Rensselaer Polytechnic Institute

1
An Asynchronous Hybrid Genetic-Simplex Search
for Modeling the Milky Way Galaxy Using
Volunteer Computing
Travis Desell, Boleslaw Szymanski, Carlos Varela

• Rensselaer Polytechnic Institute
• Department of Computer Science
• GECCO 2008
• Tuesday, July 15, 2008

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Overview

• Introduction
• Motivation
• Driving Scientific Application
• Large Scale Computing
• Research Questions and Challenges
• Asynchronous Genetic Search
• Methodology
• Recombination

• ?Generic Optimization Framework
• Approach
• Vision
• Architecture
• Results
• Computing Environments
• Convergence Rates
• Recombination Analysis
• Conclusions Future Work
• Questions?

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Motivation

• Scientists need easily accessible distributed
  optimization tools
• Distribution is essential for scientific
  computing
• Scientific models are becoming increasingly
  complex
• Rates of data acquisition are far exceeding
  increases in computing power
• Traditional optimization strategies not well
  suited to large scale computing
• Lack scalability and fault tolerance

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Large Scale Computing

• Supercomputing
• 10k homogeneous processors
• Very fast communication
• Highly reliable
• Internet Computing
• 10k 100k heterogeneous processors
• Heterogeneous (up to global-scale) communication
• Highly volatile

• Computational Grids
• 100s of processors per homogeneous cluster
• Fast homogeneous communication within clusters
• Heterogeneous (up to global-scale) latency
  between clusters
• Moderately reliable

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Astro-Informatics
What is the structure and origin of the Milky Way
galaxy?

• Being inside the Milky Way provides 3D data
• SLOAN digital sky survey has collected over 10 TB
  data.
• Can determine it's structure not possible for
  other galaxies.
• Very challenging evaluating a single model of
  the Milky Way with a single set of parameters can
  take hours or days on a typical high-end
  computer.
• Models determine where different star streams are
  in the Milky Way, which helps us understand
  better its structure and how it was formed.

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• Asynchronous
• Genetic Search

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Issues With Traditional Genetic Search

• Traditional genetic search is dependent and
  iterative
• Current generation is used to generate the next
  generation
• Dependencies and iterations limit scalability and
  impact performance
• With volatile hosts, what if an individual in the
  next generation is lost?
• Redundancy is expensive
• Scalability limited by population size

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Asynchronous Search Strategy

• Use an asynchronous search methodology
• No explicit dependencies
• No iterations
• Continuously updated population
• N individuals are generated randomly for the
  initial population
• Fulfil work requests by applying recombination
  operators to the population
• Update population with reported results

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Asynchronous Search Strategy (2)?
Workers
Report results and update population
Send work
Request work
Work Queue
Population
Request work when queue is low
Parameter Set (1)?
Fitness (1)?
Unevaluated Parameter Set (1)?
Parameter Set (2)?
Fitness (2)?
Unevaluated Parameter Set (2)?
. . . . .
. . . . .
. . . . .
Generate members from population
Parameter Set (n)?
Fitness (n)?
Unevaluated Parameter Set (m)?
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Asynchronous Genetic Search Operators (1)?

• Average
• Traditional operator for continuous problems
• Generated parameters are the average of two
  randomly selected parents
• Mutation
• Takes a parent and generates a mutation by
  randomly selecting a parameter and mutating it

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Asynchronous Genetic Search Operators (2)?

• Double Shot - two parents generate three children
• The average of the parents
• A point outside the less fit parent, the same
  distance from that parent as the average
• A point outside the more fit parent, the same
  distance from that parent as the average

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Asynchronous Genetic Search Operators (3)?

