thread pool
v4.1.0
BS::thread_pool ：高速で軽量で使いやすいC ++ 17スレッドプールライブラリBarak Shoshanyによる
メール：[email protected]
ウェブサイト：https：//baraksh.com/
Github：https：//github.com/bshoshany
これは、2024-03-22にリリースされたライブラリのV4.1.0の完全なドキュメントです。
BS::multi_future<T>の詳細BS::synced_streamを使用してストリームに印刷を同期しますBS::timerで実行時間を測定しますBS::signaller使用してスレッド間で簡単な信号を送信しますBS_thread_pool.hpp ）BS::thread_poolクラスBS::thread_poolクラスのオプション機能BS::this_thread namespaceBS::multi_future<T>クラスBS_thread_pool_utils.hpp ）BS::signallerクラスBS::synced_streamクラスBS::timerクラスマルチスレッドは、最新の高性能コンピューティングに不可欠です。 C ++ 11以降、C ++標準ライブラリにはstd::threadなどのコンストラクトを使用した低レベルのマルチスレッドサポートが組み込まれています。ただし、 std::thread呼び出されるたびに新しいスレッドを作成します。これは、大きなパフォーマンスオーバーヘッドを持つことができます。さらに、ハードウェアが同時に処理できるよりも多くのスレッドを作成することが可能であり、潜在的に大幅な減速をもたらす可能性があります。
ここに示されているライブラリには、C ++スレッドプールクラス、 BS::thread_poolが含まれています。これにより、これらの問題は、固定されたスレッドのプールを完全に作成し、プログラムの生涯を通じて異なるタスクを実行するために同じスレッドを継続的に再利用します。デフォルトでは、プール内のスレッドの数は、ハードウェアが並行して実行できるスレッドの最大数に等しくなります。
ユーザーは、実行されるタスクをキューに送信します。スレッドが利用可能になるたびに、キューから次のタスクを取得して実行します。プールは、各タスクのstd::futureを自動的に生成します。これにより、ユーザーはタスクが実行を終了し、該当する場合は最終的な返品値を取得するのを待つことができます。スレッドとタスクは、目的のタスクを送信する以外にユーザーからの入力を必要とせずに、バックグラウンドのプールによって自律的に管理されます。
このライブラリの設計は、4つの重要な原則によって導かれています。まず、コンパクトさ：ライブラリ全体は、オプションのユーティリティを備えた小さな自己完結型ヘッダーファイルを除き、他のコンポーネントや依存関係を持つ1つの自己完結型ヘッダーファイルで構成されています。第二に、ポータビリティ：ライブラリは、コンパイラ拡張機能や第3パーティライブラリに依存することなく、C ++ 17の標準ライブラリのみを使用しているため、プラットフォーム上の最新の標準を構成するC ++ 17コンパイラと互換性があります。第三に、使いやすさ：ライブラリは広範囲に文書化されており、あらゆるレベルのプログラマーがすぐに使用できるはずです。
4番目の最終ガイド原則はパフォーマンスです。このライブラリのすべてのコード行は、最大のパフォーマンスを念頭に置いて慎重に設計され、パフォーマンスはさまざまなコンパイラやプラットフォームでテストおよび検証されました。実際、ライブラリはもともと、著者自身の計算集中型科学コンピューティングプロジェクトで使用するように設計されており、ハイエンドのデスクトップ/ラップトップコンピューターと高性能コンピューティングノードの両方で実行されています。
その他のより高度なマルチスレッドライブラリは、より多くの機能やより高いパフォーマンスを提供する場合があります。ただし、通常、複数のコンポーネントと依存関係を備えた広大なコードベースで構成されており、学習にかなりの時間投資が必要な複雑なAPIが含まれます。このライブラリは、これらのより高度なライブラリを置き換えることを意図していません。代わりに、非常に高度な機能を必要としないユーザー向けに設計されており、学習と使用が簡単で、既存または新しいプロジェクトに容易に組み込むことができるシンプルで軽量のライブラリを好みます。
#include "BS_thread_pool.hpp"だけで、あなたはすべて設定されています！submit_task()メンバー関数を使用してキューに送信されたすべてのタスクはstd::future自動的に生成します。BS::multi_futureを返すsubmit_loop()メンバー関数を使用して、任意の数のタスクに自動的に平行化できます。detach_task()を使用してタスクを提出することができ、ループはdetach_loop()を使用して平行化できます - パフォーマンスをさらに高めるために利便性を犠牲にします。その場合、 wait() 、 wait_for() 、およびwait_until()を使用して、キュー内のすべてのタスクが完了するのを待つことができます。BS_thread_pool_test.cppは、徹底的な自動テストとベンチマークを実行するために使用でき、ライブラリを適切に使用する方法の包括的な例としても機能します。含まれるPowerShellスクリプトBS_thread_pool_test.ps1は、複数のコンパイラでテストを実行するポータブルな方法を提供します。BS_thread_pool_utils.hppには、いくつかの便利なユーティリティクラスが含まれています。BS::signallerユーティリティクラスを使用して、スレッド間で簡単な信号を送信します。BS::synced_streamユーティリティクラスを使用して、複数のスレッドからのストリームへの出力を同期します。BS::timerユーティリティクラスを使用して、ベンチマーク目的で実行時間を簡単に測定します。detach_sequence()およびsubmit_sequence()を使用して、インデックスによって列挙された一連のタスクをキューに送信します。reset()メンバー関数を使用して、必要に応じて、プール内のスレッドの数を安全に変更します。get_tasks_queued() 、 get_tasks_running() 、およびget_tasks_total()メンバー関数を使用して、キューの数および/または実行中のタスクの数を監視します。get_thread_count()を使用して、プールの現在のスレッド数を取得します。pause() 、 unpause() 、およびis_paused()メンバー関数を使用して、自由に一時停止してプールを再開します。一時停止したとき、スレッドはキューから新しいタスクを取得しません。purge()メンバー関数でキューで現在待機しているすべてのタスクをパージします。submit_task()またはsubmit_loop()を使用して送信されたタスクによってスローされた例外をキャッチします。BS::this_thread::get_index()を使用して現在のスレッドのプールインデックスを取得し、 BS::this_thread::get_pool()を使用してスレッドを所有するプールへのポインターを取得します。get_thread_ids()を使用してプール内のすべてのスレッドの一意のスレッドIDまたはオプションのget_native_handles()メンバー関数を使用して実装定義のスレッドハンドルを使用します。このライブラリは、そのようなコンパイラが利用できるすべてのオペレーティングシステムとアーキテクチャで、C ++ 17の標準準拠コンパイラで正常にコンパイルする必要があります。互換性は、次のコンパイラとプラットフォームを使用して、24コア（8p+16e） / 32-thread Intel I9-13900K CPUで検証されました。
さらに、このライブラリは、GCC V13.2.0を使用してCentos Linux 7.9.2009を実行している2つの20コア / 40スレッドIntel Xeon Gold 6148 CPU（合計40コアと80個のスレッド）を装備したカナダノードのデジタルリサーチアライアンスでテストされました。
テストプログラムBS_thread_pool_test.cppは警告なしにコンパイルされました（警告フラグ-Wall -Wextra -Wconversion -Wsign-conversion -Wpedantic -Weffc++ -Wshadow in GCC /Clangおよび/W4のMSVCのW4）、実行されたすべての自動テストを使用して、すべての自動テストとベンチマークを使用して完全に完了しました。
このライブラリにはC ++ 17の機能が必要なため、コードはC ++ 17のサポートでコンパイルする必要があります。
-std=c++17フラグを使用します。 Linuxでは、 -pthreadフラグを使用してPOSIXスレッドライブラリを有効にする必要があります。/std:c++17を使用し、標準の適合性を確保するために使用/permissive- 。パフォーマンスを最大限に活用するには、利用可能なすべてのコンパイラの最適化でコンパイルすることをお勧めします。
-O3フラグを使用します。/O2を使用します。例として、警告と最適化を使用してテストプログラムBS_thread_pool_test.cppをコンパイルするには、次のコマンドを使用することをお勧めします。
g++ BS_thread_pool_test.cpp -std=c++17 -O3 -Wall -Wextra -Wconversion -Wsign-conversion -Wpedantic -Weffc++ -Wshadow -pthread -o BS_thread_pool_testg++ clang++に置き換えます。-o BS_thread_pool_testを-o BS_thread_pool_test.exeに置き換え、 -pthreadを削除します。cl BS_thread_pool_test.cpp /std:c++17 /permissive- /O2 /W4 /EHsc /Fo:BS_thread_pool_test.obj /Fe:BS_thread_pool_test.exe BS::thread_poolをインストールするには、Githubリポジトリから最新リリースをダウンロードするだけで、目的のフォルダーのincludeフォルダーからヘッダーファイルBS_thread_pool.hppを配置し、プログラムにそれを含めます。
# include " BS_thread_pool.hpp "スレッドプールは、 BS::thread_poolクラスからアクセスできるようになりました。さらに迅速にインストールするために、このURLでヘッダーファイル自体を直接ダウンロードできます。
このライブラリには、独立したユーティリティヘッダーファイルBS_thread_pool_utils.hppも付属しています。このヘッダーファイルは、 includeフォルダーにもあります。このURLで直接ダウンロードできます。
このライブラリは、VCPKG、CONAN、MESON、CMAKEなどのさまざまなパッケージマネージャーやビルドシステムでも利用できます。詳細については、以下をご覧ください。
デフォルトのコンストラクターはstd::thread::hardware_concurrency()を介した実装で報告されているように、ハードウェアと同じくらい多くのスレッドを備えたスレッドプールを同時に処理できるスレッドプールを作成します。これは通常、CPUのコア数によって決定されます。コアがハイパースレッドされている場合、2つのスレッドとしてカウントされます。例えば：
// Constructs a thread pool with as many threads as available in the hardware.
BS::thread_pool pool;オプションで、ハードウェアの並行性とは異なる多くのスレッドを、コンストラクターへの引数として指定できます。ただし、ハードウェアが処理できるよりも多くのスレッドを追加してもパフォーマンスは向上しないことに注意してください。実際、妨害する可能性が高いことに注意してください。このオプションは、他のプロセスで利用可能なスレッドを残したい場合に、ハードウェアの同時性よりも少ないスレッドを使用することを許可するために存在します。例えば：
// Constructs a thread pool with only 12 threads.
BS::thread_pool pool ( 12 );通常、スレッドプールを使用する場合、プログラムのメインスレッドはタスクをスレッドプールにのみ提出し、それらが終了するのを待つ必要があり、それ自体で計算的に集中的なタスクを実行する必要はありません。その場合、スレッドの数にデフォルト値を使用することをお勧めします。これにより、メインスレッドが待機している間、ハードウェアで使用可能なすべてのスレッドが機能するようになります。
メンバー関数get_thread_count()プール内のスレッドの数を返します。これは、デフォルトのコンストラクターが使用された場合std::thread::hardware_concurrency()に等しくなります。
スレッドプールの全体的なポイントは、スレッドを1回だけ作成することであるため、プールが作成された後、プールのスレッドの数を変更する必要はありません。ただし、必要に応じて、これは、 reset()メンバー関数を使用して、安全に、オンザフライで行うことができます。
reset() 、現在実行されているすべてのタスクが完了するのを待ちますが、残りのタスクはキューに残ります。次に、関数の引数で指定されているように、スレッドプールを破壊し、目的の新しい数のスレッドを使用して新しいスレッドを作成します（または、引数が与えられていない場合はハードウェアの並行性）。新しいスレッドプールは、キューに残っているタスクと新しい提出されたタスクの実行を再開します。
必要に応じて、このライブラリのバージョンは、次の3つのマクロからの編集時間中に読み取ることができます。
BS_THREAD_POOL_VERSION_MAJORメジャーバージョンを示します。BS_THREAD_POOL_VERSION_MINORマイナーバージョンを示します。BS_THREAD_POOL_VERSION_PATCHパッチバージョンを示します。 std::cout << " Thread pool library version is " << BS_THREAD_POOL_VERSION_MAJOR << ' . ' << BS_THREAD_POOL_VERSION_MINOR << ' . ' << BS_THREAD_POOL_VERSION_PATCH << " . n " ;サンプル出力：
Thread pool library version is 4.1.0.
