Distributed computation and parameter estimation on identification of physiological systems
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Distributed computation and parameter estimation on identification of
physiological systemsTomáš Kulhánek 1,2
Jan Šilar 1
Marek Mateják 1
Pavol Privitzer 1
Jiří Kofránek 1 Martin Tribula 1
1 First Faculty of Medicine, Charles University, Prague2 CESNET z.s.p.o.
VPH 2010, Brussels, 30th September -1st October 2010
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Distributed computation and parameter estimation on identification of
physiological systems
Computational models Estimation algorithmIdentification of parameters
Measured (measurable)Searched (computed, estimated)
Distributed (GRID) computing approach
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CESNETNational research and education network operator in Czech RepublicDepartment of network application – application in medicine
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Laboratory of biocybernetics and computer aided teaching
- Institute of Patophysiology, 1st Faculty of Medicine, Charles Univerzity, Prague- Atlas - web based education simulators and presentations- Acausal modeling of physiological systems
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From Guyton model 1972 to HumMod 2010
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Models of physiological systems
Cardiac Output and Its Regulation
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Cardiac Output and Its RegulationMeasured(measurable, guessed)
parameters:
Pthorax
PSystemicArteries
...Searched parameters:
RSystemicVeins,Rsystemic,RPulmonary
Elasticity C, Initial volume V0
Parameters of the models
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Identification of physiological system Make custom model for
specific patient Some parameters cannot
be measured:can be computed – estimated Identification: measured
parameters and estimated parameters match the model.
Optimization methods: Simplex method, Genetic algorithm (CMA-ES), ...
Model evaluation library: .NET, C++, Java
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Computation system
model evaluation from given parameters = 1 iteration~ 1 second
Optimization method for the model Cardiac output and it's regulation (5 parameters)~ 20 000 iterations
~ 20 000 seconds = 5 hours 33 minutes
Optimization method for more complex model (6 parameters)~ 200 000 iterations
– ~200 000 seconds = 2 days 7 hours
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Parallel computation system
Parallelize some iterations -> reduce number of serial steps ~ 1000 iterations
Theoretically: 1000 seconds = 16 minutes vs. 5 hours 33 minutes
Practically: 1000 x (1 parallel iteration + parallelization overhead)
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Parallel computation system
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Computation system - BOINC
Computation service – SOAP web service
BOINC – desktop grid - volunteer computing grid (like seti@home)
DC-API – SZTAKI desktop grid API based upon BOINC
Computation nodes – BOINC clients
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Computation system conclusion 1Parallelization overhead time (1-60 seconds per iteration)
BOINC computation model
– Employed computers in laboratory and virtual computers in cloud build on high speed network (1GBit/s)
– Pull model – client asks for new task in reasonable time – preparation for computing (increases overhead time in the begining)
– Easy to establish and mantain
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future development Employ GRID offered by NGI based on gLite (or Globus)
– Enhance computation web service– Push model – computation node is scheduled by the master
task
CPU (4cores) + GPU (400+ cores) computing– nVidia TESLA
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Thank you for your attention
This work was supported by grant FR CESNET 2009 number 361
Tomáš Kulhánek [email protected]