Distributedmega-scale Agent Management

in MASS:

diffusion, guarded migration,merger and termination

Cherie WasousCSS_700 Thesis – Winter 2014 (Feb. 6)

from: Romanus, css497 summer2013, “Developing and Extending the MASS Library (Java) Places.exchangeBoundary( )”

Overall MASS Framework

Places

• Maintain & Manages the Place locations

• Manages exchange

between the Place

locations

Place

• Maintains Place location data

• Provides a user software interface

Agents

• Maintains & Manages the Agent units

• Manages the exchange and migration of Agent units

Agent

• Maintains the Agent data

• Provides a user software interface

callAll( ) callSome( )exchangeAll( )exchangeBoundary( )

callMethod( )

{User created functions}callAll( ) manageAll( )migrate( ) spawn( ) kill( )

callMethod( )

{User created functions}

MASS execution model

from: Chuang, MS Thesis, “Design and Qualitative/Quantative Analysis of Multi-Agent Spatial Simulation Library”

Current state of “Climate” app for Evaluation

• Places: timeSlots X numberOfDays

• Each Place Element: a 123(XRANGE) x 162(YRANGE) grid of the Pacific NW

• Place Compute:(bogus) moisture_flux, direction for each grid location, find max

Places climateData = new Places( 1, "ClimateData", (Object)null, nTimeSlots, nDays );

In User Application: climateData.callAll( ClimateData.compute_, null );

In ClimateData.compute_: for ( int x = 0; x < XRANGE; x++ ) { for ( int y = 0; y < YRANGE; y++ ) { moisture_flux[x][y] = ( time + 1) * ( day + 1 ) * ( (double) x + ( (double) y / 1000.0 ) ); } } for ( int x = 0; x < XRANGE; x++ ) { for ( int y = 0; y < YRANGE; y++ ) { direction[x][y] = (double) x + ( (double) y / 1000.0 ); } } for ( int x = 0; x < XRANGE; x++ ) { for ( int y = 0; y < YRANGE; y++ ) { if ( maxFlux < moisture_flux[x][y] ) { maxFlux = moisture_flux[x][y]; maxX = x; maxY = y; } } }

a made-up/bogus “Climate” app for evaluations

• Place elements of climate related data for the Pacific NW

• Agents who search the place elements looking for a maximum value

• As agents complete their job, they put the max they found into Vector at first place element for this node

• User App then collects these values, and then looks for the overall max value

Current state of “Climate” app for Evaluation• Agents: create one agent at each timeSlot for Day Zero

• Agent: if finds new max, copy it; migrate to the next Day (same timeSlot); when do last Day, deposit max found into Vector maxSeen & prepare to kill Self

Agents agents = new Agents( 2, "MaxFinder", maxFinderArgs, climateData, nAgents );

public int map( int maxAgents, int[ ] size, int[ ] coordinates ) { int currX = coordinates[0], currY = coordinates[1]; if ( myRunMode == 1 ) { // place an agent at each timeslot for the first day if ( currY == 0 ) return 1; else return 0; } }

In User Application:Agents agents = new Agents( 2, "MaxFinder", maxFinderArgs, climateData, nAgents );while ( agents.totalAgents() > 0 ) { agents.callAll( MaxFinder.find_, (Object)null ); agents.manageAll(); }

In MaxFinder.find_:if ( this.maxFlux < ((ClimateData)place).maxFlux ) { this.maxFlux = ((ClimateData)place).maxFlux; this.maxX = ((ClimateData)place).maxX; this.maxY = ((ClimateData)place).maxY; this.maxDir=((ClimateData)place).direction[maxX][maxY]; this.maxTime=((ClimateData)place).time; this.maxDay=((ClimateData)place).day; }if (currY == nDays - 1 ) { // dump this agent's value into Vector “maxSeen” for this process MaxClimateData temp = new MaxClimateData( this.maxFlux, this.maxDir, this.maxTime, this.maxDay, this.maxX, this.maxY ); ((ClimateData)place).maxSeen.add( temp ); kill( ); // set-up to kill this agent on next manageAll } else { migrate( currX, currY+1); }

Current state of “Climate” app for Evaluation• Place Collect: searches Vector maxSeen and returns max value for this node

In User Application: Vector<MaxClimateData> results = new Vector<MaxClimateData>(); Object[ ] tempArgs = new Object[ nDays * nTimeSlots ]; Object[ ] valuesReturned = climateData.callAll( ClimateData.collect_, (Object[ ])tempArgs );

In ClimateData.collect_: if ( this is first place element on this node ) { MaxClimateData thisNodeMax = new MaxClimateData(); Iterator<MaxClimateData> iter = maxSeen.iterator(); while ( iter.hasNext( ) ) { MaxClimateData temp = iter.next(); if ( thisNodeMax.mcdFlux < temp.mcdFlux ) { thisNodeMax = temp; } } return (Object)thisNodeMax; } else { return null; }

Current state of “Climate” app for Evaluation• Finally, back in User Application: does final search of each node’s returned value

to find the overallMax

for ( int i = 0; i < valuesReturned.length; i++ ) { if ( valuesReturned[ i ] != null ) { MaxClimateData gotValue = (MaxClimateData)valuesReturned[ i ]; results.add( gotValue ); } }

