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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
Distributed computation and parameter estimation on identification of
physiological systems
Computational models
Estimation algorithm
Identification of parameters
Measured (measurable)
Searched (computed, estimated)
Distributed (GRID) computing approach
CESNETNational research and education network operator in Czech Republic
Department of network application – application in medicine
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
From Guyton model 1972 to HumMod 2010
Models of physiological systems
Cardiac Output and Its Regulation
Cardiac Output and Its Regulation
Measured(measurable, guessed) parameters:
Pthorax
PSystemicArteries
...
Searched parameters:
RSystemicVeins
,Rsystemic
,RPulmonary
Elasticity C, Initial volume V0
Parameters of the models
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
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
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)
Parallel computation system
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
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
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
Thank you for your attention
This work was supported by grant FR CESNET 2009 number 361
Tomáš Kulhánek [email protected]
