User Guide


OpenMP Code to Synchronize the Shared Resources

OpenMP provides several forms of synchronization:
  • A
    critical section
    prevents multiple threads from accessing the critical section's code at the same time, thus only one active thread can update the data referenced by the code. A critical section may consist of one or more statements. To implement a critical section:
    • With C/C++:
      #pragma omp critical
    • With Fortran:
      !$omp critical
      !$omp end critical
    Use the optional named form for a non-nested mutex, such as (C/C++)
    #pragma omp critical(name)
    or (Fortran)
    !$omp critical(name)
    !$omp end critical(name)
    . If the optional
    is omitted, it locks a single unnamed global mutex. The easiest approach is to use the unnamed form unless performance measurement shows this shared mutex is causing unacceptable delays.
  • An
    atomic operation
    allows multiple threads to safely update a shared numeric variable on hardware platforms that support its use. An atomic operation applies to only one assignment statement that immediately follows it. To implement an atomic operation:
    • With C/C++: insert a
      #pragma omp atomic
      before the statement to be protected.
    • With Fortran: insert a
      !$omp atomic
      before the statement to be protected.
    The statement to be protected must meet certain criteria (see your compiler or OpenMP documentation).
  • Locks
    provide a low-level means of general-purpose locking. To implement a lock, use the OpenMP types, variables, and functions to provide more flexible and powerful use of locks. For example, use the
    type in C/C++ or the
    in Fortran. These types and functions are easy to use and usually directly replace
    Intel Advisor
    lock annotations.
  • Reduction operations
    can be used for simple cases, such as incrementing a shared numeric variable or summing an array into a shared numeric variable. To implement a reduction operation, add the
    clause within a parallel region to instruct the compiler to perform the summation operation in parallel using the specified operation and variable.
  • OpenMP provides other synchronization techniques, including specifying a
    construct where threads will wait for each other, an
    construct that ensures sequential execution of a structured block within a parallel loop, and
    regions that can only be executed by the master thread. For more information, see your compiler or OpenMP documentation.
After you rewrite your code to use OpenMP* parallel framework, you can analyze its performance with
Intel® Advisor
perspectives. Use the
Vectorization and Code Insights
perspective to analyze how well you OpenMP code is vectorized or use the
Offload Modeling
perspective to model its performance on a GPU.
The following topics briefly describe these forms of synchronization. Check your compiler documentation for details.

Product and Performance Information


Performance varies by use, configuration and other factors. Learn more at