Literature DB >> 23514937

CloudMC: a cloud computing application for Monte Carlo simulation.

H Miras1, R Jiménez, C Miras, C Gomà.   

Abstract

This work presents CloudMC, a cloud computing application-developed in Windows Azure®, the platform of the Microsoft® cloud-for the parallelization of Monte Carlo simulations in a dynamic virtual cluster. CloudMC is a web application designed to be independent of the Monte Carlo code in which the simulations are based-the simulations just need to be of the form: input files → executable → output files. To study the performance of CloudMC in Windows Azure®, Monte Carlo simulations with penelope were performed on different instance (virtual machine) sizes, and for different number of instances. The instance size was found to have no effect on the simulation runtime. It was also found that the decrease in time with the number of instances followed Amdahl's law, with a slight deviation due to the increase in the fraction of non-parallelizable time with increasing number of instances. A simulation that would have required 30 h of CPU on a single instance was completed in 48.6 min when executed on 64 instances in parallel (speedup of 37 ×). Furthermore, the use of cloud computing for parallel computing offers some advantages over conventional clusters: high accessibility, scalability and pay per usage. Therefore, it is strongly believed that cloud computing will play an important role in making Monte Carlo dose calculation a reality in future clinical practice.

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Year:  2013        PMID: 23514937     DOI: 10.1088/0031-9155/58/8/N125

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  7 in total

1.  Experimental depth dose curves of a 67.5 MeV proton beam for benchmarking and validation of Monte Carlo simulation.

Authors:  Bruce A Faddegon; Jungwook Shin; Carlos M Castenada; José Ramos-Méndez; Inder K Daftari
Journal:  Med Phys       Date:  2015-07       Impact factor: 4.071

2.  Analytical modeling and feasibility study of a multi-GPU cloud-based server (MGCS) framework for non-voxel-based dose calculations.

Authors:  J Neylon; Y Min; P Kupelian; D A Low; A Santhanam
Journal:  Int J Comput Assist Radiol Surg       Date:  2016-08-25       Impact factor: 2.924

Review 3.  Internet-based computer technology on radiotherapy.

Authors:  James C L Chow
Journal:  Rep Pract Oncol Radiother       Date:  2017-09-08

4.  Modified Geometry of 106Ru Asymmetric Eye Plaques to Improve Dosimetric Calculations in Ophthalmic Brachytherapy.

Authors:  Héctor Miras; José Antonio Terrón; Alejandro Bertolet; Antonio Leal
Journal:  J Pers Med       Date:  2022-04-29

Review 5.  A scoping review of cloud computing in healthcare.

Authors:  Lena Griebel; Hans-Ulrich Prokosch; Felix Köpcke; Dennis Toddenroth; Jan Christoph; Ines Leb; Igor Engel; Martin Sedlmayr
Journal:  BMC Med Inform Decis Mak       Date:  2015-03-19       Impact factor: 2.796

6.  Monte Carlo verification of radiotherapy treatments with CloudMC.

Authors:  Hector Miras; Rubén Jiménez; Álvaro Perales; José Antonio Terrón; Alejandro Bertolet; Antonio Ortiz; José Macías
Journal:  Radiat Oncol       Date:  2018-06-27       Impact factor: 3.481

7.  Scalable and accessible personalized photodynamic therapy optimization with FullMonte and PDT-SPACE.

Authors:  Shuran Wang; Xiao Ying Dai; Shengxiang Ji; Tina Saeidi; Fynn Schwiegelshohn; Abdul-Amir Yassine; Lothar Lilge; Vaughn Betz
Journal:  J Biomed Opt       Date:  2022-04       Impact factor: 3.758

  7 in total

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