これは、たとえば、同じコードベースが#ifディレクティブを使用してライブラリのいくつかの互換性のないバージョンで動作できるようにするために使用できます。
注：この機能は、v4.0.1からのみ使用できます。このライブラリの以前のリリースでは、これらのマクロは定義されていません。
このセクションでは、引数なしではなく、潜在的に返品値を持つタスクをキューに送信する方法を学びます。タスクが送信されると、スレッドが利用可能になるとすぐに実行されます。タスクの優先度が有効になっていない限り（以下を参照）、タスクは提出された順に実行されます（最初の、最初のアウト）。
たとえば、プールに8つのスレッドと空のキューがあり、16のタスクを提出した場合、最初の8つのタスクが並行して実行されると予想され、残りのタスクはスレッドによって1つずつピックアップされます。
メンバー関数submit_task()は、キューにタスクを送信するために使用されます。提出するタスクである1つの入力が正確に必要です。このタスクは、引数なしの関数でなければなりませんが、返品値を持つことができます。返品値は、タスクに関連付けられたstd::futureです。
送信された関数にタイプTの返品値がある場合、将来はstd::future<T>のタイプになり、関数が実行を終了すると戻り値に設定されます。提出された関数に戻り値がない場合、将来はstd::future<void>になります。
submit_task()の返品値にauto使用すると、コンパイラがテンプレートstd::futureのどのインスタンスを使用するかを自動的に検出します。ただし、以下の例のように、特定のタイプstd::future<T>を指定することをお勧めします。
タスクが終了するまで待つには、将来のメンバー関数wait()を使用します。戻り値を取得するには、メンバー関数get()を使用します。これは、タスクがまだない場合に自動的に終了するまで待ちます。簡単な例を次に示します。
# include " BS_thread_pool.hpp " // BS::thread_pool
# include < future > // std::future
# include < iostream > // std::cout
int the_answer ()
{
return 42 ;
}
int main ()
{
BS::thread_pool pool;
std::future< int > my_future = pool. submit_task (the_answer);
std::cout << my_future. get () << ' n ' ;
}この例では、 intを返す関数the_answer()を提出しました。したがって、プールのメンバー関数submit_task()はstd::future<int>を返しました。次に、Futureのget()メンバー関数を使用して返品値を取得し、印刷しました。
事前定義された関数を提出することに加えて、ラムダ式を使用して、オンザフライでタスクを迅速に定義することもできます。ラムダの表現の観点から前の例を書き直すと、次のようになります。
# include " BS_thread_pool.hpp " // BS::thread_pool
# include < future > // std::future
# include < iostream > // std::cout
int main ()
{
BS::thread_pool pool;
std::future< int > my_future = pool. submit_task ([]{ return 42 ; });
std::cout << my_future. get () << ' n ' ;
}ここで、ラムダ式[]{ return 42; } 2つの部分があります。
[]で示される空のキャプチャ条項。これは、ラムダの式が定義されていることをコンパイラに意味します。{ return 42; }値42単に返すだけです。一般に、特に次のセクションで説明するローカル変数をキャプチャする能力により、事前定義された機能ではなく、ラムダ式を提出する方がシンプルで高速です。
もちろん、タスクは値を返す必要はありません。次の例では、返品値のない関数を送信し、将来を使用して実行が終了するのを待ちます。
# include " BS_thread_pool.hpp " // BS::thread_pool
# include < chrono > // std::chrono
# include < future > // std::future
# include < iostream > // std::cout
# include < thread > // std::this_thread
int main ()
{
BS::thread_pool pool;
const std::future< void > my_future = pool. submit_task (
[]
{
std::this_thread::sleep_for ( std::chrono::milliseconds ( 500 ));
});
std::cout << " Waiting for the task to complete... " ;
my_future. wait ();
std::cout << " Done. " << ' n ' ;
}ここでは、ラムダを複数のラインに分割して、読みやすくします。コマンドstd::this_thread::sleep_for(std::chrono::milliseconds(500))計算集中タスクをシミュレートするために、500ミリ秒間睡眠をとるようにタスクに指示します。
前のセクションで述べたように、 submit_task()を使用して送信されたタスクは引数を持つことはできません。ただし、ラムダで関数を包むか、ラムダキャプチャを直接使用することにより、引数でタスクを簡単に送信することは簡単です。ここに2つの例があります。
以下は、ラムダでそれを包むことにより、引数で事前に定義された関数を提出する例です。
# include " BS_thread_pool.hpp " // BS::thread_pool
# include < future > // std::future
# include < iostream > // std::cout
double multiply ( const double lhs, const double rhs)
{
return lhs * rhs;
}
int main ()
{
BS::thread_pool pool;
std::future< double > my_future = pool. submit_task (
[]
{
return multiply ( 6 , 7 );
});
std::cout << my_future. get () << ' n ' ;
}ご覧のとおり、引数を渡すためにmultiplyするために、ラムダ内でmultiply(6, 7)明示的に呼び出します。引数がリテラルではない場合は、Lambda Capture句を使用して、ローカル範囲からの引数をキャプチャする必要があります。
# include " BS_thread_pool.hpp " // BS::thread_pool
# include < future > // std::future
# include < iostream > // std::cout
double multiply ( const double lhs, const double rhs)
{
return lhs * rhs;
}
int main ()
{
BS::thread_pool pool;
constexpr double first = 6 ;
constexpr double second = 7 ;
std::future< double > my_future = pool. submit_task (
[first, second]
{
return multiply (first, second);
});
std::cout << my_future. get () << ' n ' ;
}必要に応じて、 multiply関数を完全に取り除き、ラムダの中にすべてを配置することもできます。
# include " BS_thread_pool.hpp " // BS::thread_pool
# include < future > // std::future
# include < iostream > // std::cout
int main ()
{
BS::thread_pool pool;
constexpr double first = 6 ;
constexpr double second = 7 ;
std::future< double > my_future = pool. submit_task (
[first, second]
{
return first * second;
});
std::cout << my_future. get () << ' n ' ;
}通常、 submit_task()を使用してタスクをキューに送信するのが最善です。これにより、タスクが終了したり、後で返品値を取得するのを待つことができます。ただし、たとえば、特定のタスクを「設定して忘れて」やりたい場合、または条件変数などの先物を使用せずにタスクがすでにメインスレッドまたは他のタスクと通信している場合、未来が必要ない場合があります。
そのような場合、パフォーマンスを向上させるために、将来をタスクに割り当てることに伴うオーバーヘッドを避けることをお勧めします。これは、タスクがメインスレッドから切り離され、独立して実行されるため、タスクを「デタッチ」すると呼ばれます。
タスクの分離は、 detach_task()メンバー関数を使用して行われます。これにより、将来を生成せずにタスクをキューにデタッチできます。タスクには任意の数の引数を持つことができますが、メインスレッドがその値を取得する方法がないため、返品値を持つことはできません。
detach_task()将来を返さないため、ユーザーがタスクの実行がいつ終了するかを知るための組み込みの方法はありません。出力に依存するものをすべて使用しようとする前に、タスクが実行が完了していることを手動で確認する必要があります。そうでなければ、悪いことが起こります！
BS::thread_poolメンバー関数wait()を提供して、キュー内のすべてのタスクが完了するか、将来に分離されているか、提出されたかにかかわらず、完了します。 wait()メンバー関数は、 std::futureのwait()メンバー関数と同様に機能します。たとえば、次のコードを検討してください。
# include " BS_thread_pool.hpp " // BS::thread_pool
# include < chrono > // std::chrono
# include < iostream > // std::cout
# include < thread > // std::this_thread
int main ()
{
BS::thread_pool pool;
int result = 0 ;
pool. detach_task (
[&result]
{
std::this_thread::sleep_for ( std::chrono::milliseconds ( 100 ));
result = 42 ;
});
std::cout << result << ' n ' ;
}このプログラムは、最初にresultという名前のローカル変数を定義し、それを0に初期化します。次に、ラムダの表現の形でタスクを分離します。 Lambdaは、その前の&内で示されるように、参照によってresultをキャプチャすることに注意してください。これは、タスクがresultを変更できることを意味し、そのような変更はメインスレッドに反映されます。タスクは42に変更resultれますが、最初に100ミリ秒間眠ります。メインスレッドがresultの値を印刷するとき、タスクはまだ眠っているため、その価値を変更する時間がまだありませんでした。したがって、プログラムは初期値0を印刷します。
タスクが完了するのを待つには、detach後にwait()メンバー関数を使用する必要があります。
# include " BS_thread_pool.hpp " // BS::thread_pool
# include < chrono > // std::chrono
# include < iostream > // std::cout
# include < thread > // std::this_thread
int main ()
{
BS::thread_pool pool;
int result = 0 ;
pool. detach_task (
[&result]
{
std::this_thread::sleep_for ( std::chrono::milliseconds ( 100 ));
result = 42 ;
});
pool. wait ();
std::cout << result << ' n ' ;
}これで、プログラムは予想どおりValue 42を印刷します。ただし、 wait()は、キュー内のすべてのタスクを待機することに注意してください。 1つのタスクだけを待ちたい場合は、 submit_task()より良い選択です。
タスクが完了するのを待つことを望むかもしれませんが、一定の時間、または特定の時点までのみです。たとえば、タスクがしばらくしてまだ完了していない場合は、遅延があることをユーザーに知らせることをお勧めします。
submit_task()を使用して先物で送信されたタスクの場合、これはstd::future ：futureの2つのメンバー関数を使用して達成できます。
wait_for()タスクが完了するのを待ちますが、 std::chrono::durationのタイプの引数として与えられた指定された期間後に待機を停止します。wait_until()タスクが完了するのを待ちますが、タイプstd::chrono::time_pointの引数として与えられた指定された時点の後に待機を停止します。どちらの場合も、関数はfuture_status::readyできています。ただし、タイムアウトが期限切れになるまでに未来がまだ準備ができていない場合std::future_status::timeoutを返します。
これが例です：
# include " BS_thread_pool.hpp " // BS::thread_pool
# include < chrono > // std::chrono
# include < future > // std::future
# include < iostream > // std::cout
# include < thread > // std::this_thread
int main ()
{
BS::thread_pool pool;
const std::future< void > my_future = pool. submit_task (
[]
{
std::this_thread::sleep_for ( std::chrono::milliseconds ( 1000 ));
std::cout << " Task done! n " ;
});
while ( true )
{
if (my_future. wait_for ( std::chrono::milliseconds ( 200 )) != std::future_status::ready)
std::cout << " Sorry, the task is not done yet. n " ;
else
break ;
}
}出力はこれに似ているはずです。
Sorry, the task is not done yet.
Sorry, the task is not done yet.
Sorry, the task is not done yet.
Sorry, the task is not done yet.
Task done!
