Distributed Parallel Deep Learning for Fast Extraction of Similar Weather Map

For real time weather forecasting, it is necessary to search most similar weather map very fast among a large amount of data accumulated so far. Recently, deep learning is used for more accurate weather forecasting. However, it takes a huge amount of time for training deep learning model in order to process a number of previous weather maps. In this paper, we shall present fast distributed parallel algorithms for training deep neural network model based on CNN on parallel and distributed environment with GPUs for various number of models in order to extract most similar weather map from CNN. For each case of single and multi nodes, we compare the performance of our algorithm increasing the number of GPUs, and for the case of multi nodes, compare the performance for two ways of communications: synchronous and asynchronous. Also, we shall show the performance of our algorithm for the various number of models on single and multi nodes.