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Support parallelization of workflows #7

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whart222 opened this issue Nov 15, 2015 · 1 comment
Open

Support parallelization of workflows #7

whart222 opened this issue Nov 15, 2015 · 1 comment

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@whart222
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This is a long-term goal of this package, and there isn't anything in pyutilib.workflow that currently supports this. Here are some considerations for this, which are related to an upcoming workshop on many-task computing:

  • Compute Resource Management
    • Scheduling
    • Job execution frameworks
    • Local resource manager extensions
    • Performance evaluation of resource managers in use on large scale systems
    • Dynamic resource provisioning
    • Techniques to manage many-core resources and/or GPUs
    • Challenges and opportunities in running many-task workloads on HPC systems
    • Challenges and opportunities in running many-task workloads on Cloud Computing infrastructure
  • Storage architectures and implementations
    • Distributed file systems
    • Parallel file systems
    • Distributed meta-data management
    • Content distribution systems for large data
    • Data caching frameworks and techniques
    • Data management within and across data centers
    • Data-aware scheduling
    • Data-intensive computing applications
    • Eventual-consistency storage usage and management
  • Programming models and tools
    • Map-reduce and its generalizations
    • Many-task computing middleware and applications
    • Parallel programming frameworks
    • Ensemble MPI techniques and frameworks
    • Service-oriented science applications
  • Large-Scale Workflow Systems
    • Workflow system performance and scalability analysis
    • Scalability of workflow systems
    • Workflow infrastructure and e-Science middleware
    • Programming Paradigms and Models
  • Large-Scale Many-Task Applications
    • High-throughput computing (HTC) applications
    • Data-intensive applications
    • Quasi-supercomputing applications, deployments, and experiences
    • Performance Evaluation
  • Performance evaluation
    • Real systems
    • Simulations
    • Reliability of large systems
@whart222 whart222 added this to the Wish List milestone Nov 15, 2015
@whart222
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Here's an idea: use the makeflow tool:

Idea:
. Dump a workflow in makeflow format
. Pickle associated Python objects/data
. Execute remotely with makeflow

See: http://cse.nd.edu/~ccl/software/makeflow/

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