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Multiscale modelling via split-step methods in neural firing
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för beräkningsvetenskap. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Tillämpad beräkningsvetenskap.
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för beräkningsvetenskap. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Tillämpad beräkningsvetenskap.ORCID-id: 0000-0002-3614-1732
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för neurovetenskap, Genetisk utvecklingsbiologi.
2018 (engelsk)Inngår i: Mathematical and Computer Modelling of Dynamical Systems, ISSN 1387-3954, E-ISSN 1744-5051, Vol. 24, s. 426-445Artikkel i tidsskrift (Fagfellevurdert) Published
sted, utgiver, år, opplag, sider
2018. Vol. 24, s. 426-445
HSV kategori
Identifikatorer
URN: urn:nbn:se:uu:diva-332008DOI: 10.1080/13873954.2018.1488740ISI: 000440605300005OAI: oai:DiVA.org:uu-332008DiVA, id: diva2:1151107
Prosjekter
UPMARCeSSENCETilgjengelig fra: 2018-08-01 Laget: 2017-10-22 Sist oppdatert: 2018-11-19bibliografisk kontrollert
Inngår i avhandling
1. Parallelism in Event-Based Computations with Applications in Biology
Åpne denne publikasjonen i ny fane eller vindu >>Parallelism in Event-Based Computations with Applications in Biology
2017 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Event-based models find frequent usage in fields such as computational physics and biology as they may contain both continuous and discrete state variables and may incorporate both deterministic and stochastic state transitions. If the state transitions are stochastic, computer-generated random numbers are used to obtain the model solution. This type of event-based computations is also known as Monte-Carlo simulation.

In this thesis, I study different approaches to execute event-based computations on parallel computers. This ultimately allows users to retrieve their simulation results in a fraction of the original computation time. As system sizes grow continuously or models have to be simulated at longer time scales, this is a necessary approach for current computational tasks.

More specifically, I propose several ways to asynchronously simulate such models on parallel shared-memory computers, for example using parallel discrete-event simulation or task-based computing. The particular event-based models studied herein find applications in systems biology, computational epidemiology and computational neuroscience.

In the presented studies, the proposed methods allow for high efficiency of the parallel simulation, typically scaling well with the number of used computer cores. As the scaling typically depends on individual model properties, the studies also investigate which quantities have the greatest impact on the simulation performance.

Finally, the presented studies include other insights into event-based computations, such as methods how to estimate parameter sensitivity in stochastic models and how to simulate models that include both deterministic and stochastic state transitions.

sted, utgiver, år, opplag, sider
Uppsala: Acta Universitatis Upsaliensis, 2017. s. 48
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1586
Emneord
Event-based computations, Parallel algorithms, Discrete-event simulation, Monte-Carlo methods, Systems biology.
HSV kategori
Forskningsprogram
Beräkningsvetenskap
Identifikatorer
urn:nbn:se:uu:diva-332009 (URN)978-91-513-0125-9 (ISBN)
Disputas
2017-12-11, 2347, ITC, Lägerhyddsvägen 2, Uppsala, 10:15 (engelsk)
Opponent
Veileder
Prosjekter
UPMARC
Tilgjengelig fra: 2017-11-30 Laget: 2017-10-22 Sist oppdatert: 2018-03-07

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