分離されたタスクの場合、それらの未来がないため、この方法を使用することはできません。ただし、 BS::thread_poolには2つのメンバー関数があり、 wait_for()とwait_until()とも呼ばれます。これは、指定された期間または指定された時点まで同様に待機しますが、すべてのタスク（提出または剥離したかどうか）に対してそれを行います。 std::future_statusの代わりに、すべてのタスクが実行された場合、または期間が終了した場合、または時点に到達した場合にfalse trueが、いくつかのタスクがまだ実行されている場合、スレッドプールの待機関数はtrueを返します。
以下は、 detach_task()とpool.wait_for()を使用して、上記と同じ例です。
# include " BS_thread_pool.hpp " // BS::thread_pool
# include < chrono > // std::chrono
# include < iostream > // std::cout
# include < thread > // std::this_thread
int main ()
{
BS::thread_pool pool;
pool. detach_task (
[]
{
std::this_thread::sleep_for ( std::chrono::milliseconds ( 1000 ));
std::cout << " Task done! n " ;
});
while ( true )
{
if (!pool. wait_for ( std::chrono::milliseconds ( 200 )))
std::cout << " Sorry, the task is not done yet. n " ;
else
break ;
}
}次のプログラムを考えてみましょう。
# include < iostream > // std::cout, std::boolalpha
class flag_class
{
public:
[[nodiscard]] bool get_flag () const
{
return flag;
}
void set_flag ( const bool arg)
{
flag = arg;
}
private:
bool flag = false ;
};
int main ()
{
flag_class flag_object;
flag_object. set_flag ( true );
std::cout << std::boolalpha << flag_object. get_flag () << ' n ' ;
}このプログラムは、クラスflag_classの新しいオブジェクトflag_objectを作成し、setterメンバーfunction set_flag()を使用してフラグをtrueに設定し、getterメンバー関数get_flag()を使用してフラグの値を印刷します。
メンバー関数set_flag()スレッドプールにタスクとして送信する場合はどうなりますか？ステートメント全体をラップするだけでflag_object.set_flag(true);この例のように、lambdaの線から、参照によりflag_object参照してラムダに渡します。
# include " BS_thread_pool.hpp " // BS::thread_pool
# include < iostream > // std::cout, std::boolalpha
class flag_class
{
public:
[[nodiscard]] bool get_flag () const
{
return flag;
}
void set_flag ( const bool arg)
{
flag = arg;
}
private:
bool flag = false ;
};
int main ()
{
BS::thread_pool pool;
flag_class flag_object;
pool. submit_task (
[&flag_object]
{
flag_object. set_flag ( true );
})
. wait ();
std::cout << std::boolalpha << flag_object. get_flag () << ' n ' ;
}もちろん、これは、返された将来ではなく、プール自体でwait()を呼び出す場合にもdetach_task()で動作します。
この例では、 submit_task()から将来を取得し、その将来を待つ代わりに、その将来にwait()すぐに呼び出したことに注意してください。これは、その間に他に何もすることがない場合、タスクが完了するのを待つ一般的な方法です。また、Lambdaを参照してflag_objectを通過したことに注意してください。同じオブジェクトにフラグを設定したいので、そのコピーではなく、値を通過することはとにかく機能しconstんでした。
あなたがしたいと思うかもしれないもう1つのことは、オブジェクト自体、つまり別のメンバー関数からメンバー関数を呼び出すことです。これは同様の構文に従いますが、ラムダのthis （つまり、現在のオブジェクトへのポインター）もキャプチャする必要があることを除きます。これが例です：
# include " BS_thread_pool.hpp " // BS::thread_pool
# include < iostream > // std::cout, std::boolalpha
BS::thread_pool pool;
class flag_class
{
public:
[[nodiscard]] bool get_flag () const
{
return flag;
}
void set_flag ( const bool arg)
{
flag = arg;
}
void set_flag_to_true ()
{
pool. submit_task (
[ this ]
{
set_flag ( true );
})
. wait ();
}
private:
bool flag = false ;
};
int main ()
{
flag_class flag_object;
flag_object. set_flag_to_true ();
std::cout << std::boolalpha << flag_object. get_flag () << ' n ' ;
}この例では、スレッドプールをグローバルオブジェクトとして定義し、 main()関数の外側にアクセスできるようにすることに注意してください。
最も一般的で効果的な並列化方法の1つは、ループをより小さなループに分割し、それらを並行して実行することです。これは、ループの各反復が他のすべての反復とは完全に独立している、ベクトルまたはマトリックス操作などの「恥ずかしいほど並行」計算に最も効果的です。
たとえば、それぞれ1000要素の2つのベクトルを合計し、10個のスレッドがある場合、合計をそれぞれ10個の要素の10ブロックに分割し、すべてのブロックを並列で実行でき、パフォーマンスを10倍まで潜在的に増加させます。
BS::thread_poolループを自動的に並列化できます。これがどのように機能するかを確認するには、次の汎用ループを検討してください。
for (T i = start; i < end; ++i)
loop (i);どこ：
T 、署名済みまたは署名されていない整数タイプです。[start, end) 、つまりstartを含みますが、 endを除く。loop() end - start要素を持つ配列の変更など、各ループインデックスiに対して実行される操作です。このループは自動的に平行化され、メンバー関数submit_loop()を使用してスレッドプールのキューに送信できます。
pool.submit_loop(start, end, loop, num_blocks);どこ：
start 、範囲内の最初のインデックスです。end 、範囲内の最後のインデックスの後のインデックスであり、フルレンジが[start, end)になるようにします。つまり、 startとendが同じ場合、ループは上記のループと同等になります。startとend両方とも同じ整数タイプTでなければなりません。同じタイプではない場合に何をすべきかの例については、以下を参照してください。end <= startと、何も起こらないことに注意してください。loop()は、ループのすべての反復で実行する必要がある関数であり、1つの引数、ループインデックスを取ります。num_blocksは、ループを分割するフォーム[a, b)のブロックの数です。たとえば、範囲が[0, 9)で、3つのブロックがある場合、ブロックは範囲[0, 3) 、 [3, 6) 、および[6, 9)になります。[0, 100)が15ブロックに分割されている場合、結果はサイズ7の10ブロックになり、最初に実行され、サイズ6の5ブロックになります。各ブロックは、別のタスクとしてスレッドプールのキューに送信されます。したがって、3つのブロックに分割されるループは、3つの個別のタスクに分割され、並列に実行される場合があります。ブロックが1つしかない場合、ループ全体が1つのタスクとして実行され、並列化は行われません。
上記の一般的なループを並列化するには、次のコマンドを使用します。
BS::multi_future< void > loop_future = pool.submit_loop(start, end, loop, num_blocks);
loop_future.wait(); submit_loop()ヘルパークラステンプレートBS::multi_futureのオブジェクトを返します。これは本質的にstd::vector<std::future<T>>の専門化です。追加のメンバー関数。各num_blocksブロックにはstd::futureが割り当てられ、これらすべての先物は返されたBS::multi_futureに保存されます。 loop_future.wait()が呼び出されると、mainスレッドは、 submit_loop()が実行されるすべてのタスクが実行され、それらのタスクのみがキューにある他のタスクではありません。これは本質的にBS::multi_futureクラスの役割です。特定のタスクのグループ、この場合はループブロックを実行しているタスクを待ちます。
num_blocksにはどのような価値を使用する必要がありますか？この引数を省略して、ブロックの数がプール内のスレッドの数に等しくなるようにすることは、通常、良い選択です。ベストパフォーマンスのために、各ループに最適なブロック数を見つけるために独自のベンチマークを実行することをお勧めします（ BS::timerユーティリティクラスを使用できます）。他のタスクも並行して実行している場合、スレッドがあるよりも少ないタスクを使用することが望ましい場合があります。スレッドよりも多くのタスクを使用すると、場合によってはパフォーマンスが向上する可能性がありますが、あまりにも多くのタスクとの並列化は、リターンの減少に悩まされます。
簡単な例として、次のコードは、0から99までのすべての整数の正方形のテーブルを計算および印刷します。
# include < iomanip > // std::setw
# include < iostream > // std::cout
int main ()
{
constexpr unsigned int max = 100 ;
unsigned int squares[max];
for ( unsigned int i = 0 ; i < max; ++i)
squares[i] = i * i;
for ( unsigned int i = 0 ; i < max; ++i)
std::cout << std::setw ( 2 ) << i << " ^2 = " << std::setw ( 4 ) << squares[i] << ((i % 5 != 4 ) ? " | " : " n " );
}次のように並列化できます。
# include " BS_thread_pool.hpp " // BS::thread_pool
# include < iomanip > // std::setw
# include < iostream > // std::cout
int main ()
{
BS::thread_pool pool ( 10 );
constexpr unsigned int max = 100 ;
unsigned int squares[max];
const BS::multi_future< void > loop_future = pool. submit_loop < unsigned int >( 0 , max,
[&squares]( const unsigned int i)
{
squares[i] = i * i;
});
loop_future. wait ();
for ( unsigned int i = 0 ; i < max; ++i)
std::cout << std::setw ( 2 ) << i << " ^2 = " << std::setw ( 4 ) << squares[i] << ((i % 5 != 4 ) ? " | " : " n " );
} 10個のスレッドがあり、 num_blocks引数を省略したため、ループは10ブロックに分割され、それぞれが10個の正方形を計算します。
submit_loop()は、明示的なテンプレートパラメーター<unsigned int>で実行されたことに注意してください。その理由は、2つのループインデックスが同じタイプでなければならないためです。ただし、ここではmaxはunsigned intであり、 0は（署名された） intであるため、タイプは一致せず、 0を適切なタイプのものにしない限り、コードはコンパイルされません。これは、テンプレートパラメーターを明示的に使用してインデックスのタイプを指定することにより、最もエレガントに行うことができます。
これが自動的に行われない理由（たとえば、 std::common_typeを使用しているため、誤った型に誤ってネガティブインデックスをキャストしたり、整数インデックスを狭すぎて整数タイプに挿入したりする可能性があり、それが誤ったループ範囲につながる可能性があります。
0明示的に署名していないINTにキャストすることもできますが、それはそれほど良く見えません。
pool.submit_loop( static_cast < unsigned int >( 0 ), max, /* ... */ );または、Cスタイルのキャストを使用できます。
pool.submit_loop(( unsigned int )( 0 ), max, /* ... */ );または、整数リテラルサフィックスを使用できます。
pool.submit_loop< size_t >( 0U , max, ...);サイドノートとして、ここでは正方形の計算に並行しているが、結果の印刷を並列化しなかったことに注意してください。これには2つの理由があります。
detach_task() submit_task()の場合と同様に、ループを並列化することもありますが、 BS::multi_futureを返すために必要ではありません。この場合、同じ引数を使用して、 submit_loop()の代わりにdetach_loop()を使用して、先物を生成するオーバーヘッド（ブロックの数に応じて重要な場合があります）を保存できます。
たとえば、上記の正方形のループのループを次のように取り外すことができます。
# include " BS_thread_pool.hpp " // BS::thread_pool
# include < iomanip > // std::setw
# include < iostream > // std::cout
int main ()
{
BS::thread_pool pool ( 10 );
constexpr unsigned int max = 100 ;
unsigned int squares[max];
pool. detach_loop < unsigned int >( 0 , max,
[&squares]( const unsigned int i)
{
squares[i] = i * i;
});
pool. wait ();
for ( unsigned int i = 0 ; i < max; ++i)
std::cout << std::setw ( 2 ) << i << " ^2 = " << std::setw ( 4 ) << squares[i] << ((i % 5 != 4 ) ? " | " : " n " );
} Warning: Since detach_loop() does not return a BS::multi_future , there is no built-in way for the user to know when the loop finishes executing. You must use either wait() as we did here, or some other method such as condition variables, to ensure that the loop finishes executing before trying to use anything that depends on its output. Otherwise, bad things will happen!
