thread pool
v4.1.0
BS::thread_pool : una biblioteca rápida, liviana y fácil de usar C ++ 17 Hild Pool Biblioteca Por Barak Shoshany
Correo electrónico: [email protected]
Sitio web: https://baraksh.com/
Github: https://github.com/bshoshany
Esta es la documentación completa para V4.1.0 de la biblioteca, lanzada el 2024-03-22.
BS::multi_future<T>BS::synced_streamBS::timerBS::signallerBS_thread_pool.hpp )BS::thread_poolBS::thread_poolBS::this_threadBS::multi_future<T>BS_thread_pool_utils.hpp )BS::signallerBS::synced_streamBS::timer La lectura múltiple es esencial para la computación moderna de alto rendimiento. Desde C ++ 11, la biblioteca estándar C ++ ha incluido soporte de lectura múltiple de bajo nivel incorporado utilizando construcciones como std::thread . Sin embargo, std::thread crea un nuevo hilo cada vez que se llama, que puede tener una sobrecarga de rendimiento significativa. Además, es posible crear más hilos de los que el hardware puede manejar simultáneamente, lo que puede provocar una desaceleración sustancial.
La biblioteca presentada aquí contiene una clase de grupo de hilos C ++, BS::thread_pool , que evita estos problemas creando un conjunto fijo de hilos de una vez por todas, y luego reutilizando continuamente los mismos hilos para realizar diferentes tareas a lo largo de la vida útil del programa. Por defecto, el número de subprocesos en el grupo es igual al número máximo de subprocesos que el hardware puede ejecutarse en paralelo.
El usuario envía tareas para ser ejecutadas en una cola. Cada vez que hay un hilo disponible, recupera la siguiente tarea de la cola y la ejecuta. El grupo produce automáticamente un std::future para cada tarea, lo que permite al usuario esperar a que la tarea termine de ejecutar y/u obtener su valor de retorno eventual, si corresponde. Los hilos y las tareas son administrados de forma autónoma por el grupo en segundo plano, sin requerir ninguna entrada del usuario además de enviar las tareas deseadas.
El diseño de esta biblioteca se guía por cuatro principios importantes. Primero, compacto : toda la biblioteca consta de solo un archivo de encabezado autónomo, sin otros componentes o dependencias, aparte de un pequeño archivo de encabezado autónomo con utilidades opcionales. Segundo, la portabilidad : la biblioteca solo utiliza la biblioteca estándar C ++ 17, sin confiar en ninguna extensión del compilador o bibliotecas de terceros, y por lo tanto es compatible con cualquier compilador C ++ 17 con conformación moderna en cualquier plataforma. Tercero, facilidad de uso : la biblioteca está ampliamente documentada, y los programadores de cualquier nivel deberían poder usarla de inmediato.
El cuarto y último principio guía es el rendimiento : cada línea de código en esta biblioteca se diseñó cuidadosamente con el máximo rendimiento en mente, y el rendimiento se probó y verificó en una variedad de compiladores y plataformas. De hecho, la biblioteca fue diseñada originalmente para su uso en los propios proyectos de computación científica computacionalmente intensiva del autor, que se ejecutan tanto en computadoras de escritorio/computadora portátil de alta gama como en nodos de computación de alto rendimiento.
Otras bibliotecas múltiples más avanzadas pueden ofrecer más funciones y/o mayor rendimiento. Sin embargo, generalmente consisten en una vasta base de código con múltiples componentes y dependencias, e involucran API complejas que requieren una inversión de tiempo sustancial para aprender. Esta biblioteca no está destinada a reemplazar estas bibliotecas más avanzadas; En cambio, fue diseñado para usuarios que no requieren características muy avanzadas, y prefieren una biblioteca simple y liviana que sea fácil de aprender y usar y se puede incorporar fácilmente en proyectos existentes o nuevos.
#include "BS_thread_pool.hpp" y ¡está todo listo!submit_task() genera automáticamente un std::future , que puede usarse para esperar a que la tarea termine de ejecutar y/u obtener su valor de retorno eventual.submit_loop() , que devuelve un BS::multi_future que se puede usar para rastrear la ejecución de todas las tareas paralelas a la vez.detach_task() , y los bucles pueden ser paralelizados usando detach_loop() , sacrificando la conveniencia para un rendimiento aún mayor. En ese caso, wait() , wait_for() y wait_until() se puede usar para esperar todas las tareas en la cola para completar.BS_thread_pool_test.cpp se puede usar para realizar pruebas y puntos de referencia automatizados exhaustivos, y también sirve como un ejemplo completo de cómo usar correctamente la biblioteca. El script de PowerShell incluido BS_thread_pool_test.ps1 proporciona una forma portátil de ejecutar las pruebas con múltiples compiladores.BS_thread_pool_utils.hpp contiene varias clases de utilidad útiles.BS::signaller .BS::synced_stream .BS::timer .detach_sequence() y submit_sequence() .reset() .get_tasks_queued() , get_tasks_running() y get_tasks_total() .get_thread_count() .pause() , unpause() e is_paused() ; Cuando se detiene, los hilos no recuperan nuevas tareas de la cola.purge() .submit_task() o submit_loop() desde el hilo principal a través de sus futuros.BS::this_thread::get_index() y un puntero al grupo que posee el hilo usando BS::this_thread::get_pool() .get_thread_ids() o los manijas de hilo definidos por implementación utilizando la función miembro opcional get_native_handles() .Esta biblioteca debe compilarse con éxito en cualquier compilador que cumpla con el estándar C ++ 17, en todos los sistemas operativos y arquitecturas para los cuales dicho compilador está disponible. La compatibilidad se verificó con una CPU Intel I9-13900K de 24 núcleos (8p+16e) / 32:
Además, esta biblioteca se probó en una alianza de investigación digital de nodo de Canadá equipado con dos CPU Intel Xeon Gold 6148 de 20 /40 /40 coro 6148 (para un total de 40 núcleos y 80 hilos), ejecutando CentOS Linux 7.9.2009, utilizando GCC V13.2.0.
