# Parallel Mode

C++
library
parallel

The libstdc++ parallel mode is an experimental parallel implementation of many algorithms the C++ Standard Library.

Several of the standard algorithms, for instance `std::sort`, are made parallel using OpenMP annotations. These parallel mode constructs and can be invoked by explicit source declaration or by compiling existing sources with a specific compiler flag.

## Intro

The following library components in the include `numeric` are included in the parallel mode:

- `std::accumulate`

- `std::adjacent_difference`

- `std::inner_product`

- `std::partial_sum`

The following library components in the include `algorithm` are included in the parallel mode:

- `std::adjacent_find`

- `std::count`

- `std::count_if`

- `std::equal`

- `std::find`

- `std::find_if`

- `std::find_first_of`

- `std::for_each`

- `std::generate`

- `std::generate_n`

- `std::lexicographical_compare`

- `std::mismatch`

- `std::search`

- `std::search_n`

- `std::transform`

- `std::replace`

- `std::replace_if`

- `std::max_element`

- `std::merge`

- `std::min_element`

- `std::nth_element`

- `std::partial_sort`

- `std::partition`

- `std::random_shuffle`

- `std::set_union`

- `std::set_intersection`

- `std::set_symmetric_difference`

- `std::set_difference`

- `std::sort`

- `std::stable_sort`

- `std::unique_copy`

## Semantics

The parallel mode STL algorithms are currently not exception-safe, i.e. user-defined functors must not throw exceptions. Also, the order of execution is not guaranteed for some functions, of course. Therefore, user-defined functors should not have any concurrent side effects.

Since the current GCC OpenMP implementation does not support OpenMP parallel regions in concurrent threads, it is not possible to call parallel STL algorithm in concurrent threads, either. It might work with other compilers, though.

## Using

### Prerequisite Compiler Flags

Any use of parallel functionality requires additional compiler and runtime support, in particular support for OpenMP. Adding this support is not difficult: just compile your application with the compiler flag `-fopenmp`. This will link in `libgomp`, the OpenMP GNU implementation, whose presence is mandatory.

In addition, hardware that supports atomic operations and a compiler capable of producing atomic operations is mandatory: GCC defaults to no support for atomic operations on some common hardware architectures. Activating atomic operations may require explicit compiler flags on some targets (like sparc and x86), such as `-march=i686`, `-march=native` or `-mcpu=v9`. See the GCC manual for more information.

### Using Parallel Mode

To use the libstdc++ parallel mode, compile your application with the prerequisite flags as detailed above, and in addition add `-D_GLIBCXX_PARALLEL`. This will convert all use of the standard (sequential) algorithms to the appropriate parallel equivalents. Please note that this doesn't necessarily mean that everything will end up being executed in a parallel manner, but rather that the heuristics and settings coded into the parallel versions will be used to determine if all, some, or no algorithms will be executed using parallel variants.

Note that the `_GLIBCXX_PARALLEL` define may change the sizes and behavior of standard class templates such as `std::search`, and therefore one can only link code compiled with parallel mode and code compiled without parallel mode if no instantiation of a container is passed between the two translation units. Parallel mode functionality has distinct linkage, and cannot be confused with normal mode symbols.

### Using Specific Parallel Components

When it is not feasible to recompile your entire application, or only specific algorithms need to be parallel-aware, individual parallel algorithms can be made available explicitly. These parallel algorithms are functionally equivalent to the standard drop-in algorithms used in parallel mode, but they are available in a separate namespace as GNU extensions and may be used in programs compiled with either release mode or with parallel mode.

An example of using a parallel version of `std::sort`, but no other parallel algorithms, is:

    #include <vector>
    #include <parallel/algorithm>

    int main()
    {
      std::vector<int> v(100);

      // ...

      // Explicitly force a call to parallel sort.
      __gnu_parallel::sort(v.begin(), v.end());
      return 0;
    }

Then compile this code with the prerequisite compiler flags (`-fopenmp` and any necessary architecture-specific flags for atomic operations.)

