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Sorting is one of the basic algorithms in computer science, and has been extensively used in a variety of applications. In the big data era, as the volumes of data increase rapidly, parallel sorting has attracted much attention. Existing parallel sorting algorithms suffered from excessive communication overhead and load imbalance, making it difficult to scale massively. To solve above problems, a scalable parallel algorithm sorting by regular sampling (ScaPSRS) was proposed, which sampled the p-1 pivot elements to divide the entire data set into p disjoint subsets by all parallel processes, rather than by only one given process as PSRS did. Furthermore, ScaPSRS adopted a novel iterative update strategy of selecting pivots to guarantee that the workloads and data were evenly scheduled among the parallel processes, thus ensuring superior overall performance. A variety of experiments conducted on the Tianhe-Ⅱ supercomputer demonstrated that ScaPSRS succeeded in scaling to 32 000 cores and outperformed state-of-the-art works significantly. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the increase of embedding dimension, the computing scale of the permutation entropy algorithm will grow exponentially. Consequently, it puts forward higher requirements for the timeliness of calculation. The Sunway TaihuLight supercomputer is a totally independently designed and developed Chinese supercomputer with a new many-core processor, the SW26010. To fully take advantages of computing and storage resources of heterogeneous many-core cluster, the phase space matrix is divided between the core groups base on MPI, and internal parallelism bases on OpenACC in the core groups. By these methods, the permutation entropy algorithm is successfully transplanted and accelerated. The experimental results on dam oscillation dataset show that the performance of the single-core group version is 7. 18 times faster than that of the MPE version, and the speedup ratio can reach 85 . 6 when using 128 core groups. [ABSTRACT FROM AUTHOR]

The Sunway TaihuLight, which is composed of Sunway many-core processors, is currently the highest performance supercomputer in China. It can provide a hardware platform for NSGA-Ⅱ to solve the large-scale problems. Considering the architecture of Sunway many-core processor, this paper designed an island combined with enhanced master-slave hybrid parallel NSGA-Ⅱ algorithm. Based on the master-slave mode, it used register communication to realize the sharing of local data memory of CPE in a core group. It optimized the algorithm process and parallelized more algorithm modules on CPE. By means of DMA transmission, vectorization, double buffering and storage optimization, it significantly increased the speedup. The experiments show that the optimized parallel NSGA-Ⅱ has good speedup and scalability on the Sunway many-core processors. [ABSTRACT FROM AUTHOR]

An economic operation platform of micro-grid based on LabVIEW is designed by using flexible load control and improved particle swarm optimization. The display interface is designed by LabVIEW. The Matlab script component is used to implant the algorithm into the super-computer. Thus, a platform is constructed with the operation module. The platform has two working modes. Mode 1: PLC serves as the slave-computer, it will transmit real-time parameters such as wind speed and solar radiation to the super-computer. After optimized computation by LabVIEW, the control information is returned to the slave-computer for simulation action, and the operation curve is displayed in the monitoring interface; Mode 2: the model of wind turbines, photovoltaic arrays, batteries and gas turbine is established and programed as the data source of system operation. In addition, 24-hour forecast data such as wind speed and solar radiation can be written to the txt file, which can be read and optimal computation by the super-computer. This experimental platform provides strong support for electrical engineering major students to carry out experiments related to the economic operation of micro-grid. [ABSTRACT FROM AUTHOR]