Using Simulation for Decision Support: Lessons Learned from FireGrid
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Transcript of 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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Intelligent Systems @ ISCRAM 2009
FireGridFireGrid
Super-real-time simulation (HPC)
Command-and-Control
Emergency responders
1000s of sensors
GridGrid
I-X Technologies
Computational models
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Intelligent Systems @ ISCRAM 2009
FireGrid Final Experiment:FireGrid Final Experiment:ArchitectureArchitecture
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Intelligent Systems @ ISCRAM 2009
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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Intelligent Systems @ ISCRAM 2009
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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Intelligent Systems @ ISCRAM 2009
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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Intelligent Systems @ ISCRAM 2009
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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Intelligent Systems @ ISCRAM 2009
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!