Abstract: The training phase of hotspot detection based on Machine Learning is time-consuming. In this paper, a parallel PCA algorithm based on MPI and a parallel AdaBoost algorithm based on OpenMP are proposed to reduce the training time. Firstly, the QR decomposition is used to optimize SVD, and then the MPI is used to implement the PCA parallel algorithm. Finally, the reduced dimension data is trained using multi-core CPU parallel algorithm. The experimental results show that the PCA parallel algorithm could achieve a 4.7 times speed-up ratio, and the AdaBoost parallel algorithm could achieve a 4.9 times speed-up ratio, which verifies the feasibility of parallelization.