Performance Modeling and Evaluation of Distributed Deep Learning Frameworks on GPUs

Deep learning frameworks have been widely deployed on GPU servers for deep learning applications in both academia and industry. In training deep neural networks (DNNs), there are many standard processes or algorithms, such as convolution and stochastic gradient descent (SGD), but the running performance of different frameworks might be different even running the same deep model on the same GPU hardware. In this study, we evaluate the running performance of four state-of-the-art distributed deep learning frameworks (i.e., Caffe-MPI, CNTK, MXNet, and TensorFlow) over single-GPU, multi-GPU, and multi-node environments. We first build performance models of standard processes in training DNNs with SGD, and then we benchmark the running performance of these frameworks with three popular convolutional neural networks (i.e., AlexNet, GoogleNet and ResNet-50), after that, we analyze what factors that result in the performance gap among these four frameworks. Through both analytical and experimental analysis, we identify bottlenecks and overheads which could be further optimized. The main contribution is that the proposed performance models and the analysis provide further optimization directions in both algorithmic design and system configuration.Comment: Published at DataCom'201

Multi-Source-Data-Oriented Ensemble Learning Based PM 2.5 Concentration Prediction in Shenyang

Shenyang where is surrounded by smokestack industries and depends on coal heating in winter, is a classical one of cities in China northeastern which has suffered from serious air pollution, especially PM2.5. The existing research on machine learning, based on historical air-monitoring data and meteorological data, does neither forecast accurately nor identify key pollutants for PM2.5. This paper presents a multi-source-data-oriented ensemble learning for predicting PM2.5 concentration. The proposed framework incorporates not only air quality data and weather data, but also industrial emission data, especially those of winter heating enterprises, in Shenyang and nearby cities; the model also takes into account location and emission frequency of pollution sources. All these data are entered into an ensemble learning model based on Extreme Gradient Boosting (XGBoost) in order to predict PM2.5 concentration, which not only improves prediction accuracy effectively, but also provides contribution analysis of different pollutants. Experimental results show that the top two factors affecting PM2.5 concentration are: (1) air pollutant emission quantities and (2) distance from pollution sources to air-monitoring stations. According to the importance of these two factors, we refine feature selection and re-train the ensemble learning model and find that the new model performs better on 72% of evaluation indexes

A STUDY ON THE CULTURAL TOURISM OF THE CHINESE TOURISTS IN JAPAN

The Japanese tourism industry is becoming an important pillar of the Japanese economy. To increase the number of Chinese visitors to Japan, emphasizing the charm of the Japanese culture will be key. Promoting “cultural tourism” as the main reason for Chinese tourists to take vacation trips to Japan will be a major factor in helping Japan reach its international tourism goals by 2020 and beyond. Earlier studies on cultural tourism have offered several conclusions, but few have addressed the subject of cultural tourism as it relates to Chinese visitors to Japan. There appears to be little existing research on Japanese tourism from the perspective of cultural exploration. In most studies on Chinese visitors to Japan, the focus is primarily on economics and policies; very few studies address “cultural tourism.” So in this study, we attempt to expand our understanding of “cultural tourism” among Chinese tourists coming to Japan by identifying factors influencing tourism from a cultural point of view. We conducted a questionnaire survey of Chinese tourists who visited Japan. Before analyzing using multiple regression analysis, we analyzed In order to grasp the visiting factors of Chinese tourists. Based on the results of the survey described here. It was established that increasing numbers of Chinese tourists now visit Japan to pursue “cultural tourism.” The specific elements of this cultural tourism were identified.&nbsp