2014/01/23 by Americo Cunha, Américo Cunha, A. Cunha Jr +8 · 1 citation
Computer Science · Decision Sciences · Mathematics · #Algorithm #Cloud computing #Computation #Computational science #Computer science #Context (archaeology) #Convergence (economics) #Database #Distributed computing #Mathematical optimization #Mathematics #Monte Carlo method #Parallelizable manifold #Probabilistic and Robust Engineering Design #Realization (probability) #Scalability #acm:62D05 #cs.MS #math.PR #msc:62D05 #stat.AP #stat.CO
paper · pdf · doi:10.1016/j.cpc.2014.01.006
published as Computer Physics Communications, vol. 185, pp. 1355-1363, 2014
openalex publication_date 2014/01/23 · crossref created 2014/01/23 · crossref issued 2014/05/01 · crossref published 2014/05/01 · crossref published-print 2014/05/01 · openalex created_date 2016/06/24 · arxiv created 2021/05/20 · arxiv updated 2021/05/21 · crossref deposited 2022/03/24 · openalex updated_date 2026/08/05 · crossref indexed 2026/08/06
The Monte Carlo (MC) method is the most common technique used for uncertainty quantification, due to its simplicity and good statistical results. However, its computational cost is extremely high, and, in many cases, prohibitive. Fortunately, the MC algorithm is easily parallelizable, which allows its use in simulations where the computation of a single realization is very costly. This work presents a methodology for the parallelization of the MC method, in the context of cloud computing. This strategy is based on the MapReduce paradigm, and allows an efficient distribution of tasks in the cloud. This methodology is illustrated on a problem of structural dynamics that is subject to uncertainties. The results show that the technique is capable of producing good results concerning statistical moments of low order. It is shown that even a simple problem may require many realizations for convergence of histograms, which makes the cloud computing strategy very attractive (due to its high scalability capacity and low-cost). Additionally, the results regarding the time of processing and storage space usage allow one to qualify this new methodology as a solution for simulations that require a number of MC realizations beyond the standard.