All parallel algorithms are intended to have signatures that are
equivalent to the ISO C++ algorithms replaced. For instance, the
std::adjacent_find function is declared as:
namespace std
{
template<typename _FIter>
_FIter
adjacent_find(_FIter, _FIter);
}
Which means that there should be something equivalent for the parallel version. Indeed, this is the case:
namespace std
{
namespace __parallel
{
template<typename _FIter>
_FIter
adjacent_find(_FIter, _FIter);
...
}
}
But.... why the ellipses?
The ellipses in the example above represent additional overloads required for the parallel version of the function. These additional overloads are used to dispatch calls from the ISO C++ function signature to the appropriate parallel function (or sequential function, if no parallel functions are deemed worthy), based on either compile-time or run-time conditions.
The available signature options are specific for the different algorithms/algorithm classes.
The general view of overloads for the parallel algorithms look like this:
ISO C++ signature
ISO C++ signature + sequential_tag argument
ISO C++ signature + algorithm-specific tag type (several signatures)
Please note that the implementation may use additional functions
(designated with the _switch suffix) to dispatch from the
ISO C++ signature to the correct parallel version. Also, some of the
algorithms do not have support for run-time conditions, so the last
overload is therefore missing.
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;
}
One namespace contain versions of code that are always
explicitly sequential:
__gnu_serial.
Two namespaces contain the parallel mode:
std::__parallel and __gnu_parallel.
Parallel implementations of standard components, including
template helpers to select parallelism, are defined in namespace
std::__parallel. For instance, std::transform from algorithm has a parallel counterpart in
std::__parallel::transform from parallel/algorithm. In addition, these parallel
implementations are injected into namespace
__gnu_parallel with using declarations.
Support and general infrastructure is in namespace
__gnu_parallel.
More information, and an organized index of types and functions related to the parallel mode on a per-namespace basis, can be found in the generated source documentation.