Get Algorithms - ESA 2015: 23rd Annual European Symposium, PDF

By Nikhil Bansal, Irene Finocchi

ISBN-10: 3662483491

ISBN-13: 9783662483497

ISBN-10: 3662483505

ISBN-13: 9783662483503

This ebook constitutes the refereed lawsuits of the twelfth overseas Colloquium on Theoretical elements of Computing, ICTAC 2015, held in Cali, Colombia, in October 2015. The 25 revised complete papers awarded including 7 invited talks, three device papers, and a pair of brief papers have been conscientiously reviewed and chosen from ninety three submissions. The papers disguise a variety of themes resembling algebra and classification thought; automata and formal languages; concurrency; constraints, good judgment and semantic; software program structure and component-based layout; and verification.

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Additional resources for Algorithms - ESA 2015: 23rd Annual European Symposium, Patras, Greece, September 14-16, 2015, Proceedings

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In part, this is probably due to the lack of a simple theoretical model of computation for GPUs. This has started to change recently, with introduction of several theoretical models for algorithm analysis on GPUs. A Brief Overview of GPU Architecture. A modern GPU contains hundreds of physical cores. To implement such a large number of cores, a GPU is designed hierarchically. It consists of a number of streaming multiprocessors Work supported in part by the Danish National Research Foundation grant DNRF84 through Center for Massive Data Algorithmics (MADALGO).

ESA 2015, LNCS 9294, pp. 13–24, 2015. 1007/978-3-662-48350-3 2 14 P. Afshani and N. Sitchinava SM w SM sh. mem w sh. mem Global memory Fig. 1. A schematic of GPU architecture (SMs) and a global memory shared by all SMs. Each SM consists of a number of cores (for concreteness, let us parameterize it by w) and a shared memory of limited size which is shared by all the cores within the SM but is inaccessible by other SMs. With computational power of hundreds of cores, latency of accessing memory becomes non-negligible and GPUs take several approaches to mitigate the problem.

Y ∈ 0, p1ab ln 1−p ab Next we consider items of Aˆb in increasing order of Yab . Let αab ∈ [ 12 , 1] be a dumping factor that we will define later. With probability αab we probe edge ab and as usual we stop the process (of probing edges incident to b) if ab is present. Otherwise (with probability 1 − αab ) we simulate the probe of ab, meaning that with probability pab we stop the process anyway — like if edge ab were probed and turned out to be present. Note that we do not get any profit from the latter simulation since we do not really probe ab.

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Algorithms - ESA 2015: 23rd Annual European Symposium, Patras, Greece, September 14-16, 2015, Proceedings by Nikhil Bansal, Irene Finocchi


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