Analysis of Large-Scale Grid-Based Monte Carlo Applications

Abstract

Monte Carlo applications are widely perceived as computationally intensive but naturally parallel. Therefore, they can be effectively executed on the Grid using the dynamic bag-of-work model. In this paper we concentrate on analyzing the characteristics of large-scale Monte Carlo computation for Grid computing. Based on these analyses, we improve the efficiency of the subtask-scheduling scheme by implementing and analyzing the “N-out-of-M” strategy, and we develop a Monte-Carlo-specific lightweight checkpoint technique, which leads to a performance improvement for Monte Carlo Grid computing. Also, we enhance the trustworthiness of Monte Carlo Grid-computing applications by utilizing the statistical nature of Monte Carlo and by cryptographically validating intermediate results utilizing the random number generator already in use in the Monte Carlo application. All these techniques lead to a high-performance Grid-computing infrastructure that is capable of providing trustworthy Monte Carlo computation services.