Programming with Auto-parallelization

The auto-parallelization feature implements some concepts of OpenMP*, such as the worksharing construct (with the PARALLEL DO directive). See Programming with OpenMP for worksharing construct. This section provides details on auto-parallelization.

Guidelines for Effective Auto-parallelization Usage

A loop can be parallelized 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 WRITE) or ANTI (WRITE after READ) loop-carried data dependencies. A loop-carried data dependency 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, procedure calls, ambiguous indirect references or global references.
• Insert the !DEC$ PARALLEL directive to disambiguate assumed data dependencies.
• Insert the !DEC$ NOPARALLEL directive before loops known to have insufficient work to justify the overhead of sharing among threads.

Auto-parallelization Data Flow

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

1. Data flow analysis: Computing the flow of data through the program.

2. Loop classification: Determining loop candidates for parallelization based on correctness and efficiency, as shown by threshold analysis.

3. dependency analysis: Computing the dependency analysis for references in each loop nest.

4. High-level parallelization: Analyzing dependency graph to determine loops which can execute in parallel, and computing run-time dependency.

5. Data partitioning: Examining data reference and partition based on the following types of access: SHARED, PRIVATE, and FIRSTPRIVATE.

6. Multi-threaded code generation: Modifying loop parameters, generating entry/exit per threaded task, and generating calls to parallel run-time routines for thread creation and synchronization.