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Apache Hadoop India Summit 2011 talk "Adaptive Parallel Computing over Distributed Military Computing Infrastructures" by Rituraj Kumar
 

Apache Hadoop India Summit 2011 talk "Adaptive Parallel Computing over Distributed Military Computing Infrastructures" by Rituraj Kumar

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    Apache Hadoop India Summit 2011 talk "Adaptive Parallel Computing over Distributed Military Computing Infrastructures" by Rituraj Kumar Apache Hadoop India Summit 2011 talk "Adaptive Parallel Computing over Distributed Military Computing Infrastructures" by Rituraj Kumar Presentation Transcript

    • Adaptive parallel computing over distributed military computing infrastructures
      RiturajKumar
      Center of Artificial Intelligence and Robotics – DRDO Lab
    • 2
      Contents
      • Introduction
      • Adaptive Parallel Computing
      • Approaches for APC
      • MPI + DDS
      • Hadoop
      • Conclusions
    • 3
      Introduction
      • Net-Centric Paradigm of warfare needs highly compute intensive operations to be performed.
      • Parallel Computing is the only feasible solution that would guarantee reduced response time.
      • Tactical deployment would not amenable for –
      • Large Clusters in the field
      • Back-hauling
      • Intelligent use of the existing spare compute capacity of the computing devices within the cloud is essential.
    • 4
      Adaptive Parallel Computing
      Cloud of Heterogeneous Computing Devices
      MILCOM
    • 5
      Adaptive Parallel Computing
      Amdahls Law
      The Amdahl's law is concerned with the speedup achievable from an improvement to a computation that affects a proportion P of that computation where the improvement has a speedup of S.
      Where,
      S = Speedup
      P = Parallel fraction of Program
      N = Number of Processors
      If 95% of the program can be parallelized, the theoretical maximum speedup using parallel computing would be 20×, no matter how many processors are used.
    • 6
      Adaptive Parallel Computing
      Cost for parallel Computation = Computation Cost + Serialization Cost
    • 7
      APC Approaches
      MPI + DDS
      MPI Code:
      • Parallel implementation of
      complex mathematical models
      MPI Controller:
      • Execution of parallel MPI code
      over distributed network.
      DDS:
      • Reliable Communication over
      challenged network.
      DMI:
      • Identification of computing
      nodes in the distributed
      network.
      MPI Code
      MPI
      Controller
      Dynamic Membership Identifier
      DDS
    • 8
      APC Approaches
      MPI + DDS
      Advantage:
      • Well-known MPI API Framework.
      • DDS provides assurance of reliable communication between nodes.
      Disadvantage:
      • Needs a wrapper for converting MPI calls to DDS framework.
      • Performance degradation because of the wrapper.
    • 9
      APC Approaches
      HADOOP
      • Map/Reduce can be effectively used for parallelization
      • Used extensively in varieties of information systems.
      • Java Based
      • Designed to handle large data sets
    • 10
      APC Approaches
      Hadoop
      Advantage:
      • Highly Scalable
      • Excellent fault tolerance capabilities
      • Good hardware interoperability
      Current challenges:
      • Requirement of slight architectural changes
      • Currently not suitable for resource constraint hardware
    • 11
      Conclusion
      • Military domain requires reliable parallel computing infrastructure over disadvantaged communication network.
      • Dynamic topology poses great challenge for computing infrastructure.
      • MPI framework has few disadvantages
      • Hadoop is a promising candidate in these conditions.
    • 12
      Thank You