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A parallel microsimulation package for modelling cancer screening policies
KTH, School of Computer Science and Communication (CSC), Centres, Centre for High Performance Computing, PDC. KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Computer Science and Communication (CSC), High Performance Computing and Visualization (HPCViz). KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).ORCID iD: 0000-0002-9901-9857
2017 (English)In: Proceedings of the 2016 IEEE 12th International Conference on e-Science, e-Science 2016, IEEE, 2017, 323-330 p.Conference paper, Published paper (Refereed)
Abstract [en]

Microsimulation with stochastic life histories is an important tool in the development of public policies. In this article, we use microsimulation to evaluate policies for prostate cancer testing. We implemented the microsimulations as an R package, with pre- and post-processing in R and with the simulations written in C++. Calibrating a microsimulation model with a large population can be computationally expensive. To address this issue, we investigated four forms of parallelism: (i) shared memory parallelism using R; (ii) shared memory parallelism using OpenMP at the C++ level; (iii) distributed memory parallelism using R; and (iv) a hybrid shared/distributed memory parallelism using OpenMP at the C++ level and MPI at the R level. The close coupling between R and C++ offered advantages for ease of software dissemination and the use of high-level R parallelisation methods. However, this combination brought challenges when trying to use shared memory parallelism at the C++ level: the performance gained by hybrid OpenMP/MPI came at the cost of significant re-factoring of the existing code. As a case study, we implemented a prostate cancer model in the microsimulation package. We used this model to investigate whether prostate cancer testing with specific re-testing protocols would reduce harms and maintain any mortality benefit from prostate-specific antigen testing. We showed that four-yearly testing would have a comparable effectiveness and a marked decrease in costs compared with two-yearly testing and current testing. In summary, we developed a microsimulation package in R and assessed the cost-effectiveness of prostate cancer testing. We were able to scale up the microsimulations using a combination of R and C++, however care was required when using shared memory parallelism at the C++ level.

Place, publisher, year, edition, pages
IEEE, 2017. 323-330 p.
Keyword [en]
Antigens, Application programming interfaces (API), C++ (programming language), Computer software, Cost effectiveness, Costs, Memory architecture, Stochastic systems, Urology, Cancer screening, Distributed-memory parallelisms, Microsimulation modeling, Prostate cancers, Prostate specific antigen, Shared memory parallelism, Software disseminations, Testing protocols, Diseases
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-208039DOI: 10.1109/eScience.2016.7870915ISI: 000405564400036Scopus ID: 2-s2.0-85016826916ISBN: 9781509042722 (print)OAI: oai:DiVA.org:kth-208039DiVA: diva2:1104402
Conference
12th IEEE International Conference on e-Science, e-Science 2016, 23 October 2016 through 27 October 2016
Note

QC 20170601

Available from: 2017-06-01 Created: 2017-06-01 Last updated: 2017-08-11Bibliographically approved

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Publisher's full textScopushttp://escience-2016.idies.jhu.edu/

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Laure, Erwin

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Centre for High Performance Computing, PDCSeRC - Swedish e-Science Research CentreHigh Performance Computing and Visualization (HPCViz)Computational Science and Technology (CST)
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