Monte Carlo Cloud Service Framework

McCloud: Monte Carlo Cloud Service Framework is a powerful framework providing a generic implementation of the Monte Carlo method on Microsoft Windows Azure. It is designed to solve a wide range of scientific and engineering problems efficiently in the cloud.

Table of Contents

• Overview
• Features
• Usage
• Authors
• Citing McCloud
• Institutional Support
• Funding

Overview

McCloud was developed as part of the following master's thesis:

• R. Nasser, McCloud service framework: development services of Monte Carlo simulation in the cloud, M.Sc. Dissertation, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, 2012 (in Portuguese).

The original code and documentation are available on the Codeplex repository: McCloud Codeplex Repository

This GitHub repository includes codes and results from a benchmark Monte Carlo simulation on a structural system, detailed in the following paper:

• A. Cunha Jr, R. Nasser, R. Sampaio, H. Lopes, and K. Breitman, Uncertainty quantification through Monte Carlo method in a cloud computing setting, Computer Physics Communications, 185, pp. 1355-1363, 2014. DOI

Features

• Generic Monte Carlo service implementation
• Cloud-based computation using Microsoft Windows Azure
• Scalable and efficient solution for scientific and engineering simulations
• Extensive documentation and examples

Usage

To get started with McCloud, follow these steps:

1. Clone the repository:

   git clone https://github.com/americocunhajr/McCloud.git

2. Navigate to the code directory:

   cd McCloud/McCloud

3. Execute:

   McCloudProcess('3','0','case1a','case1a_process.csv')
   McCloudMerge('3','case1a','case1a_process.csv','case1a_merge.csv')
   sed -e "s/'//g" case1a_merge.csv > case1a_post.dat
   McCloudPost('3','case1a','case1a_post.dat')

Authors

• Americo Cunha
• Rafael Nasser
• Rubens Sampaio
• Hélio Lopes
• Karin Breitman

Citing McCloud

If you use McCloud in your research, please cite the following publications:

• A. Cunha Jr, R. Nasser, R. Sampaio, H. Lopes, and K. Breitman, Uncertainty quantification through Monte Carlo method in a cloud computing setting, Computer Physics Communications, v. 185, pp. 1355-1363, 2014 http://dx.doi.org/10.1016/j.cpc.2014.01.006
• R. Nasser, McCloud service framework: development services of Monte Carlo simulation in the cloud, M.Sc. Dissertation, Pontifícia Universidade Católica do Rio de Janeiro, Rio de Janeiro, 2012 (in Portuguese)

@article{CunhaJr2014p1355,
  author  = {A. {Cunha~Jr} and R. Nasser and R. Sampaio and H. Lopes and K. Breitman},
  title   = {Uncertainty quantification through {M}onte {C}arlo method in a cloud computing setting},
  journal = {Computer Physics Communications},
  year    = {2014},
  volume  = {185},
  pages   = {1355-1363},
  doi     = {http://dx.doi.org/10.1016/j.cpc.2014.01.006},
}

@mastersthesis{Nasser2012,
  author  = {R. Nasser},
  title   = { {McCloud} service framework: development services of {M}onte {C}arlo simulation in the cloud},
  school  = {Pontifícia Universidade Católica do Rio de Janeiro},
  year    = {2012},
  address = {Rio de Janeiro},
  note    = {(in Portuguese)},
}

Institutional support

Funding