IBM’s Quest Synthetic Data Generator [2] were used to verify the algorithm.

100 Data Blocks External Interior 80 64 Total Communications 68 64 60 56 60 72 80 64 80 76 40 12 20 0 12 12 0 0 4,4,8 4,5,8 4,6,8 4,7,8 4,8,8 Fig. 5. Evaluation of processor mapping technique (K=4, P=16, 17, 18, 19, 20) 6 Conclusions and Future Work In this paper, we have presented a generalized processor mapping technique for localizing distributions of parallel data. The data to logical grid nodes mapping technique is employed to enhance the performance of data parallel programs on cluster grid.

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