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DPS for DC2 Summary

DPS for DC2 Summary. Model Implementation Pipeline & Slice in Python and C++ Stage Loop, Policy configuration, Event handling in Python MPI env and communications in C++ Executable scripts (run by mpiexec): runPipeline.py, runSlice.py Pipeline and Slice configured from same Policy file

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DPS for DC2 Summary

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  1. DPS for DC2 Summary • Model Implementation • Pipeline & Slice in Python and C++ • Stage Loop, Policy configuration, Event handling in Python • MPI env and communications in C++ • Executable scripts (run by mpiexec): runPipeline.py, runSlice.py • Pipeline and Slice configured from same Policy file • Clipboard, Queue, Stage in Python • One Clipboard per Pipeline/Slice used in DC2 • New: Generic Stages • InputStage, OutputStage, EventStage, SymLinkStage • Model elements not completed • Complete C++ implementation • Pipeline-Slice communication of data (DataProperty’s) • Full Queue capabilities • Clipboard metadata: less ad hoc mechanism (schema?)

  2. DPS for DC2 Summary (cont.) • Key Features • Events handled prior to Stage execution • Policy designates the stages that require a trigger event • Pipeline receives Events from external sources => events to Slices • MPI Communications are collective • All Slices need to be present, running thru Stage loop • Slices process each Stage in sync : MPI_Bcast, MPI_Barrier • Exception handling in important places • Exceptions from stage preprocess(), process(), postprocess() caught • If one Slice catches an Exception, others are undisturbed. • Multiple visits supported • Shutdown event implemented • Clean shutdown of MPI env/Slices at the end of Stage loop • Todo: a “no more data event” of the same topic as trigger events • Logging integrated into Pipeline/Slice • Memory management (Clipboard cleanup) stabilized

  3. DPS: DC2 and Beyond • Results • Three Parallel Pipelines executing Application Stages • Reasonable stability observed (~36 Slices across 6 nodes) • Performance: e.g., Utilization of 8 cores ? • Open Questions • Stage API : preprocess(), process(), postprocess() • Has this model been useful (validated)? • Direct MPI Communications • Finer communication between Pipeline/Slices? • Avoid events, collective operations? • Restart a Slice that disappears? • Slice/CCD mapping • Should these mapping strategies be integral part of dps? • High level script to run pipelines • run.sh, startPipeline.py? • Should dc2pipe/ scripts be incorporated into dps?

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