Programming with Auto-parallelization

The auto-parallelization feature implements some concepts of OpenMP*, such as worksharing construct (with the parallel for directive). This section provides specifics of auto-parallelization.

Guidelines for Effective Auto-parallelization Usage

A loop is parallelizable if:

• The loop is countable at compile time. This means that an expression representing how many times the loop will execute (also called "the loop trip count") can be generated just before entering the loop.
• There are no FLOW (READ after WRITE), OUTPUT (WRITE after READ) or ANTI (WRITE after READ) loop-carried data dependences. A loop-carried data dependence occurs when the same memory location is referenced in different iterations of the loop. At the compiler's discretion, a loop may be parallelized if any assumed inhibiting loop-carried dependencies can be resolved by run-time dependency testing.

The compiler may generate a run-time test for the profitability of executing in parallel for loop with loop parameters that are not compile-time constants.

Coding Guidelines

Enhance the power and effectiveness of the auto-parallelizer by following these coding guidelines:

• Expose the trip count of loops whenever possible. Specifically use constants where the trip count is known and save loop parameters in local variables.
• Avoid placing structures inside loop bodies that the compiler may assume to carry dependent data, for example, function calls, ambiguous indirect references, or global references.

Auto-parallelization Data Flow

For auto-parallelization processing, the compiler performs the following steps:

1. Data flow analysis
2. Loop classification
3. Dependence analysis
4. High-level parallelization
5. Data partitioning
6. Multi-threaded code generation

These steps include:

• Data flow analysis: compute the flow of data through the program
• Loop classification: determine loop candidates for parallelization based on correctness and efficiency as shown by threshold analysis
• Dependence analysis: compute the dependence analysis for references in each loop nest
• High-level parallelization:
• analyze dependence graph to determine loops which can execute in parallel.
• compute run-time dependency
• Data partitioning: examine data reference and partition based on the following types of access: shared, private, and firstprivate.
• Multi-threaded code generation:
• modify loop parameters
• generate entry/exit per threaded task
• generate calls to parallel runtime routines for thread creation and synchronization