Heterogeneous computing for epidemiological model fitting and simulation
Abstract Background Over the last years, substantial effort has been put into enhancing our arsenal in fighting epidemics from both technological and theoretical perspectives with scientists from different fields teaming up for rapid assessment of potentially urgent situations. This paper focusses o...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2018-03-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-018-2108-3 |
| _version_ | 1818478103941349376 |
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| author | Thomas Kovac Tom Haber Frank Van Reeth Niel Hens |
| author_facet | Thomas Kovac Tom Haber Frank Van Reeth Niel Hens |
| author_sort | Thomas Kovac |
| collection | DOAJ |
| description | Abstract Background Over the last years, substantial effort has been put into enhancing our arsenal in fighting epidemics from both technological and theoretical perspectives with scientists from different fields teaming up for rapid assessment of potentially urgent situations. This paper focusses on the computational aspects of infectious disease models and applies commonly available graphics processing units (GPUs) for the simulation of these models. However, fully utilizing the resources of both CPUs and GPUs requires a carefully balanced heterogeneous approach. Results The contribution of this paper is twofold. First, an efficient GPU implementation for evaluating a small-scale ODE model; here, the basic S(usceptible)-I(nfected)-R(ecovered) model, is discussed. Second, an asynchronous particle swarm optimization (PSO) implementation is proposed where batches of particles are sent asynchronously from the host (CPU) to the GPU for evaluation. The ultimate goal is to infer model parameters that enable the model to correctly describe observed data. The particles of the PSO algorithm are candidate parameters of the model; finding the right one is a matter of optimizing the likelihood function which quantifies how well the model describes the observed data. By employing a heterogeneous approach, in which both CPU and GPU are kept busy with useful work, speedups of 10 to 12 times can be achieved on a moderate machine with a high-end consumer GPU as compared to a high-end system with 32 CPU cores. Conclusions Utilizing GPUs for parameter inference can bring considerable increases in performance using average host systems with high-end consumer GPUs. Future studies should evaluate the benefit of using newer CPU and GPU architectures as well as applying this method to more complex epidemiological scenarios. |
| first_indexed | 2024-12-10T09:44:07Z |
| format | Article |
| id | doaj.art-89c6167502de4523adf872113857b558 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-10T09:44:07Z |
| publishDate | 2018-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-89c6167502de4523adf872113857b5582022-12-22T01:53:54ZengBMCBMC Bioinformatics1471-21052018-03-0119111110.1186/s12859-018-2108-3Heterogeneous computing for epidemiological model fitting and simulationThomas Kovac0Tom Haber1Frank Van Reeth2Niel Hens3Center for Statistics, I-BioStat, Hasselt UniversityExpertise Centre for Digital Media, Hasselt UniversityExpertise Centre for Digital Media, Hasselt UniversityCenter for Statistics, I-BioStat, Hasselt UniversityAbstract Background Over the last years, substantial effort has been put into enhancing our arsenal in fighting epidemics from both technological and theoretical perspectives with scientists from different fields teaming up for rapid assessment of potentially urgent situations. This paper focusses on the computational aspects of infectious disease models and applies commonly available graphics processing units (GPUs) for the simulation of these models. However, fully utilizing the resources of both CPUs and GPUs requires a carefully balanced heterogeneous approach. Results The contribution of this paper is twofold. First, an efficient GPU implementation for evaluating a small-scale ODE model; here, the basic S(usceptible)-I(nfected)-R(ecovered) model, is discussed. Second, an asynchronous particle swarm optimization (PSO) implementation is proposed where batches of particles are sent asynchronously from the host (CPU) to the GPU for evaluation. The ultimate goal is to infer model parameters that enable the model to correctly describe observed data. The particles of the PSO algorithm are candidate parameters of the model; finding the right one is a matter of optimizing the likelihood function which quantifies how well the model describes the observed data. By employing a heterogeneous approach, in which both CPU and GPU are kept busy with useful work, speedups of 10 to 12 times can be achieved on a moderate machine with a high-end consumer GPU as compared to a high-end system with 32 CPU cores. Conclusions Utilizing GPUs for parameter inference can bring considerable increases in performance using average host systems with high-end consumer GPUs. Future studies should evaluate the benefit of using newer CPU and GPU architectures as well as applying this method to more complex epidemiological scenarios.http://link.springer.com/article/10.1186/s12859-018-2108-3ODEPDEInfectious diseasesEpidemiologySIR modelGPU |
| spellingShingle | Thomas Kovac Tom Haber Frank Van Reeth Niel Hens Heterogeneous computing for epidemiological model fitting and simulation BMC Bioinformatics ODE PDE Infectious diseases Epidemiology SIR model GPU |
| title | Heterogeneous computing for epidemiological model fitting and simulation |
| title_full | Heterogeneous computing for epidemiological model fitting and simulation |
| title_fullStr | Heterogeneous computing for epidemiological model fitting and simulation |
| title_full_unstemmed | Heterogeneous computing for epidemiological model fitting and simulation |
| title_short | Heterogeneous computing for epidemiological model fitting and simulation |
| title_sort | heterogeneous computing for epidemiological model fitting and simulation |
| topic | ODE PDE Infectious diseases Epidemiology SIR model GPU |
| url | http://link.springer.com/article/10.1186/s12859-018-2108-3 |
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