distributed computation and parameter estimation on identification of physiological systems

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 algorithmIdentification of parameters

Measured (measurable)Searched (computed, estimated)

Distributed (GRID) computing approach

CESNETNational research and education network operator in Czech RepublicDepartment 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 RegulationMeasured(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 tomaton@centrum.cz