Iterator<MaxClimateData> iter = results.iterator();MaxClimateData overallMax = new MaxClimateData(); overallMax.mcdFlux = 0.0;MaxClimateData tempCD = new MaxClimateData();while ( iter.hasNext( ) ) { tempCD = iter.next(); if ( overallMax.mcdFlux < tempCD.mcdFlux ) { overallMax = tempCD; } }

print overallMax

Summary of “Climate” app for Evaluation

• Places: timeSlots X numberOfDays• Each Place Element: a 123x162 grid of the Pacific NW

• Place Compute:(bogus) moisture_flux, direction for each grid location, find max

• Agents: create one agent at each timeSlot for Day Zero • Agent: if finds new max, copy it; migrate to the next Day (same timeSlot)• Agent: when do last Day, deposit max found into Vector at place 0,0 & killSelf

• Place Collect: first place element on each node searches thru Vector and returns the max value for that node

• Finally, in user app.: final search of returned values to find the overallMax

Snapshot of PerformanceThursday, February 06, 2014

All runs: 1 process, 12 agents, 12 timeSlots x 364 days, runmode=1 (an agent at day zero for each timeslot & migrate thru the days, one at a time)

runmode=1 (an agent at day zero for each timeslot & migrate thru the days, one at a time, at last day put max into place0,0 maxSeen Vector)

All times: in milliseconds

UW-B 302 corner PC (UW1-302-83QZLP1.uwb.edu) Cherie's Laptop UW-B 302 Hercules (hercules.uwb.edu)

# thmass init

Places create

Places compute

Agents search

Places collect

user main sort

mass finish # th

mass init

Places create

Places compute

Agents search

Places collect

user main sort

mass finish # th

mass init

Places create

Places compute

Agents search

Places collect

user main sort

mass finish

1 17 961 1454 2177 3 0 0 1 16 841 1110 2158 3 0 0 1 22 6065 1906 3007

16 953 1476 2208 3 0 0 15 821 1103 1814 3 0 1 67 6715 1920 2656

2 17 970 787 1438 2 1 0 2 16 811 568 1150 2 0 0 2 19 5731 971 2655 8 1 1

16 960 791 1519 2 0 1 15 814 573 1143 22 5576 950 2592

4 17 956 406 1038 2 0 0 4 13 835 305 913 1 1 0 4 22 7139 507 2147 5 1 1

17 960 486 1225 2 0 0 15 895 300 932 2 0 0 23 5700 510 2655

6 6 16 850 289 900 6 70 6640 366 2579 16 2 3

812 262 955

8 8 16 807 234 861 2 0 0 8 24 6143 343 2136 6 1 1

16 840 258 1076 1 0 1 25 5978 285 2187

12 19 956 648 1035 2 0 0 12 16 838 486 883 2 0 0 12 26 6020 293 3381

18 968 645 1047 1 0 1 24 6095 316 2193

Xeon QuadCore W3520,2.7GHz, 4cores8th, 45nm, i7-3615QM,2.3GHz,4core8th,22nm2Q'13 Xeon E5520, 2.27GHz, (4cores8th)*2, 45nmQ1'09,

L1=256kB, L2=4x256kB, L3=6-MB(1.5M/core), 6-GBram L1=,L2=IntelSmartCache=6MB, 8-GBram 8MiSmartCache, (?cpuinfo:8siblings,16proc.), 6-GBram

(Package 2 CPUs) (the 2nd th./core shares ALU/FP)

Next Steps

• Add to current “Climate” app:• runMode=0: only use Places (so each place sends its max to main, then main sorts)• runMode=2: spawn more Agents, once startup

• Scripts to gather & present performance data for various # proc/th/runModes

• Begin Agent Enhancements in MASS code, re-eval. with “Climate” app

“diffusion, guarded migration, merger, and termination”

Modify Agents• Overload Constructor:

Agents( byte inject, byte guardedMigration, int handle, String className, Object argument, Places places )

Instantiates a set of agents from the “className” class according to the

technique indicated by “inject”:

0 = chaotic (nThreads*nProc agent elements),

1 = controlled (nThreads*nProc agent elements),

2 = one-way scan ( size[0] in 1D, size[1] in 2D, size[2] in 3D

These agent elements migrate per “guardedMigration” algorithm:

0 = naive (collisions are not a concern, unlimited agents per place)

1 = greedy (only 1 agent per place maximum)

2 = best-effort at greedy ( try not to have more than 1 agent per

place, but on occasion there will be more than 1 )

“diffusion, guarded migration, merger, and termination”

Modify Agent abstract class• Overload version of migrate:

public boolean migrate( byte diffusion )

Initiates an agent migration upon a next call to Agents.manageAll( ).

diffusion indicates technique of migration to use:

0 = chaotic, 1 = controlled, 2 = one-way scan

MASS code base known problems:Congestion (seen in SugarScape app) – when use multi-threaded multi-process MASS.

Workaround for now: when using multi-process, use single-thread (1/24/14 email)

Hang – when 2 processes, single-thread, and agents try to cross over to other node.

Testbed provided dslab on hercules: /cherie/ClimateNew4/testrun/runClimate.sh (2/4/14)

Agents Object[] callSome() does not have implementation codeCurrently just return null.

This function was desired to be used by agent code of bionetworks & climate in Fall 2013.

minor: change “ERROR” to “WARNING” re: missing DLB config file (so users do not “panic”) (this problem still seen in code base of .new4)

When totally delete DLB.properties file you can see this message.

Questions ???