# Programming with Auto-parallelization

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

## Guidelines for Effective Auto-parallelization Usage

A loop can be parallelized if it meets the following criteria:

- The loop is countable at compile time: This means that an expression representing how many times the loop will execute (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; 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 !DIR$ PARALLEL directive to disambiguate assumed data dependencies.

- Insert the !DIR$ 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 Enabling Auto-parallelization.

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

4. High-level parallelization: Analyzing the dependency graph to determine loops that 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. Multithreaded code generation: Modifying loop parameters, generating entry/exit per threaded task, and generating calls to parallel run-time routines for thread creation and synchronization.

### Note

Options that use OpenMP are available for both Intel® and non-Intel microprocessors, but these options may perform additional optimizations on Intel® microprocessors than they perform on non-Intel microprocessors. The list of major, user-visible OpenMP constructs and features that may perform differently on Intel® microprocessors than on non-Intel microprocessors includes: locks (internal and user visible), the SINGLE construct, barriers (explicit and implicit), parallel loop scheduling, reductions, memory allocation, and thread affinity and binding.

Parent topic: Automatic Parallelization

## See Also

Enabling Auto-parallelization