Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2020-09-28 Cooperative journals: 《计算机应用研究》
Abstract： 3d seismic acoustic wave theory and calculation method are the basis of geological exploration research. By analyzing the propagation characteristics of acoustic waves in different media, we can apply the 3d seismic acoustic wave forward mod-eling in exploration work. In order to solve the problem of huge numerical calculation and large memory consumption while proceeding 3D seismic wave equation staggered grid finite difference forwarding model, we studied and implemented the parallel optimization on the heterogeneous many-core processors of the Sunway Taihulight supercomputer. Based on the implementation of a two-level parallel programming model by using MPI+Sunway Athread, we generated the DMA communication, 2.5D pipelining task division and other optimization strategies. The model with this improvement reduced the negative effects by bandwidth and greatly utilized the computing power. In large-scale conditions, we tackled the issue of low efficiency about program execution on SW26010 het-erogeneous many-core processors. The experimental results reveal that the performance of parallel-ism of a single node is much better than that of a master core. Another example is the calculation of solving numeri-cal stress is 80 times faster on the core group than that on the single master core. This experiment could keep a con-stant performance by using 266240 cores, 5.5GFlpos updates of field.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-05-02 Cooperative journals: 《计算机应用研究》
Abstract： Fuzzy clustering is an important research content in the fields of pattern recognition, machine learning and image processing. Fuzzy C-means clustering algorithm is the most commonly used fuzzy clustering algorithm. The algorithm needs to preset the number of clusters in order to cluster the data set. This paper propose a new clustering validity index to validate the clustering results. This index defines the three important features of compactness, resolution and overlap degree from the perspective of partition entropy, membership degree and geometric structure. On this basis, this paper propose a method of determining the optimal clustering number. This paper validate the new clustering validity index and the traditional effectiveness index in six artificial data sets and three real data sets. The experimental results show that the proposed indexes and methods can effectively evaluate the clustering results and are suitable for determining the optimal clustering number of the samples.
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