Abstract. This paper proposes a distributed data clustering technique based on deep neural network. First, each record in the distributed database is taken as an input vector, and its characteristics are extracted and input to the input layer of the depth neural network. The weight of the connection is trained by BP algorithm, and the training of depth neural network output is realized by adjusting the weight. Finally, the data clustering results are judged according to the similarity of the current vector corresponding to the output data. Experimental results based on small-scale distributed systems show that this method has better test set accuracy than traditional k-means clustering method, and is more suitable for large-scale data clustering in the distributed environments.
With high bandwidth and low latency, the InfiniBand protocol has been widely used in distributed databases and high-performance computing in recent years. Compared to traditional PCI bus, 10 Gigabit Ethernet and Myrinet technology, InfiniBand in addition to the delay and bandwidth on the dominant and has a better quality of service. This paper first introduces the basic situation and principle of InfiniBand protocol and its application status in high performance computing field. Based on InfiniBand protocol, this paper proposes a high-performance message passing method based on remote direct memory access, such as delay and peak bandwidth Indicators have very good characteristics. The experimental results show that compared with the traditional technology, the improved method proposed in this paper reduces the delay of more than 20% and the bandwidth is more than doubled, and improves the system performance by reducing the transmission time of the control message.
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