Download Advanced Parallel Processing Technologies: 11th by Yunji Chen, Paolo Ienne, Qing Ji PDF

By Yunji Chen, Paolo Ienne, Qing Ji

ISBN-10: 3319232150

ISBN-13: 9783319232157

This ebook constitutes the court cases of the eleventh foreign Symposium on complicated Parallel Processing applied sciences, APPT 2015, held in Jinan, China, in August 2015. The eight papers offered during this quantity have been rigorously reviewed and chosen from 24 submissions. They take care of the hot advances in massive facts processing; parallel architectures and platforms; parallel software program; parallel algorithms and purposes; and allotted and cloud computing.

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Extra resources for Advanced Parallel Processing Technologies: 11th International Symposium, APPT 2015, Jinan, China, August 20-21, 2015, Proceedings

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However, the proposed scheduler can also deal with the multiple-to-one mapping problem by set a quota of applications for each core, as in the well-known college admission model [15]. 1 49 Scheduler Algorithm In the single-ISA HMPs, due to the heterogeneity of cores and the diverse resource demands of applications, it is likely that different applications/cores would prefer different cores/applications. Further more, the preferences of single application or core would vary across phases [1]. Therefore, to improve the efficiency of scheduling, one must take the diverse preferences of applications and cores into account.

Firstly, the Map function maps a pair of input data to another data domain: Map: < k1, v1 > → < k2, v2 > After map stage, the runtime shuffles all the intermediate results and groups the pairs according to the value of key. Then, the Reduce function is executed for each group and produces a collection of values in the same key: Reduce: < k2, list(v2) > → list(v3) With the rapid development of coprocessors, many studies for adapting MapReduce framework on CPU-coprocessor architecture have emerged.

Kumar et al. proposed a heuristic sampling-based scheduler and demonstrated that heterogeneous architecture could deliver better performance and energy efficiency [7]. IPC-driven is a similar sampling-based approach which avoids to sample all possible assignments [9]. Winter et al. proposed a combinatorial optimization scheduler to coordinate applications scheduling and power management [14]. In general, the sampling-based schedulers introduce significant overhead due to the frequent migration of applications between cores.

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