We have seen that detach_loop() and submit_loop() execute the function loop(i) for each index i in the loop. However, behind the scenes, the loop is split into blocks, and each block executes the loop() function multiple times. Each block has an internal loop of the form (where T is the type of the indices):
for (T i = start; i < end; ++i)
loop (i); The start and end indices of each block are determined automatically by the pool. For example, in the previous section, the loop from 0 to 100 was split into 10 blocks of 10 indices each: start = 0 to end = 10 , start = 10 to end = 20 , and so on; the blocks are not inclusive of the last index, since the for loop has the condition i < end and not i <= end .
However, this also means that the loop() function is executed multiple times per block. This generates additional overhead due to the multiple function calls. For short loops, this should not affect performance. However, for very long loops, with millions of indices, the performance cost may be significate.
For this reason, the thread pool library provides two additional member functions for parallelizing loops: detach_blocks() and submit_blocks() . While detach_loop() and submit_loop() execute a function loop(i) once per index but multiple times per block, detach_blocks() and submit_blocks() execute a function block(start, end) once per block.
The main advantage of this method is increased performance, but the main disadvantage is slightly more complicated code. In particular, the user must define the loop from start to end manually within each block. Here is the previous example using detach_blocks() :
# include " BS_thread_pool.hpp " // BS::thread_pool
# include < iomanip > // std::setw
# include < iostream > // std::cout
int main ()
{
BS::thread_pool pool ( 10 );
constexpr unsigned int max = 100 ;
unsigned int squares[max];
pool. detach_blocks < unsigned int >( 0 , max,
[&squares]( const unsigned int start, const unsigned int end)
{
for ( unsigned int i = start; i < end; ++i)
squares[i] = i * i;
});
pool. wait ();
for ( unsigned int i = 0 ; i < max; ++i)
std::cout << std::setw ( 2 ) << i << " ^2 = " << std::setw ( 4 ) << squares[i] << ((i % 5 != 4 ) ? " | " : " n " );
}Note how the block function takes two arguments, and includes the internal loop.
Generally, compiler optimizations should be able to make detach_loop() and submit_loop() perform roughly the same as detach_blocks() and submit_blocks() . However, you should perform your own benchmarks to see which option works best for your particular use case.
Unlike submit_task() , the member function submit_loop() only takes loop functions with no return values. The reason is that it wouldn't make sense to return a future for every single index of the loop. However, submit_blocks() does allow the block function to have a return value, as the number of blocks will generally not be too large, unlike the number of indices.
The block function will be executed once for each block, but the blocks are managed by the thread pool, with the user only able to select the number of blocks, but not the range of each block. Therefore, there is limited usability in returning one value per block. However, for cases where this is desired, such as for summation or some sorting algorithms, submit_blocks() does accept functions with return values, in which case it returns a BS::multi_future<T> object where T is the type of the return values.
Here's an example of a function template summing all elements of type T in a given range:
# include " BS_thread_pool.hpp " // BS::thread_pool
# include < cstdint > // std::uint64_t
# include < future > // std::future
# include < iostream > // std::cout
BS::thread_pool pool;
template < typename T>
T sum (T min, T max)
{
BS::multi_future<T> loop_future = pool. submit_blocks <T>(
min, max + 1 ,
[]( const T start, const T end)
{
T block_total = 0 ;
for (T i = start; i < end; ++i)
block_total += i;
return block_total;
},
100 );
T result = 0 ;
for (std::future<T>& future : loop_future)
result += future. get ();
return result;
}
int main ()
{
std::cout << sum<std:: uint64_t >( 1 , 1'000'000 );
} Here we used the fact that BS::multi_future<T> is a specialization of std::vector<std::future<T>> , so we can use a range-based for loop to iterate over the futures, and use the get() member function of each future to get its value. The values of the futures will be the partial sums from each block, so when we add them up, we will get the total sum. Note that we divided the loop into 100 blocks, so there will be 100 futures in total, each with the partial sum of 10,000 numbers.
The range-based for loop will likely start before the loop finished executing, and each time it calls a future, it will get the value of that future if it is ready, or it will wait until the future is ready and then get the value. This increases performance, since we can start summing the results without waiting for the entire loop to finish executing first - we only need to wait for individual blocks.
If we did want to wait until the entire loop finishes before summing the results, we could have used the get() member function of the BS::multi_future<T> object itself, which returns an std::vector<T> with the values obtained from each future. In that case, the sum could be obtained after calling submit_blocks() as follows:
std::vector<T> partial_sums = loop_future.get();
T result = std::reduce(partial_sums.begin(), partial_sums.end());
return result; The member functions detach_loop() , submit_loop() , detach_blocks() , and submit_blocks() parallelize a loop by splitting it into blocks, and submitting each block as an individual task to the queue, with each such task iterating over all the indices in the corresponding block's range, which can be numerous. However, sometimes we have loops with few indices, or more generally, a sequence of tasks enumerated by some index. In such cases, we can avoid the overhead of splitting into blocks and simply submit each individual index as its own independent task to the pool's queue.
This can be done with detach_sequence() and submit_sequence() . The syntax of these functions is similar to detach_loop() and submit_loop() , except that they don't have the num_blocks argument at the end. The sequence function must take only one argument, the index. As usual, detach_sequence() detaches the tasks and does not return a future, while submit_sequence() returns a BS::multi_future . If the tasks in the sequence return values, then the futures will contain those values, otherwise they will be void futures.
Here is a simple example:
# include " BS_thread_pool.hpp " // BS::thread_pool
# include < cstdint > // std::uint64_t
# include < iostream > // std::cout
# include < vector > // std::vector
using ui64 = std:: uint64_t ;
ui64 factorial ( const ui64 n)
{
ui64 result = 1 ;
for (ui64 i = 2 ; i <= n; ++i)
result *= i;
return result;
}
int main ()
{
BS::thread_pool pool;
constexpr ui64 max = 20 ;
BS::multi_future<ui64> sequence_future = pool. submit_sequence <ui64>( 0 , max + 1 , factorial);
std::vector<ui64> factorials = sequence_future. get ();
for (ui64 i = 0 ; i < max + 1 ; ++i)
std::cout << i << " ! = " << factorials[i] << ' n ' ;
}BS::multi_future<T> The helper class template BS::multi_future<T> , which we have been using throughout this section, provides a convenient way to collect and access groups of futures. This class is a specialization of std::vector<T> , so it should be used in a similar way:
[] operator to access the future at a specific index, or the push_back() member function to append a new future to the list.size() member function tells you how many futures are currently stored in the object. However, BS::multi_future<T> also has additional member functions that are aimed specifically at handling futures:
wait() to wait for all of them at once or get() to get an std::vector<T> with the results from all of them.ready_count() .valid() .wait_for() or wait until a specific time with wait_until() . These functions return true if all futures have been waited for before the duration expired or the time point was reached, and false otherwise. Aside from using BS::multi_future<T> to track the execution of parallelized loops, it can also be used, for example, whenever you have several different groups of tasks and you want to track the execution of each group individually.
The optional header file BS_thread_pool_utils.hpp contains several useful utility classes. These are not necessary for using the thread pool itself; BS_thread_pool.hpp is the only header file required. However, the utility classes can make writing multithreading code more convenient.
As with the main header file, the version of the utilities header file can be found by checking three macros:
BS_THREAD_POOL_UTILS_VERSION_MAJOR - indicates the major version.BS_THREAD_POOL_UTILS_VERSION_MINOR - indicates the minor version.BS_THREAD_POOL_UTILS_VERSION_PATCH - indicates the patch version.BS::synced_streamWhen printing to an output stream from multiple threads in parallel, the output may become garbled. For example, consider this code:
# include " BS_thread_pool.hpp " // BS::thread_pool
# include < iostream > // std::cout
BS::thread_pool pool;
int main ()
{
pool. detach_sequence ( 0 , 5 ,
[]( int i)
{
std::cout << " Task no. " << i << " executing. n " ;
});
}The output will be a mess similar to this:
Task no. Task no. Task no. 3 executing.
0 executing.
Task no. 41 executing.
Task no. 2 executing.
executing.
The reason is that, although each individual insertion to std::cout is thread-safe, there is no mechanism in place to ensure subsequent insertions from the same thread are printed contiguously.
The utility class BS::synced_stream is designed to eliminate such synchronization issues. The constructor takes one optional argument, specifying the output stream to print to. If no argument is supplied, std::cout will be used:
// Construct a synced stream that will print to std::cout.
BS::synced_stream sync_out;
// Construct a synced stream that will print to the output stream my_stream.
BS::synced_stream sync_out (my_stream); The member function print() takes an arbitrary number of arguments, which are inserted into the stream one by one, in the order they were given. println() does the same, but also prints a newline character n at the end, for convenience. A mutex is used to synchronize this process, so that any other calls to print() or println() using the same BS::synced_stream object must wait until the previous call has finished.
As an example, this code:
# include " BS_thread_pool.hpp " // BS::thread_pool
# include " BS_thread_pool_utils.hpp " // BS::synced_stream
BS::synced_stream sync_out;
BS::thread_pool pool;
int main ()
{
pool. detach_sequence ( 0 , 5 ,
[]( int i)
{
sync_out. println ( " Task no. " , i, " executing. " );
});
}Will print out:
Task no. 0 executing.
Task no. 1 executing.
Task no. 2 executing.
Task no. 3 executing.
Task no. 4 executing.
Warning: Always create the BS::synced_stream object before the BS::thread_pool object, as we did in this example. When the BS::thread_pool object goes out of scope, it waits for the remaining tasks to be executed. If the BS::synced_stream object goes out of scope before the BS::thread_pool object, then any tasks using the BS::synced_stream will crash. Since objects are destructed in the opposite order of construction, creating the BS::synced_stream object before the BS::thread_pool object ensures that the BS::synced_stream is always available to the tasks, even while the pool is destructing.
Most stream manipulators defined in the headers <ios> and <iomanip> , such as std::setw (set the character width of the next output), std::setprecision (set the precision of floating point numbers), and std::fixed (display floating point numbers with a fixed number of digits), can be passed to print() and println() just as you would pass them to a stream.
The only exceptions are the flushing manipulators std::endl and std::flush , which will not work because the compiler will not be able to figure out which template specializations to use. Instead, use BS::synced_stream::endl and BS::synced_stream::flush . Here is an example:
# include " BS_thread_pool.hpp " // BS::thread_pool
# include " BS_thread_pool_utils.hpp " // BS::synced_stream
# include < cmath > // std::sqrt
# include < iomanip > // std::setprecision, std::setw
# include < ios > // std::fixed
BS::synced_stream sync_out;
BS::thread_pool pool;
int main ()
{
sync_out. print ( std::setprecision ( 10 ), std::fixed);
pool. detach_sequence ( 0 , 16 ,
[]( int i)
{
sync_out. print ( " The square root of " , std::setw ( 2 ), i, " is " , std::sqrt (i), " . " , BS::synced_stream::endl);
});
} Note, however, that BS::synced_stream::endl should only be used if flushing is desired; otherwise, a newline character should be used instead.
BS::timerIf you are using a thread pool, then your code is most likely performance-critical. Achieving maximum performance requires performing a considerable amount of benchmarking to determine the optimal settings and algorithms. Therefore, it is important to be able to measure the execution time of various computations and operations under different conditions.
The utility class BS::timer provides a simple way to measure execution time. It is very straightforward to use:
BS::timer object.start() member function.stop() member function.ms() to obtain the elapsed time for the computation in milliseconds.current_ms() to obtain the elapsed time so far but keep the timer ticking.例えば：
BS::timer tmr;
tmr.start();
do_something ();
tmr.stop();
std::cout << " The elapsed time was " << tmr.ms() << " ms. n " ; A practical application of the BS::timer class can be found in the benchmark portion of the test program BS_thread_pool_test.cpp .
