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  • Simple == Just two functions: Map and Reduce, Scalable == automatic parallelization across machines, fault tolerance, speculative execution,
  • Modify this slide to show partitioning function

Isda Presentation Transcript

  • 1. Scaling Genetic Algorithms using MapReduce
    AbhishekVerma, Xavier Llora,
    David E. Goldberg, Roy H. Campbell
  • 2. Motivation
    Genetic Algorithms (GAs)
    applied to very large scale data-intensiveproblems
    Current approach: MPI
    Requires detailed knowledge of h/w architecture
    Complicated to program, debug, checkpoint
    Does not scale on commodity clusters
    MapReduce: simple and scalable abstraction
    Use MapReduce to scale GAs
    2
    Intelligent Systems Design and Applications 2009
  • 3. Outline
    Motivation
    MapReduce
    Genetic Algorithm
    Approach
    Experimental Results
    Conclusion
    3
    Intelligent Systems Design and Applications 2009
  • 4. MapReduce Overview
    k1
    v1
    k1
    v1
    k2
    v2
    k1
    v3
    k1
    v3
    k1
    v5
    k2
    v2
    k2
    v4
    k2
    v4
    k1
    v5
    Input
    records
    h(k1)
    Output
    records
    Map
    Reduce
    h(k1)
    h(k2)
    Split
    h(k1)
    Reduce
    Map
    h(k2)
    Split
    Shuffle
    4
    Intelligent Systems Design and Applications 2009
  • 5. Genetic Algorithm
    Initialize population with random individuals.
    Evaluate fitness value of individuals.
    Select good solutions by using tournament selection without replacement.
    Create new individuals by recombining the selected population using uniform crossover.
    Evaluate the fitness value of all offspring.
    Repeat steps 3-5 until some convergence criteria are met.
    5
    Intelligent Systems Design and Applications 2009
  • 6. Genetic Algorithm
    Initialize population with random individuals.
    Evaluate fitness value of individuals.
    Repeat steps 4-5 to 2 until some convergence criteria are met.
    Select good solutions by using tournament selection without replacement.
    Create new individuals by recombining the selected population using uniform crossover.
    6
    Map
    Reduce
    Intelligent Systems Design and Applications 2009
  • 7. MapReducing Genetic Algorithm
    7
    Random
    partitioner
    00010
    10000
    01001
    <00010, 1>
    <10000, 1>
    <01001, 2>
    Map
    10110
    00001
    Reduce
    <01001, 2>
    10001
    01000
    10001
    01000
    Reduce
    10101
    10000
    00000
    <10101, 3>
    <10000, 1>
    <00000, 0>
    Map
    <10101, 3>
    Distributed File System
    Intelligent Systems Design and Applications 2009
  • 8. MapReducing Genetic Algorithm (2)
    Modifications
    Mappers write to DFS so that clients can evaluate convergence criteria and control next iteration
    Random partitioner function
    Maintain a window of individuals in each reducer
    Optimizations
    Create the initial population in 0th MapReduce
    Compactly represent bits in array of long ints
    8
    Intelligent Systems Design and Applications 2009
  • 9. Experimental Results
    9
    Experimental setup
    52 nodes: 16GB RAM, 2TB hard drives
    Each node runs 5 mappers + 3 reducers
    Population set to nlog(n)
    Intelligent Systems Design and Applications 2009
  • 10. Scaling GAs to 100 million variables
    10
    Intelligent Systems Design and Applications 2009
  • 11. Conclusion
    Modeled GAs in MapReduce
    Scales on a commodity clusters to 100 million variables
    Can also use Pthreads(Phoenix), GPUs(Mars), …
    Future Work
    Demonstrate scalability for practical applications
    MapReduce Compact GAs and Extended Compact GAs
    Comparison with MPI implementation
    11
    Intelligent Systems Design and Applications 2009
  • 12. Questions?
  • 13. Thank You