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- Bauer, Pavol
(författare)
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Parallelism in Event-Based Computations with Applications in Biology
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 3. |
- Bauer, Pavol, et al.
(författare)
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The URDME manual Version 1.3
- 2017
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- We have developed URDME, a general software for simulation of stochastic reaction-diffusion processes on unstructured meshes. This allows for a more flexible handling of complicated geometries and curved boundaries compared to simulations on structured, cartesian meshes. The underlying algorithm is the next subvolume method, extended to unstructured meshes by obtaining jump coefficients from a finite element formulation of the corresponding macroscopic equation. This manual describes version 1.3 of the software. URDME 1.3 includes support for Comsol Multiphysics 5.x and PDE Toolbox version 1.5 and above.
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| 7. |
- Lindén, Jonatan, et al.
(författare)
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Exposing inter-process information for efficient parallel discrete event simulation of spatial stochastic systems
- 2017
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Ingår i: Proc. 5th ACM SIGSIM Conference on Principles of Advanced Discrete Simulation. - New York : ACM Press. - 9781450344890 ; , s. 53-64
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Konferensbidrag (refereegranskat)abstract
- We present a new efficient approach to the parallelization of discrete event simulators for multicore computers, which is based on exposing and disseminating essential information between processors. We aim specifically at simulation models with a spatial structure, where time intervals between successive events are highly variable and without lower bounds. In Parallel Discrete Event Simulation (PDES), the model is distributed onto parallel processes. A key challenge in PDES is that each process must continuously decide when to pause its local simulation in order to reduce the risk of expensive rollbacks caused by future "delayed"' incoming events from other processes. A process could make such decisions optimally if it would know the timestamps of future incoming events. Unfortunately, this information is often not available in PDES algorithms. We present an approach to designing efficient PDES algorithms, in which an existing natural parallelization of PDES is restructured in order to expose and disseminate more precise information about future incoming events to each LP. We have implemented our approach in a parallel simulator for spatially extended Markovian processes, intended for simulating, e.g., chemical reactions, biological and epidemiological processes. On 32 cores, our implementation exhibits speedup that significantly outweighs the overhead incurred by the refinement. We also show that our resulting simulator is superior in performance to existing simulators for comparable models, achieving for 32 cores an average speedup of 20 relative to an efficient sequential implementation.
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| 9. |
- Stankevic, Tomas, et al.
(författare)
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Interferometric characterization of rotation stages for X-ray nanotomography
- 2017
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Ingår i: Review of Scientific Instruments. - : American Institute of Physics (AIP). - 0034-6748 .- 1089-7623. ; 88:5
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Tidskriftsartikel (refereegranskat)abstract
- The field of three-dimensional multi-modal X-ray nanoimaging relies not only on high-brilliance X-rays but also on high-precision mechanics and position metrology. Currently available state-of-the-art linear and rotary drives can provide 3D position accuracy within tens to hundreds of nm, which is often insufficient for high resolution imaging with nanofocused X-ray beams. Motion errors are especially troublesome in the case of rotation drives and their correction is more complicated and relies on the metrology grade reference objects. Here we present a method which allows the characterisation and correction of the radial and angular errors of the rotary drives without the need for a highly accurate metrology object. The method is based on multi-probe error separation using fiber-laser interferometry and uses a standard cylindrical sample holder as a reference. The obtained runout and shape measurements are then used to perform the position corrections using additional drives. We demonstrate the results of the characterization for a piezo-driven small rotation stage. The error separation allowed us to measure the axis runout to be approximately +/-1.25 mu m, and with active runout compensation this could be reduced down to +/-42 nm.
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