BS::signaller BS::signaller is a utility class which can be used to allow simple signalling between threads. To use it, construct an object and then pass it to the different threads. Multiple threads can call the wait() member function of the signaller. When another thread calls the ready() member function, the waiting threads will stop waiting.
That's really all there is to it; BS::signaller is really just a convenient wrapper around std::promise , which contains both the promise and its future. For usage examples, please see the test program BS_thread_pool_test.cpp .
Sometimes you may wish to monitor what is happening with the tasks you submitted to the pool. This may be done using three member functions:
get_tasks_queued() gets the number of tasks currently waiting in the queue to be executed by the threads.get_tasks_running() gets the number of tasks currently being executed by the threads.get_tasks_total() gets the total number of unfinished tasks: either still in the queue, or running in a thread.get_tasks_total() == get_tasks_queued() + get_tasks_running() .These functions are demonstrated in the following program:
# include " BS_thread_pool.hpp " // BS::thread_pool
# include " BS_thread_pool_utils.hpp " // BS::synced_stream
# include < chrono > // std::chrono
# include < thread > // std::this_thread
BS::synced_stream sync_out;
BS::thread_pool pool ( 4 );
void sleep_half_second ( const int i)
{
std::this_thread::sleep_for ( std::chrono::milliseconds ( 500 ));
sync_out. println ( " Task " , i, " done. " );
}
void monitor_tasks ()
{
sync_out. println (pool. get_tasks_total (), " tasks total, " , pool. get_tasks_running (), " tasks running, " , pool. get_tasks_queued (), " tasks queued. " );
}
int main ()
{
pool. wait ();
pool. detach_sequence ( 0 , 12 , sleep_half_second);
monitor_tasks ();
std::this_thread::sleep_for ( std::chrono::milliseconds ( 750 ));
monitor_tasks ();
std::this_thread::sleep_for ( std::chrono::milliseconds ( 500 ));
monitor_tasks ();
std::this_thread::sleep_for ( std::chrono::milliseconds ( 500 ));
monitor_tasks ();
}Assuming you have at least 4 hardware threads (so that 4 tasks can run concurrently), the output should be similar to:
12 tasks total, 0 tasks running, 12 tasks queued.
Task 0 done.
Task 1 done.
Task 2 done.
Task 3 done.
8 tasks total, 4 tasks running, 4 tasks queued.
Task 4 done.
Task 5 done.
Task 6 done.
Task 7 done.
4 tasks total, 4 tasks running, 0 tasks queued.
Task 8 done.
Task 9 done.
Task 10 done.
Task 11 done.
0 tasks total, 0 tasks running, 0 tasks queued.
The reason we called pool.wait() in the beginning is that when the thread pool is created, an initialization task runs in each thread, so if we don't wait, the first line will say there are 16 tasks in total, including the 4 initialization tasks.詳細については、以下をご覧ください。
Consider a situation where the user cancels a multithreaded operation while it is still ongoing. Perhaps the operation was split into multiple tasks, and half of the tasks are currently being executed by the pool's threads, but the other half are still waiting in the queue.
The thread pool cannot terminate the tasks that are already running, as the C++17 standard does not provide that functionality (and in any case, abruptly terminating a task while it's running could have extremely bad consequences, such as memory leaks and data corruption). However, the tasks that are still waiting in the queue can be purged using the purge() member function.
Once purge() is called, any tasks still waiting in the queue will be discarded, and will never be executed by the threads. Please note that there is no way to restore the purged tasks; they are gone forever.
Consider for example the following program:
# include " BS_thread_pool.hpp " // BS::thread_pool
# include " BS_thread_pool_utils.hpp " // BS::synced_stream
# include < chrono > // std::chrono
# include < thread > // std::this_thread
BS::synced_stream sync_out;
BS::thread_pool pool ( 4 );
int main ()
{
for ( size_t i = 0 ; i < 8 ; ++i)
{
pool. detach_task (
[i]
{
std::this_thread::sleep_for ( std::chrono::milliseconds ( 100 ));
sync_out. println ( " Task " , i, " done. " );
});
}
std::this_thread::sleep_for ( std::chrono::milliseconds ( 50 ));
pool. purge ();
pool. wait ();
} The program submit 8 tasks to the queue. Each task waits 100 milliseconds and then prints a message. The thread pool has 4 threads, so it will execute the first 4 tasks in parallel, and then the remaining 4. We wait 50 milliseconds, to ensure that the first 4 tasks have all started running. Then we call purge() to purge the remaining 4 tasks. As a result, these tasks never get executed. However, since the first 4 tasks are still running when purge() is called, they will finish uninterrupted; purge() only discards tasks that have not yet started running. The output of the program therefore only contains the messages from the first 4 tasks:
Task 0 done.
Task 1 done.
Task 2 done.
Task 3 done.
submit_task() catches any exceptions thrown by the submitted task and forwards them to the corresponding future. They can then be caught when invoking the get() member function of the future.例えば：
# include " BS_thread_pool.hpp "
BS::synced_stream sync_out;
BS::thread_pool pool;
double inverse ( const double x)
{
if (x == 0 )
throw std::runtime_error ( " Division by zero! " );
else
return 1 / x;
}
int main ()
{
constexpr double num = 0 ;
std::future< double > my_future = pool. submit_task (inverse, num);
try
{
const double result = my_future. get ();
sync_out. println ( " The inverse of " , num, " is " , result, " . " );
}
catch ( const std:: exception & e)
{
sync_out. println ( " Caught exception: " , e. what ());
}
}The output will be:
Caught exception: Division by zero!
However, if you change num to any non-zero number, no exceptions will be thrown and the inverse will be printed.
It is important to note that wait() does not throw any exceptions; only get() does. Therefore, even if your task does not return anything, ie your future is an std::future<void> , you must still use get() on the future obtained from it if you want to catch exceptions thrown by it. Here is an example:
# include " BS_thread_pool.hpp "
BS::synced_stream sync_out;
BS::thread_pool pool;
void print_inverse ( const double x)
{
if (x == 0 )
throw std::runtime_error ( " Division by zero! " );
else
sync_out. println ( " The inverse of " , x, " is " , 1 / x, " . " );
}
int main ()
{
constexpr double num = 0 ;
std::future< void > my_future = pool. submit_task (print_inverse, num);
try
{
my_future. get ();
}
catch ( const std:: exception & e)
{
sync_out. println ( " Caught exception: " , e. what ());
}
} When using BS::multi_future to handle multiple futures at once, exception handling works the same way: if any of the futures may throw exceptions, you may catch these exceptions when calling get() , even in the case of BS::multi_future<void> .
If you do not require exception handling, or if exceptions are explicitly disabled in your codebase, you can define the macro BS_THREAD_POOL_DISABLE_EXCEPTION_HANDLING before including BS_thread_pool.hpp , which will disable exception handling in submit_task() . Note that if the feature-test macro __cpp_exceptions is undefined, BS_THREAD_POOL_DISABLE_EXCEPTION_HANDLING will be automatically defined.
BS::thread_pool comes with a variety of methods to obtain information about the threads in the pool:
BS::this_thread provides functionality similar to std::this_thread . If the current thread belongs to a BS::thread_pool object, then BS::this_thread::get_index() can be used to get the index of the current thread, and BS::this_thread::get_pool() can be used to get the pointer to the thread pool that owns the current thread. Please see the reference below for more details.get_thread_ids() returns a vector containing the unique identifiers for each of the pool's threads, as obtained by std::thread::get_id() . These values are not so useful on their own, but can be used for whatever the user wants to use them for.get_native_handles() , if enabled, returns a vector containing the underlying implementation-defined thread handles for each of the pool's threads, as obtained by std::thread::native_handle() . For more information, see the relevant section below. Sometimes, it is necessary to initialize the threads before they run any tasks. This can be done by submitting a proper initialization function to the constructor or to reset() , either as the only argument or as the second argument after the desired number of threads. The thread initialization must take no arguments and have no return value. However, if needed, the function can use BS::this_thread::get_index() and BS::this_thread::get_pool() to figure out which thread and pool it belongs to.
The thread initialization function is submitted as a set of special tasks, one per thread, which bypass the queue, but still count towards the number of running tasks, which means get_tasks_total() and get_tasks_running() will report that these tasks are running if they are checked immediately after the pool is initialized.
This is done so that the user has the option to either wait for the initialization tasks to finish, by calling wait() on the pool, or just keep going. In either case, the initialization tasks will always finish executing before any tasks are picked out of the queue, so there is no reason to wait for them to finish unless they have some side-effects that affect the main thread.
Here is a simple example:
# include " BS_thread_pool.hpp " // BS::thread_pool
# include " BS_thread_pool_utils.hpp " // BS::synced_stream
# include < random > // std::mt19937_64, std::random_device
BS::synced_stream sync_out;
thread_local std::mt19937_64 twister;
int main ()
{
BS::thread_pool pool (
[]
{
twister. seed ( std::random_device ()());
});
pool. submit_sequence ( 0 , 4 ,
[]( int )
{
sync_out. println ( " I generated a random number: " , twister ());
})
. wait ();
} In this example, we create a thread_local Mersenne twister engine, meaning that each thread has its own independent engine. However, we did not seed the engine, so each thread will generate the exact same sequence of pseudo-random numbers. To remedy this, we pass an initialization function to the BS::thread_pool constructor which seeds the twister in each thread with the (hopefully) non-deterministic random number generator std::random_device .
In C++, it is often crucial to pass function arguments by reference or constant reference, instead of by value. This allows the function to access the object being passed directly, rather than creating a new copy of the object. We have already seen that submitting an argument by reference is a simple matter of capturing it with a & in the lambda capture list. To submit as constant reference, we can use std::as_const as in the following example:
# include " BS_thread_pool.hpp " // BS::thread_pool
# include " BS_thread_pool_utils.hpp " // BS::synced_stream
# include < utility > // std::as_const
BS::synced_stream sync_out;
void increment ( int & x)
{
++x;
}
void print ( const int & x)
{
sync_out. println (x);
}
int main ()
{
BS::thread_pool pool;
int n = 0 ;
pool. submit_task (
[&n]
{
increment (n);
})
. wait ();
pool. submit_task (
[&n = std::as_const (n)]
{
print (n);
})
. wait ();
} The increment() function takes a reference to an integer, and increments that integer. Passing the argument by reference guarantees that n itself, in the scope of main() , will be incremented - rather than a copy of it in the scope of increment() .
Similarly, the print() function takes a constant reference to an integer, and prints that integer. Passing the argument by constant reference guarantees that the variable will not be accidentally modified by the function, even though we are accessing n itself, rather than a copy. If we replace print with increment , the program won't compile, as increment cannot take constant references.
Generally, it is not really necessary to pass arguments by constant reference, but it is more "correct" to do so, if we would like to guarantee that the variable being referenced is indeed never modified. This section is therefore included here for completeness.
Sometimes you may wish to temporarily pause the execution of tasks, or perhaps you want to submit tasks to the queue in advance and only start executing them at a later time. You can do this using the member functions pause() , unpause() , and is_paused() .
However, these functions are disabled by default, and must be explicitly enabled by defining the macro BS_THREAD_POOL_ENABLE_PAUSE before including BS_thread_pool.hpp . The reason is that pausing the pool adds additional checks to the waiting and worker functions, which have a very small but non-zero overhead.
When you call pause() , the workers will temporarily stop retrieving new tasks out of the queue. However, any tasks already executed will keep running until they are done, since the thread pool has no control over the internal code of your tasks. If you need to pause a task in the middle of its execution, you must do that manually by programming your own pause mechanism into the task itself. To resume retrieving tasks, call unpause() . To check whether the pool is currently paused, call is_paused() .
