By Yunji Chen, Paolo Ienne, Qing Ji
This ebook constitutes the lawsuits 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 care for the new advances in gigantic facts processing; parallel architectures and structures; parallel software program; parallel algorithms and purposes; and dispensed and cloud computing.
Read or Download Advanced Parallel Processing Technologies: 11th International Symposium, APPT 2015, Jinan, China, August 20-21, 2015, Proceedings PDF
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Additional resources for Advanced Parallel Processing Technologies: 11th International Symposium, APPT 2015, Jinan, China, August 20-21, 2015, Proceedings
45–59, 2015. 1007/978-3-319-23216-4_4 46 L. Wang et al. g. ) [2, 3]. In non-identical-ISA HMPs, cores have non-identical ISAs [4, 5] and different architectures correspondingly. In this paper, we focus on single-ISA HMPs, since it maximizes the flexibility of scheduling. The overall performance of single-ISA HMPs mainly relies on the efﬁciency of scheduling, which dynamically assigns applications to the appropriate cores. However, since the current operate system is unaware of the heterogeneity of architectures, the inefﬁcient random algorithm and round-robin algorithm are the most frequently used .
However, one challenge is that how to dig the huge value from the large scale data set whin an acceptable time. To deal with this urge, MapReduce framework  is proposed by Google, which could process a huge data set by utilizing the computing resources in parallel. Moreover, developers only need to write the map and reduce functions, and the other works are handled by MapReduce runtime automatically. Therefore, the open-source implementations, such as Hadoop and Spark, have been widely used in internet enterprises and research communities.
Overall, the goal of our work is to utilize the power of CPU-MIC cluster to process large scale data set in eﬃcient, fault tolerant, and easy programming ways. 1 The Overall Framework Like other MapReduce frameworks, the overall workﬂow of our system includes task distribution, map, shuﬄe, and reduce, etc. The most diﬀerence is that the tasks are not only processed by CPUs, but also by MIC coprocessors. Figure 1 shows the overall framework. The job manager manages a MapReduce job. When a new job comes, it splits the job into small tasks ﬁrstly.