Algorithms and Architectures for Parallel Processing: 13th - download pdf or read online

By Antonio Balzanella, Rosanna Verde (auth.), Joanna Kołodziej, Beniamino Di Martino, Domenico Talia, Kaiqi Xiong (eds.)

ISBN-10: 3319038583

ISBN-13: 9783319038582

ISBN-10: 3319038591

ISBN-13: 9783319038599

This quantity set LNCS 8285 and 8286 constitutes the court cases of the thirteenth overseas convention on Algorithms and Architectures for Parallel Processing, ICA3PP 2013, held in Vietri sul Mare, Italy in December 2013. the 1st quantity comprises 10 unique and 31 average papers chosen from ninety submissions and masking issues similar to massive information, multi-core programming and software program instruments, disbursed scheduling and cargo balancing, high-performance clinical computing, parallel algorithms, parallel architectures, scalable and dispensed databases, dependability in disbursed and parallel platforms, instant and cellular computing. the second one quantity comprises 4 sections together with 35 papers from one symposium and 3 workshops held at the side of ICA3PP 2013 major convention. those are thirteen papers from the 2013 foreign Symposium on Advances of allotted and Parallel Computing (ADPC 2013), five papers of the overseas Workshop on giant info Computing (BDC 2013), 10 papers of the foreign Workshop on relied on info in great info (TIBiDa 2013) in addition to 7 papers belonging to Workshop on Cloud-assisted shrewdpermanent Cyber-Physical platforms (C-Smart CPS 2013).

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Extra resources for Algorithms and Architectures for Parallel Processing: 13th International Conference, ICA3PP 2013, Vietri sul Mare, Italy, December 18-20, 2013, Proceedings, Part I

Example text

A key aspect for the effectiveness of our approach is that it is performed at runtime (albeit with very low overheads). In contrast with this, Afek et al. [1] integrated static analysis in Deuce STM to eliminate redundant read and write operations in transactional methods, including accesses to transaction local-data. Yet, the results presented in their work are far from the speedups shown with our approach. M. Carvalho and J. Cachopo Conclusions and Future Work STMs are often criticized for introducing unacceptable overhead when compared with either the sequential version or a lock-based version of any realistic benchmark.

We have chose three datasets in the evaluation process: The first is made by 76 highly evolving time series, downloaded from Yahoo finance where the observations are the daily closing price of several random chosen stocks. Each time series is made by 4000 observations. The second is made by 179 highly evolving time series which collect daily electricity supply at several locations in Australia. Each time series is made by 3288 observations. The third is a simulated dataset consisting in n = 100 time series each having 6, 000 observations.

SADHANA, Academy Proceedings in Engineering Sciences 31(2), 173–198 (2006) 22. : Temporal Data Mining. CRC Press (2010) ISBN:9781420089769 23. : Hierarchical Clustering of Time Series Data Streams. IEEE Transactions on Knowledge and Data Engineering 20(5) (2008) 24. : Random sampling with a reservoir. ACM Trans. Math. Softw. 11, 37–57 (1985) 25. : Mining Data Streams. , Rokach, L. ) The Data Mining and Knowledge Discovery Handbook 2005. pt Abstract. Software Transactional Memory (STM) implementations typically instrument each memory access within transactions with a call to an STM barrier to ensure the correctness of the transactions.

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Algorithms and Architectures for Parallel Processing: 13th International Conference, ICA3PP 2013, Vietri sul Mare, Italy, December 18-20, 2013, Proceedings, Part I by Antonio Balzanella, Rosanna Verde (auth.), Joanna Kołodziej, Beniamino Di Martino, Domenico Talia, Kaiqi Xiong (eds.)


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