Algorithms and Architectures for Parallel Processing: 16th by Jesus Carretero, Javier Garcia-Blas, Ryan K.L. Ko, Peter PDF

By Jesus Carretero, Javier Garcia-Blas, Ryan K.L. Ko, Peter Mueller, Koji Nakano

ISBN-10: 3319495828

ISBN-13: 9783319495828

ISBN-10: 3319495836

ISBN-13: 9783319495835

This ebook constitutes the refereed court cases of the sixteenth foreign convention on Algorithms and Architectures for Parallel Processing, ICA3PP 2016, held in Granada, Spain, in December 2016.

The 30 complete papers and 22 brief papers provided have been conscientiously reviewed and chosen from 117 submissions. They disguise many dimensions of parallel algorithms and architectures, encompassing basic theoretical methods, functional experimental tasks, and advertisement elements and platforms attempting to push past the boundaries of current applied sciences, together with experimental efforts, cutting edge platforms, and investigations that determine weaknesses in current parallel processing technology.

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Extra resources for Algorithms and Architectures for Parallel Processing: 16th International Conference, ICA3PP 2016, Granada, Spain, December 14-16, 2016, Proceedings

Sample text

When there is a choice, the values in bold are the default ones. We use PIN [18] to write the profiler. We use three sets of benchmarks for evaluation (Table 2). The first set has implementations of concurrent data structures and mutual exclusion algorithms that have potential SCVs [5,6]. , MySQL, Gcc, Cilk). Finally, we use eight applications from SPLASH-2 and two applications from Parsec. SCV Detection. e. SPLASH2 & Parsec) 5 times. In each run, we force different interleavings by introducing some randomness.

However, the programs can have occasional data races (intentional or unintentional) and hence, SCVs (Sect. 2 discusses how data races and SCVs are related). The situation gets complicated when the memory model specifications of commercial processors from Intel and AMD do not even match the actual behavior of the machines [27]. Therefore, programmers may not be able to reason about SC behavior with those specifications. To make things worse, many work on semantics and software checking [26] that can potentially make parallel programming easier, would not be useful in the presence of SCVs.

Based on this notation, the application of a relaxation policy to this query produces a relaxed query Q = (D, δ ), with δ defining the RDF graph pattern of the query. To this end, a similarity metric S(Q , Q) has been designed to provide a quantitative estimation of the similarity between queries Q and Q. This similarity metric is designed to replace the generic function P (Q , Q). Basically, S(Q , Q) maps two SPARQL queries to a real value in the closed interval [0, 1], such that higher values indicate that queries are more similar to each other.

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Algorithms and Architectures for Parallel Processing: 16th International Conference, ICA3PP 2016, Granada, Spain, December 14-16, 2016, Proceedings by Jesus Carretero, Javier Garcia-Blas, Ryan K.L. Ko, Peter Mueller, Koji Nakano


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