Here is an example:
# define BS_THREAD_POOL_ENABLE_PAUSE
# include " BS_thread_pool.hpp " // BS::thread_pool
# include " BS_thread_pool_utils.hpp " // BS::synced_stream
# include < chrono > // std::chrono
# include < thread > // std::this_thread
BS::synced_stream sync_out;
BS::thread_pool pool ( 4 );
void sleep_half_second ( const int i)
{
std::this_thread::sleep_for ( std::chrono::milliseconds ( 500 ));
sync_out. println ( " Task " , i, " done. " );
}
void check_if_paused ()
{
if (pool. is_paused ())
sync_out. println ( " Pool paused. " );
else
sync_out. println ( " Pool unpaused. " );
}
int main ()
{
pool. detach_sequence ( 0 , 8 , sleep_half_second);
sync_out. println ( " Submitted 8 tasks. " );
std::this_thread::sleep_for ( std::chrono::milliseconds ( 250 ));
pool. pause ();
check_if_paused ();
std::this_thread::sleep_for ( std::chrono::milliseconds ( 1000 ));
sync_out. println ( " Still paused... " );
std::this_thread::sleep_for ( std::chrono::milliseconds ( 1000 ));
pool. detach_sequence ( 8 , 12 , sleep_half_second);
sync_out. println ( " Submitted 4 more tasks. " );
sync_out. println ( " Still paused... " );
std::this_thread::sleep_for ( std::chrono::milliseconds ( 1000 ));
pool. unpause ();
check_if_paused ();
}Assuming you have at least 4 hardware threads, the output should be similar to:
Submitted 8 tasks.
Pool paused.
Task 0 done.
Task 1 done.
Task 2 done.
Task 3 done.
Still paused...
Submitted 4 more tasks.
Still paused...
Pool unpaused.
Task 4 done.
Task 5 done.
Task 6 done.
Task 7 done.
Task 8 done.
Task 9 done.
Task 10 done.
Task 11 done.
Here is what happened. We initially submitted a total of 8 tasks to the queue. Since we waited for 250ms before pausing, the first 4 tasks have already started running, so they kept running until they finished. While the pool was paused, we submitted 4 more tasks to the queue, but they just waited at the end of the queue. When we unpaused, the remaining 4 initial tasks were executed, followed by the 4 new tasks.
While the workers are paused, wait() will wait for the running tasks instead of all tasks (otherwise it would wait forever). This is demonstrated by the following program:
# define BS_THREAD_POOL_ENABLE_PAUSE
# include " BS_thread_pool.hpp " // BS::thread_pool
# include " BS_thread_pool_utils.hpp " // BS::synced_stream
# include < chrono > // std::chrono
# include < thread > // std::this_thread
BS::synced_stream sync_out;
BS::thread_pool pool ( 4 );
void sleep_half_second ( const int i)
{
std::this_thread::sleep_for ( std::chrono::milliseconds ( 500 ));
sync_out. println ( " Task " , i, " done. " );
}
void check_if_paused ()
{
if (pool. is_paused ())
sync_out. println ( " Pool paused. " );
else
sync_out. println ( " Pool unpaused. " );
}
int main ()
{
pool. detach_sequence ( 0 , 8 , sleep_half_second);
sync_out. println ( " Submitted 8 tasks. Waiting for them to complete. " );
pool. wait ();
pool. detach_sequence ( 8 , 20 , sleep_half_second);
sync_out. println ( " Submitted 12 more tasks. " );
std::this_thread::sleep_for ( std::chrono::milliseconds ( 250 ));
pool. pause ();
check_if_paused ();
sync_out. println ( " Waiting for the " , pool. get_tasks_running (), " running tasks to complete. " );
pool. wait ();
sync_out. println ( " All running tasks completed. " , pool. get_tasks_queued (), " tasks still queued. " );
std::this_thread::sleep_for ( std::chrono::milliseconds ( 1000 ));
sync_out. println ( " Still paused... " );
std::this_thread::sleep_for ( std::chrono::milliseconds ( 1000 ));
sync_out. println ( " Still paused... " );
std::this_thread::sleep_for ( std::chrono::milliseconds ( 1000 ));
pool. unpause ();
check_if_paused ();
std::this_thread::sleep_for ( std::chrono::milliseconds ( 250 ));
sync_out. println ( " Waiting for the remaining " , pool. get_tasks_total (), " tasks ( " , pool. get_tasks_running (), " running and " , pool. get_tasks_queued (), " queued) to complete. " );
pool. wait ();
sync_out. println ( " All tasks completed. " );
}The output should be similar to:
Submitted 8 tasks. Waiting for them to complete.
Task 0 done.
Task 1 done.
Task 2 done.
Task 3 done.
Task 4 done.
Task 5 done.
Task 6 done.
Task 7 done.
Submitted 12 more tasks.
Pool paused.
Waiting for the 4 running tasks to complete.
Task 8 done.
Task 9 done.
Task 10 done.
Task 11 done.
All running tasks completed. 8 tasks still queued.
Still paused...
Still paused...
Pool unpaused.
Waiting for the remaining 8 tasks (4 running and 4 queued) to complete.
Task 12 done.
Task 13 done.
Task 14 done.
Task 15 done.
Task 16 done.
Task 17 done.
Task 18 done.
Task 19 done.
All tasks completed.
The first wait() , which was called while the pool was not paused, waited for all 8 tasks, both running and queued. The second wait() , which was called after pausing the pool, only waited for the 4 running tasks, while the other 8 tasks remained queued, and were not executed since the pool was paused. Finally, the third wait() , which was called after unpausing the pool, waited for the remaining 8 tasks, both running and queued.
Warning: If the thread pool is destroyed while paused, any tasks still in the queue will never be executed!
Consider the following program:
# include " BS_thread_pool.hpp " // BS::thread_pool
# include < iostream > // std::cout
int main ()
{
BS::thread_pool pool;
pool. detach_task (
[&pool]
{
pool. wait ();
std::cout << " Done waiting. n " ;
});
}This program creates a thread pool, and then detaches a task that waits for tasks in the same thread pool to complete. If you run this program, it will never print the message "Done waiting", because the task will wait for itself to complete. This causes a deadlock , and the program will wait forever.
Usually, in simple programs, this will never happen. However, in more complicated programs, perhaps ones running multiple thread pools in parallel, wait deadlocks could potentially occur. In such cases, the macro BS_THREAD_POOL_ENABLE_WAIT_DEADLOCK_CHECK can be defined before including BS_thread_pool.hpp . wait() will then check whether the user tried to call it from within a thread of the same pool, and if so, it will throw the exception BS::thread_pool::wait_deadlock instead of waiting. This check is disabled by default because wait deadlocks are not something that happens often, and the check adds a small but non-zero overhead every time wait() is called.
Here is an example:
# define BS_THREAD_POOL_ENABLE_WAIT_DEADLOCK_CHECK
# include " BS_thread_pool.hpp " // BS::thread_pool
# include < iostream > // std::cout
int main ()
{
BS::thread_pool pool;
pool. detach_task (
[&pool]
{
try
{
pool. wait ();
std::cout << " Done waiting. n " ;
}
catch ( const BS::thread_pool::wait_deadlock&)
{
std::cout << " Error: Deadlock! n " ;
}
});
} This time, wait() will detect the deadlock, and will throw an exception, causing the output to be "Error: Deadlock!" 。
Note that if the feature-test macro __cpp_exceptions is undefined, BS_THREAD_POOL_ENABLE_WAIT_DEADLOCK_CHECK will be automatically undefined.
The BS::thread_pool member function get_native_handles() returns a vector containing the underlying implementation-defined thread handles for each of the pool's threads. These can then be used in an implementation-specific way to manage the threads at the OS level
However, note that this will generally not be portable code. Furthermore, this feature uses std::thread::native_handle(), which is in the C++ standard library, but is not guaranteed to be present on all systems. Therefore, this feature is turned off by default, and must be turned on by defining the macro BS_THREAD_POOL_ENABLE_NATIVE_HANDLES before including BS_thread_pool.hpp .
Here is an example:
# define BS_THREAD_POOL_ENABLE_NATIVE_HANDLES
# include " BS_thread_pool.hpp " // BS::thread_pool
# include " BS_thread_pool_utils.hpp " // BS::synced_stream
# include < thread > // std::thread
# include < vector > // std::vector
BS::synced_stream sync_out;
BS::thread_pool pool ( 4 );
int main ()
{
std::vector<std::thread::native_handle_type> handles = pool. get_native_handles ();
for (BS:: concurrency_t i = 0 ; i < handles. size (); ++i)
sync_out. println ( " Thread " , i, " native handle: " , handles[i]);
}The output will depend on your compiler and operating system. Here is an example:
Thread 0 native handle: 000000F4
Thread 1 native handle: 000000F8
Thread 2 native handle: 000000EC
Thread 3 native handle: 000000FC
Defining the macro BS_THREAD_POOL_ENABLE_PRIORITY before including BS_thread_pool.hpp enables task priority. The priority of a task or group of tasks may then be specified as an additional argument (at the end of the argument list) to detach_task() , submit_task() , detach_blocks() , submit_blocks() , detach_loop() , submit_loop() , detach_sequence() , and submit_sequence() . If the priority is not specified, the default value will be 0.
The priority is a number of type BS::priority_t , which is a signed 16-bit integer, so it can have any value between -32,768 and 32,767. The tasks will be executed in priority order from highest to lowest. If priority is assigned to the block/loop/sequence parallelization functions, which submit multiple tasks, then all of these tasks will have the same priority.
The namespace BS::pr contains some pre-defined priorities for users who wish to avoid magic numbers and enjoy better future-proofing. In order of decreasing priority, the pre-defined priorities are: BS::pr::highest , BS::pr::high , BS::pr::normal , BS::pr::low , and BS::pr::lowest .
Here is a simple example:
# define BS_THREAD_POOL_ENABLE_PRIORITY
# include " BS_thread_pool.hpp " // BS::thread_pool
# include " BS_thread_pool_utils.hpp " // BS::synced_stream
BS::synced_stream sync_out;
BS::thread_pool pool ( 1 );
int main ()
{
pool. detach_task ([] { sync_out. println ( " This task will execute third. " ); }, BS::pr:: normal );
pool. detach_task ([] { sync_out. println ( " This task will execute fifth. " ); }, BS::pr::lowest);
pool. detach_task ([] { sync_out. println ( " This task will execute second. " ); }, BS::pr::high);
pool. detach_task ([] { sync_out. println ( " This task will execute first. " ); }, BS::pr::highest);
pool. detach_task ([] { sync_out. println ( " This task will execute fourth. " ); }, BS::pr::low);
}This program will print out the tasks in the correct priority order. Note that for simplicity, we used a pool with just one thread, so the tasks will run one at a time. In a pool with 5 or more threads, all 5 tasks will actually run more or less at the same time, because, for example, the task with the second-highest priority will be picked up by another thread while the task with the highest priority is still running.
Of course, this is just a pedagogical example. In a realistic use case we may want, for example, to submit tasks that must be completed immediately with high priority so they skip over other tasks already in the queue, or background non-urgent tasks with low priority so they evaluate only after higher-priority tasks are done.
