Download Algorithms and Architectures for Parallel Processing: 11th by David Abramson (auth.), Yang Xiang, Alfredo Cuzzocrea, PDF

By David Abramson (auth.), Yang Xiang, Alfredo Cuzzocrea, Michael Hobbs, Wanlei Zhou (eds.)

This quantity set LNCS 7016 and LNCS 7017 constitutes the refereed lawsuits of the eleventh overseas convention on Algorithms and Architectures for Parallel Processing, ICA3PP 2011, held in Melbourne, Australia, in October 2011.
The first quantity provides 24 revised common papers and 17 revised brief papers including the summary of the keynote lecture - all conscientiously reviewed and chosen from eighty five preliminary submissions. The papers hide the numerous dimensions of parallel algorithms and architectures, encompassing basic theoretical techniques, functional experimental effects, and advertisement elements and platforms and concentrate on wide parts of parallel and dispensed computing, i.e., architectures, algorithms and networks, and structures and applications.

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Additional info for Algorithms and Architectures for Parallel Processing: 11th International Conference, ICA3PP, Melbourne, Australia, October 24-26, 2011, Proceedings, Part I

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Fig(b) if construct ure 3 illustrates these two cases. However, in contrast to [12] and [11] that are limited to if and if-else constructs, we’re also able to identify loops. This is Fig. 3. Finding the repossible, because of using our loop detection patterns convergence point (black introduced in [3,4]. Furthermore we support non-local filled circle) jumps caused by break, continue or return. 2 Adapting the Lockset Algorithm As mentioned, the original lockset algorithm checks if every access to a shared resource obeys a certain locking discipline that ensures mutual exclusion.

First and foremost, we Fig. 1. Function with concurrency bug must extend the definition of data races to capture scenarios like the one we just described. Therefore, two aspects of data races need reconsideration: Spatial Aspect: Instead of single memory locations, we must monitor sets of correlated variables that share a semantic consistency property. We call such sets correlated sets. In the example above s = {x, y} is one such correlated set. Temporal Aspect: A logical operation on a correlated set that preserves its consistency property may consist of several elementary reads or writes.

Extending our scheme to support the data collection at intermediate nodes results in another problem. The aggregation result at each intermediate node will be based on the data of its child nodes and its own data. We need to get the sensor reading collected by an intermediate node to recalculate the aggregation result in the verification phase. However, the intermediate node may forge a false reading of its own. Such a node is more likely a faulty sensor, which can be detected via various existing techniques [7,20,21].

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