The following table provides the names and headers of all the parallel algorithms that can be used in a similar manner:

#### Parallel Algorithms

Algorithm | Header | Parallel algorithm | Parallel header
--- | --- | --- | ---
`std::accumulate` | `numeric` | `__gnu_parallel::accumulate` | `parallel/numeric`
`std::adjacent_difference` | `numeric` | `__gnu_parallel::adjacent_difference` | `parallel/numeric`
`std::inner_product` | `numeric` | `__gnu_parallel::inner_product` | `parallel/numeric`
`std::partial_sum` | `numeric` | `__gnu_parallel::partial_sum` | `parallel/numeric`
`std::adjacent_find` | `algorithm` | `__gnu_parallel::adjacent_find` | `parallel/algorithm`
`std::count` | `algorithm` | `__gnu_parallel::count` | `parallel/algorithm`
`std::count_if` | `algorithm` | `__gnu_parallel::count_if` | `parallel/algorithm`
`std::equal` | `algorithm` | `__gnu_parallel::equal` | `parallel/algorithm`
`std::find` | `algorithm` | `__gnu_parallel::find` | `parallel/algorithm`
`std::find_if` | `algorithm` | `__gnu_parallel::find_if` | `parallel/algorithm`
`std::find_first_of` | `algorithm` | `__gnu_parallel::find_first_of` | `parallel/algorithm`
`std::for_each` | `algorithm` | `__gnu_parallel::for_each` | `parallel/algorithm`
`std::generate` | `algorithm` | `__gnu_parallel::generate` | `parallel/algorithm`
`std::generate_n` | `algorithm` | `__gnu_parallel::generate_n` | `parallel/algorithm`
`std::lexicographical_compare` | `algorithm` | `__gnu_parallel::lexicographical_compare` | `parallel/algorithm`
`std::mismatch` | `algorithm` | `__gnu_parallel::mismatch` | `parallel/algorithm`
`std::search` | `algorithm` | `__gnu_parallel::search` | `parallel/algorithm`
`std::search_n` | `algorithm` | `__gnu_parallel::search_n` | `parallel/algorithm`
`std::transform` | `algorithm` | `__gnu_parallel::transform` | `parallel/algorithm`
`std::replace` | `algorithm` | `__gnu_parallel::replace` | `parallel/algorithm`
`std::replace_if` | `algorithm` | `__gnu_parallel::replace_if` | `parallel/algorithm`
`std::max_element` | `algorithm` | `__gnu_parallel::max_element` | `parallel/algorithm`
`std::merge` | `algorithm` | `__gnu_parallel::merge` | `parallel/algorithm`
`std::min_element` | `algorithm` | `__gnu_parallel::min_element` | `parallel/algorithm`
`std::nth_element` | `algorithm` | `__gnu_parallel::nth_element` | `parallel/algorithm`
`std::partial_sort` | `algorithm` | `__gnu_parallel::partial_sort` | `parallel/algorithm`
`std::partition` | `algorithm` | `__gnu_parallel::partition` | `parallel/algorithm`
`std::random_shuffle` | `algorithm` | `__gnu_parallel::random_shuffle` | `parallel/algorithm`
`std::set_union` | `algorithm` | `__gnu_parallel::set_union` | `parallel/algorithm`
`std::set_intersection` | `algorithm` | `__gnu_parallel::set_intersection` | `parallel/algorithm`
`std::set_symmetric_difference` | `algorithm` | `__gnu_parallel::set_symmetric_difference` | `parallel/algorithm`
`std::set_difference` | `algorithm` | `__gnu_parallel::set_difference` | `parallel/algorithm`
`std::sort` | `algorithm` | `__gnu_parallel::sort` | `parallel/algorithm`
`std::stable_sort` | `algorithm` | `__gnu_parallel::stable_sort` | `parallel/algorithm`
`std::unique_copy` | `algorithm` | `__gnu_parallel::unique_copy` | `parallel/algorithm`

## Design

### Interface Basics

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.

### Configuration and Tuning

#### Setting up the OpenMP Environment

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.

#### Compile Time Switches

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`.

#### Run Time Settings and Defaults

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;
    }

### Implementation Namespaces

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.

## Testing

Both the normal conformance and regression tests and the supplemental performance tests work.

To run the conformance and regression tests with the parallel mode active,

      make check-parallel

The log and summary files for conformance testing are in the `testsuite/parallel` directory.

To run the performance tests with the parallel mode active,

      make check-performance-parallel

The result file for performance testing are in the `testsuite` directory, in the file `libstdc++_performance.sum`. In addition, the policy-based containers have their own visualizations, which have additional software dependencies than the usual bare-boned text file, and can be generated by using the `make doc-performance` rule in the testsuite's Makefile.

## Bibliography

Parallelization of Bulk Operations for STL Dictionaries

Johannes
Singler
Leonor
Frias
2007
Workshop on Highly Parallel Processing on a Chip (HPPC) 2007. (LNCS)
The Multi-Core Standard Template Library

Johannes
Singler
Peter
Sanders
Felix
Putze
2007
Euro-Par 2007: Parallel Processing. (LNCS 4641)