By Yunji Chen, Paolo Ienne, Qing Ji
This e-book 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 provided during this quantity have been rigorously reviewed and chosen from 24 submissions. They take care of the hot advances in huge information processing; parallel architectures and platforms; parallel software program; parallel algorithms and purposes; and disbursed and cloud computing.
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Additional resources 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.
Finally, an eﬃcient memory management scheme is designed for MIC memory allocation, in which several types of buﬀer can be used by users. The Array Buﬀer has no synchronization overhead but a large memory space, while the Combine Buﬀer has a low synchronization overhead but a small memory space. Meanwhile, the task buﬀer is designed for storing the map tasks. All buﬀers are scalable for multi-threads and reused during a job running. A prototype system is implemented on a CPU-MIC heterogeneous cluster, which includes 8 CPUs and 6 MIC coprocessors.
Ovis-2: a robust distributed architecture for scalable RAS. In: IEEE International Symposium on Parallel and Distributed Processing, pp. cn Abstract. MapReduce is a distributed programming paradigm to process large scale data set. Meanwhile, with the development of coprocessors, heterogeneous architecture is widely used for getting high performance. Therefore, it is natural to try to leverage both of them for big data processing. In this paper, we propose an optimized MapReduce framework for CPU-MIC heterogeneous Cluster, which mainly provides the following new features: First, a runtime is developed for MIC management, fault tolerance, and task scheduling.