6/25/2015 1 mrm computational challenges for modeling and simulation michael macedonia chief...

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03/21/22 1 MRM Computational Challenges for Modeling and Simulation Michael Macedonia Chief Technology Officer, US Army Program Executive Office for Simulation, Training and Instrumentation ( PEO STRI)

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Page 1: 6/25/2015 1 MRM Computational Challenges for Modeling and Simulation Michael Macedonia Chief Technology Officer, US Army Program Executive Office for Simulation,

04/18/23 1MRM

Computational Challenges for Modeling and Simulation

Michael MacedoniaChief Technology Officer, US Army Program Executive Office for Simulation, Training and

Instrumentation ( PEO STRI)

Page 2: 6/25/2015 1 MRM Computational Challenges for Modeling and Simulation Michael Macedonia Chief Technology Officer, US Army Program Executive Office for Simulation,

04/18/23 2MRM

Real-time Computational Challenges for Computer Generated Forces (CGF)

Need to provide interactive, real-time terrain reasoning for Computer Generated Forces given: Extremely dense terrain

databases (e.g. Baku, NYC, Baghdad)

Thousand of simulated entities (size of Army Unit of Action)

Simulation of long-range and novel sensors

Must fit on Future Combat System platforms (no Beowulf clusters allowed)

Bottomline: Traditional CPU architecture and Moore’s law are not enough to achieve capability in

this decade.

Page 3: 6/25/2015 1 MRM Computational Challenges for Modeling and Simulation Michael Macedonia Chief Technology Officer, US Army Program Executive Office for Simulation,

04/18/23 3MRM

Real-time Terrain Algorithms for Computer Generated Forces

Best algorithms are O(N2 ) where N = objects/entities in the CGF database (e.g., sensors, platforms, buildings, people)

40% to 80% of CGF CPU time is required for battalion-level scenarios spent in sensing functions:

Collision detection

Line of sight computation

Page 4: 6/25/2015 1 MRM Computational Challenges for Modeling and Simulation Michael Macedonia Chief Technology Officer, US Army Program Executive Office for Simulation,

04/18/23 4MRM

Purpose of LOS Algorithms

• Simulated LOS for Models and Simulations• Position Sensors to Maximum Visibility• Position Targets to Minimize Visibility

Basically, we need to answer the question: Can a sensor at location A see a Target at location B?

Courtesy Danny Champion, TRAC

Page 5: 6/25/2015 1 MRM Computational Challenges for Modeling and Simulation Michael Macedonia Chief Technology Officer, US Army Program Executive Office for Simulation,

04/18/23 5MRM

• Coordinate System (UTM or Lat/Long or Geocentric)? • Curved or Flat Earth or Radar LOS?• How is sensor/target elevations determined? • How are features (vegetation/urban) treated?• How is LOS blocked? (slope or calculated elevation)• Terrain Resolution and its effects on LOS Lower Resolution – faster, less accurate Higher Resolution – slower, more accurate• How should the algorithm be implemented? Which algorithms work best with my hardware?• Precision, Precision, Precision

Algorithm Considerations

Courtesy Danny Champion, TRAC

Page 6: 6/25/2015 1 MRM Computational Challenges for Modeling and Simulation Michael Macedonia Chief Technology Officer, US Army Program Executive Office for Simulation,

04/18/23 6MRM

Triangular Irregular Network

Page 7: 6/25/2015 1 MRM Computational Challenges for Modeling and Simulation Michael Macedonia Chief Technology Officer, US Army Program Executive Office for Simulation,

04/18/23 7MRM

Current Army Visibility Products

Line-of-SightPoint-to-Point

Masked Area Plot orViewshedPoint-to-MultipointSource: Doug Caldwell Topographic Engineering Center

Page 8: 6/25/2015 1 MRM Computational Challenges for Modeling and Simulation Michael Macedonia Chief Technology Officer, US Army Program Executive Office for Simulation,

04/18/23 8MRM

TIREM Propagation Model

Page 9: 6/25/2015 1 MRM Computational Challenges for Modeling and Simulation Michael Macedonia Chief Technology Officer, US Army Program Executive Office for Simulation,

04/18/23 9MRM

A Familiar Curve

Impact of Terrain Resolution on LOS computation

0

5

10

15

20

25

30

35

40

45

0 10 20 30 40 50 60

Resolution in Meters

Rel

ativ

e co

mp

uta

tio

n t

ime

TRAC WSR LOS Study 1995

Page 10: 6/25/2015 1 MRM Computational Challenges for Modeling and Simulation Michael Macedonia Chief Technology Officer, US Army Program Executive Office for Simulation,

04/18/23 10MRM

A Pathological Example

3200

1

1000

1000000

1000000000

0 10000 20000 30000

Entities

LO

S C

alc

ula

tio

ns

Series1

Page 11: 6/25/2015 1 MRM Computational Challenges for Modeling and Simulation Michael Macedonia Chief Technology Officer, US Army Program Executive Office for Simulation,

04/18/23 11MRM

Real World Example: Falling Performance of CCTT CGF

CCTT Baseline

-20406080

100

Build 2(~1994)

Build 7(~1997)

PPSS(~1999)

FBCB2Merge(2000)

OC Merge(2001)

PartialLSE

Merge(2001)

FidelityRelax(2001)

Major Events

Ent

ities

per

pro

cess

or

CCTT Baseline

Page 12: 6/25/2015 1 MRM Computational Challenges for Modeling and Simulation Michael Macedonia Chief Technology Officer, US Army Program Executive Office for Simulation,

04/18/23 12MRM

Why GPU/Streaming ?

1

10

100

1000

Jan-99 Jan-00 Jan-01 Jan-02 Jan-03 Jan-04

Date

Mil

lio

ns

of

Tri

ang

les

per

Sec

on

d

Source: Anselmo Lastra