Accelerating Airline Delay Prediction-Based P-CUDA Computing Environment

Machine learning techniques have enabled machines to achieve human-like thinking and learning abilities. The sudden surge in the rate of data production has enabled enormous research opportunities in the field of machine learning to introduce new and improved techniques that deal with the challenging tasks of higher level. However, this rise in size of data quality has introduced a new challenge in this field, regarding the processing of such huge chunks of the dataset in limited available time. To deal such problems, in this paper, we present a parallel method of solving and interpreting the ML problems to achieve the required efficiency in the available time period. To solve this problem, we use CUDA, a GPU-based approach, to modify and accelerate the training and testing phases of machine learning problems. We also emphasize to demonstrate the efficiency achieved via predicting airline delay through both the sequential as well as CUDA-based parallel approach. Experimental results show that the proposed parallel CUDA approach outperforms in terms of its execution time.