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Mutation testing is a powerful technique for evaluating the quality of test suite which plays a key role in ensuring software quality. The concept of mutation testing has also been widely used in other software engineering studies, e.g., test generation, fault localization, and program repair. During the process of mutation testing, large number of mutants may be generated and then executed against the test suite to examine whether they can be killed, making the process extremely computational expensive. Several techniques have been proposed to speed up this process, including selective, weakened, and predictive mutation testing. Among those techniques, Predictive Mutation Testing (PMT) tries to build a classification model based on an amount of mutant execution records to predict whether coming new mutants would be killed or alive without mutant execution, and can achieve significant mutation cost reduction. In PMT, each mutant is represented as a list of features related to the mutant itself and the test suite, transforming the mutation testing problem to a binary classification problem. In this paper, we perform an extensive study on the effectiveness and efficiency of the promising PMT technique under the cross-project setting using a total 654 real world projects with more than 4 Million mutants. Our work also complements the original PMT work by considering more features and the powerful deep learning models. The experimental results show an average of over 0.85 prediction accuracy on 654 projects using cross validation, demonstrating the effectiveness of PMT. Meanwhile, a clear speed up is also observed with an average of 28.7× compared to traditional mutation testing with 5 threads. In addition, we analyze the importance of different groups of features in classification model, which provides important implications for the future research.