Using Simulation for Decision Support: Lessons Learned from FireGrid

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Intelligent Systems @ ISCRAM 2009

Using Simulation for Decision Support:Using Simulation for Decision Support:Lessons Learned from FireGrid Lessons Learned from FireGrid

Gerhard WicklerGerhard Wickler11

George BeckettGeorge Beckett22, , LiangxiuLiangxiu HanHan33, Sung Han Koo, Sung Han Koo44, , Stephen PotterStephen Potter11, Gavin Pringle, Gavin Pringle22, Austin Tate, Austin Tate11

1:AIAI, 2:EPCC, 3:NeSC, 4:SEE,1:AIAI, 2:EPCC, 3:NeSC, 4:SEE,University of Edinburgh, United KingdomUniversity of Edinburgh, United Kingdom

[email protected]@ed.ac.uk

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FireGridFireGrid

Super-real-time simulation (HPC)

Command-and-Control

Emergency responders

1000s of sensors

GridGrid

I-X Technologies

Computational models

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FireGrid Final Experiment:FireGrid Final Experiment:ArchitectureArchitecture

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FireGrid Final Experiment: FireGrid Final Experiment: A Real FireA Real Fire

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FireGrid Final Experiment: FireGrid Final Experiment: User InterfaceUser Interface

3D schematized overview of relevant locations

for each location:– double traffic light

(current/future hazard level) per location

– time-line window on demand

» time slider

» hazard points

» beliefs with justifications

» link for more information

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Lessons Learned: OverviewLessons Learned: Overview

questions: can we re-apply the FireGrid approach for in a different scenario, e.g. FloodGrid, QuakeGrid, PandemicGrid, etc.

lessons learned structured according to data flow:– data acquisition from sensors

– high-performance computing (HPC)

– the Grid

– models and simulation

– intelligent decision support

HPC / Grid

sensor data acquisition simulation

softwareinterpretation

model

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Data Acquisition from Sensors: Data Acquisition from Sensors: OverviewOverview

aim: collect raw data from available sensors

experiment: ca. 140 sensors of different types (mostly thermocouples) used

caveats for lessons learned:– sensors used were simple: single quantity at

specific location; no image data used/analysed

– sensors were pre-installed: exact number and location known; may not be possible in other scenarios (e.g. oil spill)

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Data Acquisition from Sensors: Data Acquisition from Sensors: Lessons Learned (1)Lessons Learned (1)

Is all the data required by the models actually available?– problem: models may demand inputs that cannot be

measured realistically, e.g. location of furniture, heat releaserates over time

– problem: number and location of sensors, e.g. centre of room not practical

Can the sensor data be channelled to and processed by the simulator?– problem: data logger is set up to write to file, e.g. when aim

is post-experimental data analysis

– problem: data is in proprietary format, e.g. to protect commercial interest

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Data Acquisition from Sensors: Data Acquisition from Sensors: Lessons Learned (2)Lessons Learned (2)

At what frequency can sensor values be expected?– not a problem in FireGrid

– problem: sensor readings not synchronized

Is there an ontology that describes the required sensor types?– problem: design database to hold sensor readings

Is there a reliable way of grading the sensor output?– problem: failing or dislocated sensors give incorrect readings

resulting in poor predictions

» sensor grading: decide which sensor readings are to be believed

» developed a constraint-based algorithm that results in a consistent picture (minimize violated constraints)

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High Performance Computing: High Performance Computing: Lessons Learned (1)Lessons Learned (1)

How fast does the simulation run on a “normal” computer?– problem: linear speed-up might not be sufficient; expected

speed-up due to multiple processors; linear speed-up is best case

– problem: current CFD model for fires do not scale well

What is the execution bottleneck for the simulation?– problem: computational bottleneck may be input/output

operations; using multiple CPUs will not provide solution

– problem: inter-process communication may slow down computation

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High Performance Computing: High Performance Computing: Lessons Learned (2)Lessons Learned (2)

Is the model implementation suitable for running on a (parallel)HPC resource?– problem: domain experts often produce serial code; need to

parallelize the simulation software

– approach: ensemble computing (used in FireGrid)

Can the existing implementation be compiled on the HPC resource?– problem: simulator (in Fortran) using non-standard features;

need to port to HPC platform using different compiler and libraries

How quickly do simulators need to start running?– problem: batch system causes delay on HPC

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The Grid:The Grid:BackgroundBackground

aim: use Grid to provide on-demand access to HPC resources

Grid: “… a form of distributed computing whereby a "super and virtual computer" is composed of a cluster of networked, loosely coupled computers, acting in concert to perform very large tasks. […] What distinguishes grid computing from conventional cluster computing systems is that grids tend to be more loosely coupled, heterogeneous, and geographically dispersed.”

issues:– not aiming to fully exploit Grid capabilities

– pre-installation of simulation software on heterogeneous systems very difficult

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The Grid:The Grid:Lessons LearnedLessons Learned

How many (heterogeneous) computing resources should be available through the Grid?– advice: start with small number (one + one spare);

minimizes porting effort

Is there a Grid expert available?– problem: software for accessing the Grid seems still

experimental

Can the simulator be adapted to the resource itis running on?– problem: Grid provides unified interface, but

setting parameters may be necessary to get optimal performance out of an HPC resource

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Models and Simulation:Models and Simulation:Lessons LearnedLessons Learned

Have the models ever been used to generate predictions?– problem: models developed in research context;

usable for predictions? validation?

Can the simulation be “calibrated on the fly”?– problem: model may not be able to assimilate live

sensor data

– FireGrid approach: parameter-sweep

Can the model be used to address “what-if” questions?– problem: model does not take into account

hypothetical actions of emergency responders

Can the model assess the accuracy of its own results?– problem: responders need confidence in model

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Intelligent Decision Support:Intelligent Decision Support:Lessons LearnedLessons Learned

Are the model outputs in terms the emergency responders can understand?– problem: model output is large amounts of numbers; need to

be contextualized and interpreted;

– approaches: AI system vs. expert at emergency

Is there a set of standard operating procedures available?– SOPs: give ways in which task can be accomplished;

preconditions represent kind of information decision makers need to know

Can uncertainty about the model results be conveyedto the user in a useful way?– problem: what do percentages mean?

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ConclusionsConclusions

aim of this paper: provide lessons learned for people trying to build a system that:– uses (large amounts of) sensor data to

– steer a super-real-time simulation that

– generates predictions which are the basis for

– decision support for emergency responders.

but: for a different type of scenario/model, e.g.– an oil spill simulator

– a flood simulator (for a river)

creating such a system requires experts from a variety of technical domains, and pitfalls that are obvious to an expert in one field may be far from it to an expert in a different field, even if they are all experts in computing!

Education

Using Simulation for Decision Support: Lessons Learned from FireGrid