Here are some subtleties to note when using task priority:
std::priority_queue , which has O(log n) complexity for storing new tasks, but only O(1) complexity for retrieving the next (ie highest-priority) task. This is in contrast with std::queue , used if priority is disabled, which both stores and retrieves with O(1) complexity.std::priority_queue as a binary heap, which means tasks are stored as a binary tree instead of sequentially. To execute tasks in submission order, give them monotonically decreasing priorities.BS::priority_t is defined to be ( std::int_least16_t ), since this type is guaranteed to be present on all systems, rather than std::int16_t , which is optional in the C++ standard. This means that on some exotic systems BS::priority_t may actually have more than 16 bits. However, the pre-defined priorities are 100% portable, and will always have the same values (eg: BS::pr::highest = 32767 ) regardless of the actual bit width. The file BS_thread_pool_test.cpp in the tests folder of the GitHub repository will perform automated tests of all aspects of the library. The output will be printed both to std::cout and to a file with the same name as the executable and the suffix -yyyy-mm-dd_hh.mm.ss.log based on the current date and time. In addition, the code is meant to serve as an extensive example of how to properly use the library.
Please make sure to:
BS_thread_pool_test.cpp with optimization flags enabled (eg -O3 on GCC / Clang or /O2 on MSVC).The test program also takes command line arguments for automation purposes:
help : Show a help message and exit. Any other arguments will be ignored.log : Create a log file.tests : Perform standard tests.deadlock Perform long deadlock tests.benchmarks : Perform benchmarks. If no options are entered, the default is: log tests benchmarks .
By default, the test program enables all the optional features by defining the suitable macros, so it can test them. However, if the macro BS_THREAD_POOL_LIGHT_TEST is defined during compilation, the optional features will not be tested.
A PowerShell script, BS_thread_pool_test.ps1 , is provided for your convenience in the tests folder to make running the test on multiple compilers and operating systems easier. Since it is written in PowerShell, it is fully portable and works on Windows, Linux, and macOS. The script will automatically detect if Clang, GCC, and/or MSVC are available, and compile the test program using each available compiler twice - with and without all the optional features. It will then run each compiled test program and report on any errors.
If any of the tests fail, please submit a bug report including the exact specifications of your system (OS, CPU, compiler, etc.) and the generated log file.
If all checks passed, BS_thread_pool_test.cpp performs simple benchmarks by filling a very large vector with values using detach_blocks() . The program decides what the size of the vector should be by testing how many elements are needed to reach a certain target duration when parallelizing using a number of blocks equal to the number of threads. This ensures that the test takes approximately the same amount of time on all systems, and is thus more consistent and portable.
Once the appropriate size of the vector has been determined, the program allocates the vector and fills it with values, calculated according to a fixed prescription. This operation is performed both single-threaded and multithreaded, with the multithreaded computation spread across multiple tasks submitted to the pool.
Several different multithreaded tests are performed, with the number of tasks either equal to, smaller than, or larger than the pool's thread count. Each test is repeated multiple times, with the run times averaged over all runs of the same test. The program keeps increasing the number of blocks by a factor of 2 until diminishing returns are encountered. The run times of the tests are compared, and the maximum speedup obtained is calculated.
As an example, here are the results of the benchmarks from a Digital Research Alliance of Canada node equipped with two 20-core / 40-thread Intel Xeon Gold 6148 CPUs (for a total of 40 cores and 80 threads), running CentOS Linux 7.9.2009. The tests were compiled using GCC v13.2.0 with the -O3 and -march=native flags. The output was as follows:
======================
Performing benchmarks:
======================
Using 80 threads.
Determining the number of elements to generate in order to achieve an approximate mean execution time of 50 ms with 80 tasks...
Each test will be repeated up to 30 times to collect reliable statistics.
Generating 27962000 elements:
[......]
Single-threaded, mean execution time was 2815.2 ms with standard deviation 3.5 ms.
[......]
With 2 tasks, mean execution time was 1431.3 ms with standard deviation 10.1 ms.
[.......]
With 4 tasks, mean execution time was 722.1 ms with standard deviation 11.4 ms.
[..............]
With 8 tasks, mean execution time was 364.9 ms with standard deviation 10.9 ms.
[............................]
With 16 tasks, mean execution time was 181.9 ms with standard deviation 8.0 ms.
[..............................]
With 32 tasks, mean execution time was 110.6 ms with standard deviation 1.8 ms.
[..............................]
With 64 tasks, mean execution time was 64.0 ms with standard deviation 6.3 ms.
[..............................]
With 128 tasks, mean execution time was 59.8 ms with standard deviation 0.8 ms.
[..............................]
With 256 tasks, mean execution time was 59.0 ms with standard deviation 0.0 ms.
[..............................]
With 512 tasks, mean execution time was 52.8 ms with standard deviation 0.4 ms.
[..............................]
With 1024 tasks, mean execution time was 50.7 ms with standard deviation 0.9 ms.
[..............................]
With 2048 tasks, mean execution time was 50.0 ms with standard deviation 0.5 ms.
[..............................]
With 4096 tasks, mean execution time was 49.4 ms with standard deviation 0.5 ms.
[..............................]
With 8192 tasks, mean execution time was 50.2 ms with standard deviation 0.4 ms.
Maximum speedup obtained by multithreading vs. single-threading: 56.9x, using 4096 tasks.
+++++++++++++++++++++++++++++++++++++++
Thread pool performance test completed!
+++++++++++++++++++++++++++++++++++++++
These two CPUs have 40 physical cores in total, with each core providing two separate logical cores via hyperthreading, for a total of 80 threads. Without hyperthreading, we would expect a maximum theoretical speedup of 40x. With hyperthreading, one might naively expect to achieve up to an 80x speedup, but this is in fact impossible, as each pair of hyperthreaded logical cores share the same physical core's resources. However, generally we would expect at most an estimated 30% additional speedup from hyperthreading, which amounts to around 52x in this case. The speedup of 56.9x in our performance test exceeds this estimate.
If you are using the vcpkg C/C++ package manager, you can easily install BS::thread_pool with the following commands:
On Linux/macOS:
./vcpkg install bshoshany-thread-pool
On Windows:
.vcpkg install bshoshany-thread-pool:x86-windows bshoshany-thread-pool:x64-windows
To update the package to the latest version, run:
vcpkg upgrade
If you are using the Conan C/C++ package manager, you can easily integrate BS::thread_pool into your project by adding the following lines to your conanfile.txt :
[requires]
bshoshany-thread-pool/4.1.0To update the package to the latest version, simply change the version number. Please refer to this package's page on ConanCenter for more information.
If you are using the Meson build system, you can install BS::thread_pool from WrapDB. To do so, create a subprojects folder in your project (if it does not already exist) and run the following command:
meson wrap install bshoshany-thread-pool
Then, use dependency('bshoshany-thread-pool') in your meson.build file to include the package. To update the package to the latest version, run:
meson wrap update bshoshany-thread-pool
If you are using CMake, you can install BS::thread_pool with CPM. If CPM is already installed, simply add the following to your project's CMakeLists.txt :
CPMAddPackage(
NAME BS_thread_pool
GITHUB_REPOSITORY bshoshany/thread-pool
VERSION 4.1.0)
add_library (BS_thread_pool INTERFACE )
target_include_directories (BS_thread_pool INTERFACE ${BS_thread_pool_SOURCE_DIR} / include )This will automatically download the indicated version of the package from the GitHub repository and include it in your project.
It is also possible to use CPM without installing it first, by adding the following lines to CMakeLists.txt before CPMAddPackage :
set (CPM_DOWNLOAD_LOCATION " ${CMAKE_BINARY_DIR} /cmake/CPM.cmake" )
if ( NOT ( EXISTS ${CPM_DOWNLOAD_LOCATION} ))
message ( STATUS "Downloading CPM.cmake" )
file (DOWNLOAD https://github.com/cpm-cmake/CPM.cmake/releases/latest/download/CPM.cmake ${CPM_DOWNLOAD_LOCATION} )
endif ()
include ( ${CPM_DOWNLOAD_LOCATION} ) Here is an example of a complete CMakeLists.txt for a project named my_project consisting of a single source file main.cpp which uses BS_thread_pool.hpp :
cmake_minimum_required ( VERSION 3.19)
project (my_project LANGUAGES CXX)
set (CMAKE_CXX_STANDARD 17)
set (CMAKE_CXX_STANDARD_REQUIRED ON )
set (CMAKE_CXX_EXTENSIONS OFF )
set (CPM_DOWNLOAD_LOCATION " ${CMAKE_BINARY_DIR} /cmake/CPM.cmake" )
if ( NOT ( EXISTS ${CPM_DOWNLOAD_LOCATION} ))
message ( STATUS "Downloading CPM.cmake" )
file (DOWNLOAD https://github.com/cpm-cmake/CPM.cmake/releases/latest/download/CPM.cmake ${CPM_DOWNLOAD_LOCATION} )
endif ()
include ( ${CPM_DOWNLOAD_LOCATION} )
CPMAddPackage(
NAME BS_thread_pool
GITHUB_REPOSITORY bshoshany/thread-pool
VERSION 4.1.0)
add_library (BS_thread_pool INTERFACE )
target_include_directories (BS_thread_pool INTERFACE ${BS_thread_pool_SOURCE_DIR} / include )
add_executable (my_project main.cpp)
target_link_libraries (my_project BS_thread_pool) With both CMakeLists.txt and main.cpp in the same folder, type the following commands to build the project:
cmake -S . -B build
cmake --build build
This section provides a complete reference to classes, member functions, objects, and macros available in this library, along with other important information. Member functions are given here with simplified prototypes (eg removing const ) for ease of reading.
More information can be found in the provided Doxygen comments. Any modern IDE, such as Visual Studio Code, can use the Doxygen comments to provide automatic documentation for any class and member function in this library when hovering over code with the mouse or using auto-complete.
