By Yunji Chen, Paolo Ienne, Qing Ji
This publication 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 awarded during this quantity have been rigorously reviewed and chosen from 24 submissions. They take care of the new advances in mammoth facts processing; parallel architectures and structures; parallel software program; parallel algorithms and purposes; and allotted and cloud computing.
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Additional info for Advanced Parallel Processing Technologies: 11th International Symposium, APPT 2015, Jinan, China, August 20-21, 2015, Proceedings
In general, the sampling-based schedulers introduce signiﬁcant overhead due to the frequent migration of applications between cores. Moreover, such sampling overhead increases rapidly with the number of core types in the system. To avoid the sampling overhead, a number of non-sample works were proposed. In [8, 13], the authors leveraged memory intensity such as cache miss rate or memory stalls to classify applications as memory-intensive or compute-intensive. Schedulers assign compute-intensive applications to the big cores for better performance and assign memory-intensive applications to the small cores for energy efﬁciency.
Due to the success in both theory and practice, this work has been awarded the 2012 nobel prize for economic . In the stable matching scheduler, to regard applications and cores as the two sets of individuals involved in the matching game, we need to know the preferences of each application and core, which are inferred by an Artiﬁcial Neural Networks (ANN) prediction model with limited hardware overhead. The detail of ANN prediction model will be described in Sect. 2. Stable Matching Scheduler 47 We leverage three different HMPs (4-core, 6-core, 8-core) and select a number of representative benchmarks from the SPEC CPU2006 benchmark suite  to evaluate our scheduler.
Figure 4 shows the 42 W. Wang et al. Fig. 5. Speedup over CPU cluster Fig. 6. Time breakdown evaluation results. 39x. The Word Count can get more beneﬁts from it, as there is more proportion of I/O and reduce phases during the execution. 2. K-Means can get the best performance, as Hadoop can hardly utilize the VPUs in map phase, while Sfm can use them eﬃciently. Especially, Word Count gets little beneﬁt from Sfm, as there is no SIMD operations in the map phase. Then, we compare our system running on the CPU-MIC cluster with Hadoop running on the ordinary CPU cluster.