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

By Yunji Chen, Paolo Ienne, Qing Ji

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

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Advances and challenges in log analysis. Commun. ACM 55(2), 55–61 (2012) 32 W. Hu et al. 14. : Dynamic syslog mining for network failure monitoring. In: KDD 2005, New York, NY, USA, pp. 499–508 (2005) 15. : Detecting large-scale system problems by mining console logs. In: SOSP 2009, NY, USA, pp. 117–132 (2009) 16. : A breadth-first algorithm for mining frequent patterns from event logs. , Wuwongse, V. ) INTELLCOMM 2004. LNCS, vol. 3283, pp. 293–308. Springer, Heidelberg (2004) 17. : Fault prediction under the microscope: a closer look into HPC systems.

To efficiently schedule single-ISA HMPs, we propose a novel stable matching scheduler based on the matching game theory. The proposed scheduler can always lead to a stable matching between applications and cores, where there does not exist an alternative application-core pair in which both the application and the core prefer each other rather than their current partners. Experimental results demonstrate that the stable matching scheduler beats previous schedules. 78 % (8-core). Keywords: Single-ISA hmps Stable matching Á Scheduling Á Multi-programmed workloads Á 1 Introduction Homogeneous multi-core processors have become mainstream over the past few decades.

The implementation are described briefly as follows. K-Means (KM): K-Means clustering is a method of grouping thousands of items into k groups. Each map task contains n items. The map task calculates the distance from each item to all central nodes of the k groups, then finds the group with the minimum distance to the given item. The reduce task sums all items within the same group and recalculates the central node. The map and reduce stage won’t stop until all the central nodes aren’t changed anymore or reach the iteration number predefined.

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