BS_thread_pool.hpp ) BS::thread_pool class The class BS::thread_pool is the main thread pool class. It can be used to create a pool of threads and submit tasks to a queue. When a thread becomes available, it takes a task from the queue and executes it. The member functions that are available by default, when no macros are defined, are:
thread_pool() : Construct a new thread pool. The number of threads will be the total number of hardware threads available, as reported by the implementation. This is usually determined by the number of cores in the CPU. If a core is hyperthreaded, it will count as two threads.thread_pool(BS::concurrency_t num_threads) : Construct a new thread pool with the specified number of threads.thread_pool(std::function<void()>& init_task) : Construct a new thread pool with the specified initialization function.thread_pool(BS::concurrency_t num_threads, std::function<void()>& init_task) : Construct a new thread pool with the specified number of threads and initialization function.void reset() : Reset the pool with the total number of hardware threads available, as reported by the implementation. Waits for all currently running tasks to be completed, then destroys all threads in the pool and creates a new thread pool with the new number of threads. Any tasks that were waiting in the queue before the pool was reset will then be executed by the new threads. If the pool was paused before resetting it, the new pool will be paused as well.void reset(BS::concurrency_t num_threads) : Reset the pool with a new number of threads.void reset(std::function<void()>& init_task) Reset the pool with the total number of hardware threads available, as reported by the implementation, and a new initialization function.void reset(BS::concurrency_t num_threads, std::function<void()>& init_task) : Reset the pool with a new number of threads and a new initialization function.size_t get_tasks_queued() : Get the number of tasks currently waiting in the queue to be executed by the threads.size_t get_tasks_running() : Get the number of tasks currently being executed by the threads.size_t get_tasks_total() : Get the total number of unfinished tasks: either still waiting in the queue, or running in a thread. Note that get_tasks_total() == get_tasks_queued() + get_tasks_running() .BS::concurrency_t get_thread_count() : Get the number of threads in the pool.std::vector<std::thread::id> get_thread_ids() : Get a vector containing the unique identifiers for each of the pool's threads, as obtained by std::thread::get_id() .T and F are template parameters):void detach_task(F&& task) : Submit a function with no arguments and no return value into the task queue. To push a function with arguments, enclose it in a lambda expression. Does not return a future, so the user must use wait() or some other method to ensure that the task finishes executing, otherwise bad things will happen.void detach_blocks(T first_index, T index_after_last, F&& block, size_t num_blocks = 0) : Parallelize a loop by automatically splitting it into blocks and submitting each block separately to the queue. The block function takes two arguments, the start and end of the block, so that it is only called only once per block, but it is up to the user make sure the block function correctly deals with all the indices in each block. Does not return a BS::multi_future , so the user must use wait() or some other method to ensure that the loop finishes executing, otherwise bad things will happen.void detach_loop(T first_index, T index_after_last, F&& loop, size_t num_blocks = 0) : Parallelize a loop by automatically splitting it into blocks and submitting each block separately to the queue. The loop function takes one argument, the loop index, so that it is called many times per block. Does not return a BS::multi_future , so the user must use wait() or some other method to ensure that the loop finishes executing, otherwise bad things will happen.void detach_sequence(T first_index, T index_after_last, F&& sequence) : Submit a sequence of tasks enumerated by indices to the queue. Does not return a BS::multi_future , so the user must use wait() or some other method to ensure that the sequence finishes executing, otherwise bad things will happen.T , F , and R are template parameters):std::future<R> submit_task(F&& task) : Submit a function with no arguments into the task queue. To submit a function with arguments, enclose it in a lambda expression. If the function has a return value, get a future for the eventual returned value. If the function has no return value, get an std::future<void> which can be used to wait until the task finishes.BS::multi_future<R> submit_blocks(T first_index, T index_after_last, F&& block, size_t num_blocks = 0) : Parallelize a loop by automatically splitting it into blocks and submitting each block separately to the queue. The block function takes two arguments, the start and end of the block, so that it is only called only once per block, but it is up to the user make sure the block function correctly deals with all the indices in each block. Returns a BS::multi_future that contains the futures for all of the blocks.BS::multi_future<void> submit_loop(T first_index, T index_after_last, F&& loop, size_t num_blocks = 0) : Parallelize a loop by automatically splitting it into blocks and submitting each block separately to the queue. The loop function takes one argument, the loop index, so that it is called many times per block. It must have no return value. Returns a BS::multi_future that contains the futures for all of the blocks.BS::multi_future<R> submit_sequence(T first_index, T index_after_last, F&& sequence) : Submit a sequence of tasks enumerated by indices to the queue. Returns a BS::multi_future that contains the futures for all of the tasks.void purge() : Purge all the tasks waiting in the queue. Tasks that are currently running will not be affected, but any tasks still waiting in the queue will be discarded, and will never be executed by the threads. Please note that there is no way to restore the purged tasks.R and P , C , and D are template parameters):void wait() : Wait for tasks to be completed. Normally, this function waits for all tasks, both those that are currently running in the threads and those that are still waiting in the queue. However, if the pool is paused, this function only waits for the currently running tasks (otherwise it would wait forever). Note: To wait for just one specific task, use submit_task() instead, and call the wait() member function of the generated future.bool wait_for(std::chrono::duration<R, P>& duration) : Wait for tasks to be completed, but stop waiting after the specified duration has passed. Returns true if all tasks finished running, false if the duration expired but some tasks are still running.bool wait_until(std::chrono::time_point<C, D>& timeout_time) : Wait for tasks to be completed, but stop waiting after the specified time point has been reached. Returns true if all tasks finished running, false if the time point was reached but some tasks are still running. When a BS::thread_pool object goes out of scope, the destructor first waits for all tasks to complete, then destroys all threads. Note that if the pool is paused, then any tasks still in the queue will never be executed.
BS::thread_pool classThe thread pool has several optional features that must be explicitly enabled using macros.
BS_THREAD_POOL_ENABLE_PRIORITY .detach_task() , submit_task() , detach_blocks() , submit_blocks() , detach_loop() , submit_loop() , detach_sequence() , and submit_sequence() . If the priority is not specified, the default value will be 0.BS::priority_t , which is a signed 16-bit integer, so it can have any value between -32,768 and 32,767. The tasks will be executed in priority order from highest to lowest.BS::pr contains some pre-defined priorities: BS::pr::highest , BS::pr::high , BS::pr::normal , BS::pr::low , and BS::pr::lowest .BS_THREAD_POOL_ENABLE_PAUSE . Adds the following member functions:void pause() : Pause the pool. The workers will temporarily stop retrieving new tasks out of the queue, although any tasks already executed will keep running until they are finished.void unpause() : Unpause the pool. The workers will resume retrieving new tasks out of the queue.bool is_paused() : Check whether the pool is currently paused.BS_THREAD_POOL_ENABLE_NATIVE_HANDLES . Adds the following member function:std::vector<std::thread::native_handle_type> get_native_handles() : Get a vector containing the underlying implementation-defined thread handles for each of the pool's threads.BS_THREAD_POOL_ENABLE_WAIT_DEADLOCK_CHECK .wait() , wait_for() , and wait_until() will check whether the user tried to call them from within a thread of the same pool, which would result in a deadlock. If so, they will throw the exception BS::thread_pool::wait_deadlock instead of waiting.BS_THREAD_POOL_DISABLE_EXCEPTION_HANDLING .submit_task() if it is not needed, or if exceptions are explicitly disabled in the codebase.BS_THREAD_POOL_ENABLE_WAIT_DEADLOCK_CHECK . Disabling exception handling removes the try - catch block from submit_task() , while enabling wait deadlock checks adds a throw expression to wait() , wait_for() , and wait_until() .__cpp_exceptions is undefined, BS_THREAD_POOL_DISABLE_EXCEPTION_HANDLING is automatically defined, and BS_THREAD_POOL_ENABLE_WAIT_DEADLOCK_CHECK is automatically undefined. BS::this_thread namespace The namespace BS::this_thread provides functionality similar to std::this_thread . It contains the following function objects:
BS::this_thread::get_index() can be used to get the index of the current thread. If this thread belongs to a BS::thread_pool object, it will have an index from 0 to BS::thread_pool::get_thread_count() - 1 . Otherwise, for example if this thread is the main thread or an independent std::thread , std::nullopt will be returned.BS::this_thread::get_pool() can be used to get the pointer to the thread pool that owns the current thread. If this thread belongs to a BS::thread_pool object, a pointer to that object will be returned. Otherwise, std::nullopt will be returned.std::optional object will be returned, of type BS::this_thread::optional_index or BS::this_thread::optional_pool respectively. Unless you are 100% sure this thread is in a pool, first use std::optional::has_value() to check if it contains a value, and if so, use std::optional::value() to obtain that value. BS::multi_future<T> class BS::multi_future<T> is a helper class used to facilitate waiting for and/or getting the results of multiple futures at once. It is defined as a specialization of std::vector<std::future<T>> . This means that all of the member functions that can be used on an std::vector can also be used on a BS::multi_future . For example, you may use a range-based for loop with a BS::multi_future , since it has iterators.
In addition to inherited member functions, BS::multi_future has the following specialized member functions ( R and P , C , and D are template parameters):
[void or std::vector<T>] get() : Get the results from all the futures stored in this BS::multi_future , rethrowing any stored exceptions. If the futures return void , this function returns void as well. If the futures return a type T , this function returns a vector containing the results.size_t ready_count() : Check how many of the futures stored in this BS::multi_future are ready.bool valid() : Check if all the futures stored in this BS::multi_future are valid.void wait() : Wait for all the futures stored in this BS::multi_future .bool wait_for(std::chrono::duration<R, P>& duration) : Wait for all the futures stored in this BS::multi_future , but stop waiting after the specified duration has passed. Returns true if all futures have been waited for before the duration expired, false otherwise.bool wait_until(std::chrono::time_point<C, D>& timeout_time) : Wait for all the futures stored in this multi_future object, but stop waiting after the specified time point has been reached. Returns true if all futures have been waited for before the time point was reached, false otherwise.BS_thread_pool_utils.hpp ) BS::signaller class BS::signaller is a utility class which can be used to allow simple signalling between threads. This class is really just a convenient wrapper around std::promise , which contains both the promise and its future. It has the following member functions:
signaller() : Construct a new signaller.void wait() : Wait until the signaller is ready.void ready() : Inform any waiting threads that the signaller is ready. BS::synced_stream class BS::synced_stream is a utility class which can be used to synchronize printing to an output stream by different threads. It has the following member functions ( T is a template parameter pack):
synced_stream(std::ostream& stream = std::cout) : Construct a new synced stream which prints to the given output stream.void print(T&&... items) : Print any number of items into the output stream. Ensures that no other threads print to this stream simultaneously, as long as they all exclusively use the same synced_stream object to print.void println(T&&... items) : Print any number of items into the output stream, followed by a newline character.In addition, the class comes with two stream manipulators, which are meant to help the compiler figure out which template specializations to use with the class:
BS::synced_stream::endl : An explicit cast of std::endl . Prints a newline character to the stream, and then flushes it. Should only be used if flushing is desired, otherwise a newline character should be used instead.BS::synced_stream::flush : An explicit cast of std::flush . Used to flush the stream. BS::timer class BS::timer is a utility class which can be used to measure execution time for benchmarking purposes. It has the following member functions:
timer() : Construct a new timer and immediately start measuring time.void start() : Start (or restart) measuring time. Note that the timer starts ticking as soon as the object is created, so this is only necessary if we want to restart the clock later.void stop() : Stop measuring time and store the elapsed time since the object was constructed or since start() was last called.std::chrono::milliseconds::rep current_ms() : Get the number of milliseconds that have elapsed since the object was constructed or since start() was last called, but keep the timer ticking.std::chrono::milliseconds::rep ms() : Get the number of milliseconds stored when stop() was last called. This library is under continuous and active development. If you encounter any bugs, or if you would like to request any additional features, please feel free to open a new issue on GitHub and I will look into it as soon as I can.
Contributions are always welcome. However, I release my projects in cumulative updates after editing and testing them locally on my system, so my policy is not to accept any pull requests. If you open a pull request, and I decide to incorporate your suggestion into the project, I will first modify your code to comply with the project's coding conventions (formatting, syntax, naming, comments, programming practices, etc.), and perform some tests to ensure that the change doesn't break anything. I will then merge it into the next release of the project, possibly together with some other changes. The new release will also include a note in CHANGELOG.md with a link to your pull request, and modifications to the documentation in README.md as needed.
Many GitHub users have helped improve this project, directly or indirectly, via issues, pull requests, comments, and/or personal correspondence. Please see CHANGELOG.md for links to specific issues and pull requests that have been the most helpful. Thank you all for your contribution! :)
If you found this project useful, please consider starring it on GitHub! This allows me to see how many people are using my code, and motivates me to keep working to improve it.
Copyright (c) 2024 Barak Shoshany. Licensed under the MIT license.
If you use this C++ thread pool library in software of any kind, please provide a link to the GitHub repository in the source code and documentation.
If you use this library in published research, please cite it as follows:
You can use the following BibTeX entry:
@article { Shoshany2024_ThreadPool ,
archiveprefix = { arXiv } ,
author = { Barak Shoshany } ,
doi = { 10.1016/j.softx.2024.101687 } ,
eprint = { 2105.00613 } ,
journal = { SoftwareX } ,
pages = { 101687 } ,
title = { {A C++17 Thread Pool for High-Performance Scientific Computing} } ,
url = { https://www.sciencedirect.com/science/article/pii/S235271102400058X } ,
volume = { 26 } ,
year = { 2024 }
} Please note that the papers on SoftwareX and arXiv are not up to date with the latest version of the library. These publications are only intended to facilitate discovery of this library by scientists, and to enable citing it in scientific research. Documentation for the latest version is provided only by the README.md file in the GitHub repository.
Beginner C++ programmers may be interested in my lecture notes for a course taught at McMaster University, which teach modern C and C++ from scratch, including some of the advanced techniques and programming practices used in developing this library.
