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Distribution Efforts in Kepler / PTII

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LPPN – Technology Choices. Distribution Efforts in Kepler / PTII. C++ for core libraries Actor, Port, Token as C++ classes Parallel Virtual Machine (PVM) for parallelization Thin layer on top of machine clusters (pool of hosts) Message passing Implemented simple RPC on top of this

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LPPN – Technology Choices

Distribution Efforts in Kepler / PTII

  • C++ for core libraries
    • Actor, Port, Token as C++ classes
  • Parallel Virtual Machine (PVM) for parallelization
    • Thin layer on top of machine clusters (pool of hosts)
    • Message passing
    • Implemented simple RPC on top of this
  • SWIG for adding higher-languages above core
    • Perl/Python interfaces for writing actors
    • Perl interfaces for composing and starting workflow
    • Java interface for composing, starting, monitoring workflows
  • Remote execution of a complete workflow
    • Hydrant (Tristan King)
    • Web service for remote execution (Jianwu Wang)
    • Parameter sweeps with Nimrod/K (Colin Enticott, David Abramson, Ilkay Altintas)
  • Distribution within actors
    • “Plumping Workflows” with ad-hoc ssh-control (Nortbert Podhorszki)
    • Globus actors in Kepler: GlobusJob, GlobusProxy, GridFTP, GridJob.
    • GLite actors available through ITER
    • Webservice executions by actors
  • Distribution of few or all actors
    • Distributed SDF Director (Daniel Cuadrado)
    • Pegasus Director (Daniel Cuadrado and Yang Zhao)
    • Master-Slave Distributed Execution (Chad Berkley and Lucas Gilbert) with DistributedCompositeActor
    • PPN Director (Daniel Zinn and Xuan Li)

Thanks to Jianwu for help with overview

Lightweight Parallel PN Engine (LPPN)

  • Motivation
    • PN as inherently parallel MoC
    • Build simple, efficient distributed PN-engine
  • Design Requirements
    • KISS
    • Avoid centralization as much as possible
    • Provide Actor and Port abstractions
    • Allow actors being written in different languages
    • “Experimentation Platform” for scheduling, data routing, …
  • Design Principles
    • One actor = one process
    • Communication between actors
    • Central component only for setup, termination detection, …

PPN Director – Architecture Overview

PPN Director – Design Decisions

  • Proxy-Actors in Kepler represent Actors in LPPN
    • Repository of available LPPN Actors in XML file
      • Actor-name
      • Parameters
      • Ports
    • Generic PPN-Actor is configured using this information
    • Monitor actor state
    • Send data from Kepler Actors to LPPN actors and vice versa
  • PPN Director
    • Start Actors with parameters, deployment info
    • Connect Actors according to Kepler workflow
    • Start and stop workflow execution

Kepler

Future Directions

Kepler PPN Director

Communication with Regular PN Actors

  • Adding Black-box (Java) actors as actors in LPPN
  • Detailed measurements when actors need time for what
  • Automatic movement of actors for CPU congestions (deploying spring/mass model)
  • Automatic data parallelism (actor cloning and scatter+gather)
  • Overhaul of LPPN, maybe in Java, RMI, JNI
  • Better resource management
  • Idea: Use Kepler as sophisticated GUI
    • Create, run and monitor LPPN workflows
  • Marrying LPPN and Kepler – The PPN Director
    • Drag’n’drop workflow creation (1:1 mapping for actors)
    • Parameter support
    • Hints for deployment from user
    • Monitor token sending and receiving
    • Monitor actor status

Monitoring Support

  • PPN Actors periodically probe LPPN actors for info
    • Number of tokens sent and received
    • Current actor state:
      • Working
      • Block on receive
      • Block on write
      • Sending BLOB tokens
  • Displayed on actor while workflow is running

LPPN

  • Sending data from regular Kepler
  • Actors to LPPN and vice versa

Parallel Virtual Machines in KeplerDaniel Zinn Xuan Li Bertram LudäscherUniversity of California at Davis
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