Support parallelization of workflows

[whart222]

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