• Probabilistic Simplex
• N parents generate one or more children
• Points randomly along the line created by the
  worst parent, and the centroid (average) of the
  remaining parents

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• Generic Optimization Framework

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Approach

• Separation of Concerns
• Distributed Computing
• Optimization
• Scientific Modeling

• Plug-and-Play
• Simple generic interfaces

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Vision
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Architecture

• Asynchronous evaluations
• Faults can be ignored
• No processor dependencies
• Results may not be reported or reported late
• Grids Internet

• Single parallel evaluation
• Uses most evolved population
• Can use traditional methods
• Faults require recalculation
• Grids require load balancing
• Supercomputers Grids

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Synchronous Architecture?
Scientific Models
Search Routines
Data Initialisation Integral Function Integral
Composition Likelihood Function Likelihood
Composition
Gradient Descent Genetic Search Simplex
Initial Parameters
Optimised Parameters
Evaluation Request
Results
Distribute Parameters
Combine Results
Evaluator
Evaluator
Evaluator
Evaluator
Evaluator
Evaluator Creation

SALSA/Java (RPI Grid)?
MPI/C (BlueGene)?
Distributed Evaluation Framework
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Asynchronous Architecture?
Scientific Models
Search Routines
Data Initialisation Integral Function Integral
Composition Likelihood Function Likelihood
Composition
Evolutionary Methods Genetic Search Particle
Swarm Optimisation
Initial Parameters
Optimised Parameters
Work Request
Results
Work Request
Results
Work
Work
Evaluator (1)?
Evaluator (N)?

Evaluator Creation
BOINC (Internet)?
SALSA/Java (RPI Grid)?
Distributed Evaluation Framework
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• Experimental Results

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Computing Environments - BlueGene

• RPI's CCNI BlueGene
• BlueGene used as a single worker
• Very fast communication topology enables parallel
  function evaluation with the synchronous
  architecture
• Used a 512 node partition of 1024 processors
• One individual generated, evaluated and inserted
  at a time
• Mimics steady state genetic search
• Most evolved population always used

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Computing Environments - BOINC

• MilkyWay_at_Home http//milkyway.cs.rpi.edu/
• Multiple Asynchronous Workers
• Approximately 2,000 3,000 volunteered computers
  used
• Asynchronous architecture used
• Asynchronous Evaluation
• Each computer could request up to 20 pending
  individuals at any time
• Work queue filled with individuals generated form
  current population
• Population updated when results reported
• Individuals may not be reported

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Asynchronous vs Iterative Genetic Search
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Asynchronous GS-Simplex on BlueGene
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Asynchronous GS-Simplex on BOINC?
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Simplex Operator Analysis

• Even with a long time to report, results still
  would improve the population

• Generation near reflection has highest insert
  rate
• Generation near centroid provide most population
  improvement for fast report times
• Generation near reflection provide most
  population improvement for long report times

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• Discussion Future Work

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Conclusions

• Asynchronous search is effective on large scale
  computing environments
• Fault tolerant without expensive redundancy
• Asynchronous evaluation on heterogeneous
  environment increases diversity
• BOINC converges almost as fast as the BlueGene,
  while offering more availability and
  computational power
• Even computers with slow result report rates are
  useful
• Simplex-Genetic Hybrid provides significant
  improvement in convergence

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Future Work

• Optimization
• Use report times to determine how to generate
  individuals
• More search methods (PSO, DE)?
• Simulate asynchrony for benchmarks
• Distributed Computing
• Parallel asynchronous workers
• Handle Malicious Volunteers
• Collaboration

http//www.nasa.gov
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• Questions?

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• Thanks!
• http//milkyway.cs.rpi.edu
• http//wcl.cs.rpi.edu

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• Extra Slides

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Simplex Operator Improvement (2)?
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Simplex Operator Improvement (3)?
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GMLE Architecture (Parallel-Asynchronous)?
Search Routines
Communication Layer
BOINC - HTTP
Grid - TCP/IP
Supercomputer - MPI
Results
Work
Work Request
Results
Work
Work Request
Worker (1)?
Worker (Z)?
Combine Results
Combine Results
Distribute Parameters
Distribute Parameters

MPI
MPI
Evaluator (1)?
Evaluator (N)?
Evaluator (2)?
Evaluator (1)?
Evaluator (M)?
Evaluator (2)?


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Operator Examination (1) - BlueGene
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Operator Examination (2) - BOINC
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Operator Examination (3) - BOINC
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Operator Examination (4) - BOINC
39
Operator Examination (5) - BOINC
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Operator Examination (6) - BOINC
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Operator Examination (7) - BOINC