Support parallelization of workflows

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

PyUtilib / pyutilib

Support parallelization of workflows #7