Several aspects of the overall runtime environment can be manipulated by standard OpenMP function calls.

To specify the number of threads to be used for the algorithms globally, use the function omp_set_num_threads. An example:

#include <stdlib.h>
#include <omp.h>

int main()
{
  // Explicitly set number of threads.
  const int threads_wanted = 20;
  omp_set_dynamic(false);
  omp_set_num_threads(threads_wanted);

  // Call parallel mode algorithms.

  return 0;
}

Some algorithms allow the number of threads being set for a particular call, by augmenting the algorithm variant. See the next section for further information.

Other parts of the runtime environment able to be manipulated include nested parallelism (omp_set_nested), schedule kind (omp_set_schedule), and others. See the OpenMP documentation for more information.

To force an algorithm to execute sequentially, even though parallelism is switched on in general via the macro _GLIBCXX_PARALLEL, add __gnu_parallel::sequential_tag() to the end of the algorithm's argument list.

Like so:

std::sort(v.begin(), v.end(), __gnu_parallel::sequential_tag());

Some parallel algorithm variants can be excluded from compilation by preprocessor defines. See the doxygen documentation on compiletime_settings.h and features.h for details.

For some algorithms, the desired variant can be chosen at compile-time by appending a tag object. The available options are specific to the particular algorithm (class).

For the "embarrassingly parallel" algorithms, there is only one "tag object type", the enum _Parallelism. It takes one of the following values, __gnu_parallel::parallel_tag, __gnu_parallel::balanced_tag, __gnu_parallel::unbalanced_tag, __gnu_parallel::omp_loop_tag, __gnu_parallel::omp_loop_static_tag. This means that the actual parallelization strategy is chosen at run-time. (Choosing the variants at compile-time will come soon.)

For the following algorithms in general, we have __gnu_parallel::parallel_tag and __gnu_parallel::default_parallel_tag, in addition to __gnu_parallel::sequential_tag. __gnu_parallel::default_parallel_tag chooses the default algorithm at compiletime, as does omitting the tag. __gnu_parallel::parallel_tag postpones the decision to runtime (see next section). For all tags, the number of threads desired for this call can optionally be passed to the respective tag's constructor.

The multiway_merge algorithm comes with the additional choices, __gnu_parallel::exact_tag and __gnu_parallel::sampling_tag. Exact and sampling are the two available splitting strategies.

For the sort and stable_sort algorithms, there are several additional choices, namely __gnu_parallel::multiway_mergesort_tag, __gnu_parallel::multiway_mergesort_exact_tag, __gnu_parallel::multiway_mergesort_sampling_tag, __gnu_parallel::quicksort_tag, and __gnu_parallel::balanced_quicksort_tag. Multiway mergesort comes with the two splitting strategies for multi-way merging. The quicksort options cannot be used for stable_sort.

The default parallelization strategy, the choice of specific algorithm strategy, the minimum threshold limits for individual parallel algorithms, and aspects of the underlying hardware can be specified as desired via manipulation of __gnu_parallel::_Settings member data.

First off, the choice of parallelization strategy: serial, parallel, or heuristically deduced. This corresponds to __gnu_parallel::_Settings::algorithm_strategy and is a value of enum __gnu_parallel::_AlgorithmStrategy type. Choices include: heuristic, force_sequential, and force_parallel. The default is heuristic.

Next, the sub-choices for algorithm variant, if not fixed at compile-time. Specific algorithms like find or sort can be implemented in multiple ways: when this is the case, a __gnu_parallel::_Settings member exists to pick the default strategy. For example, __gnu_parallel::_Settings::sort_algorithm can have any values of enum __gnu_parallel::_SortAlgorithm: MWMS, QS, or QS_BALANCED.

Likewise for setting the minimal threshold for algorithm parallelization. Parallelism always incurs some overhead. Thus, it is not helpful to parallelize operations on very small sets of data. Because of this, measures are taken to avoid parallelizing below a certain, pre-determined threshold. For each algorithm, a minimum problem size is encoded as a variable in the active __gnu_parallel::_Settings object. This threshold variable follows the following naming scheme: __gnu_parallel::_Settings::[algorithm]_minimal_n. So, for fill, the threshold variable is __gnu_parallel::_Settings::fill_minimal_n,

Finally, hardware details like L1/L2 cache size can be hardwired via __gnu_parallel::_Settings::L1_cache_size and friends.

All these configuration variables can be changed by the user, if desired. There exists one global instance of the class _Settings, i. e. it is a singleton. It can be read and written by calling __gnu_parallel::_Settings::get and __gnu_parallel::_Settings::set, respectively. Please note that the first call return a const object, so direct manipulation is forbidden. See settings.h for complete details.

A small example of tuning the default:

#include <parallel/algorithm>
#include <parallel/settings.h>

int main()
{
  __gnu_parallel::_Settings s;
  s.algorithm_strategy = __gnu_parallel::force_parallel;
  __gnu_parallel::_Settings::set(s);

  // Do work... all algorithms will be parallelized, always.

  return 0;
}