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Parallelizing Data Flow and Dependence Graphs

• Parallelizing Data Flow and Dependency Graphs
• Basic Flow Graph Concepts
  • Flow Graph Basics: Graph Object
  • Flow Graph Basics: Nodes
  • Flow Graph Basics: Edges
  • Flow Graph Basics: Mapping Nodes to Tasks
  • Flow Graph Basics: Message Passing Protocol
  • Flow Graph Basics: Single-push vs. Broadcast-push
  • Flow Graph Basics: Buffering and Forwarding
  • Flow Graph Basics: Reservation
• Graph Application Categories
  • Data Flow Graph
  • Dependence Graph
• Predefined Node Types
• Flow Graph Tips and Tricks
  • Flow Graph Tips for Waiting for and Destroying a Flow Graph
    • Always Use wait_for_all()
    • Avoid Dynamic Node Removal
    • Destroying Graphs That Run Outside the Main Thread
  • Flow Graph Tips on Making Edges
    • Use make_edge and remove_edge
    • Sending to One or Multiple Successors
    • Communication Between Graphs
    • Using input_node
    • Avoiding Data Races
  • Flow Graph Tips on Nested Parallelism
    • Use Nested Algorithms to Increase Scalability
    • Use Nested Flow Graphs
  • Flow Graph Tips for Limiting Resource Consumption
    • Using limiter_node
    • Use Concurrency Limits
    • Create a Token-Based System
    • Attach Flow Graph to an Arbitrary Task Arena
      • Guiding Task Scheduler Execution
      • Work Isolation
  • Flow Graph Tips for Exception Handling and Cancellation
    • Catching Exceptions Inside the Node that Throws the Exception
    • Cancel a Graph Explicitly
    • Use graph::reset() to Reset a Canceled Graph
    • Canceling Nested Parallelism
• Estimating Flow Graph Performance

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Summary of Loops and Pipelines

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Parallelizing Data Flow and Dependency Graphs