El programa de prueba BS_thread_pool_test.cpp se compiló sin advertencias (con las banderas de advertencia -Wall -Wextra -Wconversion -Wsign-conversion -Wpedantic -Weffc++ -Wshadow en GCC /Clang y /W4 en MSVC), ejecutados y ejecutados con éxito con éxito y contenido de comparación y contenido de contenido de contenido de los sistemas anteriores.
Como esta biblioteca requiere características de C ++ 17, el código debe compilarse con soporte C ++ 17:
-std=c++17 . En Linux, también necesitará usar el indicador -pthread para habilitar la biblioteca POSIX Threads./std:c++17 , y también /permissive- para garantizar la conformidad de los estándares.Para el máximo rendimiento, se recomienda compilar con todas las optimizaciones de compiladores disponibles:
-O3 ./O2 . Como ejemplo, para compilar el programa de prueba BS_thread_pool_test.cpp con advertencias y optimizaciones, se recomienda usar los siguientes comandos:
g++ BS_thread_pool_test.cpp -std=c++17 -O3 -Wall -Wextra -Wconversion -Wsign-conversion -Wpedantic -Weffc++ -Wshadow -pthread -o BS_thread_pool_testg++ con clang++ .-o BS_thread_pool_test con -o BS_thread_pool_test.exe y elimine -pthread .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 Para instalar BS::thread_pool , simplemente descargue la última versión desde el repositorio de GitHub, coloque el archivo de encabezado BS_thread_pool.hpp en la carpeta include en la carpeta deseada e incluya en su programa:
# include " BS_thread_pool.hpp " El grupo de subprocesos ahora se puede acceder a través de la clase BS::thread_pool . Para una instalación aún más rápida, puede descargar el archivo de encabezado en sí mismo directamente en esta URL.
Esta biblioteca también viene con un archivo de encabezado de utilidades independientes BS_thread_pool_utils.hpp , que no es necesario para usar el grupo de subprocesos, pero proporciona algunas clases de utilidad que pueden ser útiles para la multitud de lectura múltiple. Este archivo de encabezado también reside en la carpeta include . Se puede descargar directamente en esta URL.
Esta biblioteca también está disponible en varios administradores de paquetes y sistema de compilación, incluidos VCPKG, Conan, Meson y CMake con CPM. Consulte a continuación para obtener más detalles.
El constructor predeterminado crea un grupo de subprocesos con tantos hilos como el hardware puede manejar simultáneamente, según lo informado por la implementación a través de std::thread::hardware_concurrency() . Esto generalmente se determina por el número de núcleos en la CPU. Si un núcleo es hiperThreaded, contará como dos hilos. Por ejemplo:
// Constructs a thread pool with as many threads as available in the hardware.
BS::thread_pool pool;Opcionalmente, varios hilos diferentes de la concurrencia de hardware se pueden especificar como un argumento para el constructor. Sin embargo, tenga en cuenta que agregar más hilos de los que el hardware puede manejar no mejorará el rendimiento y, de hecho, probablemente lo obstaculizará. Esta opción existe para permitir el uso de menos hilos que la concurrencia de hardware, en los casos en que desea dejar algunos hilos disponibles para otros procesos. Por ejemplo:
// Constructs a thread pool with only 12 threads.
BS::thread_pool pool ( 12 );Por lo general, cuando se usa el grupo de subprocesos, el hilo principal de un programa solo debe enviar tareas al grupo de subprocesos y esperar a que terminen, y no debe realizar ninguna tarea computacionalmente intensiva por sí solo. En ese caso, se recomienda usar el valor predeterminado para el número de subprocesos. Esto asegura que todos los hilos disponibles en el hardware se pongan a funcionar mientras el hilo principal espera.
La función miembro get_thread_count() devuelve el número de hilos en el grupo. Esto será igual a std::thread::hardware_concurrency() si se usó el constructor predeterminado.
En general, es innecesario cambiar el número de hilos en la piscina después de haber sido creado, ya que todo el punto de un grupo de hilos es que solo crea los hilos una vez. Sin embargo, si es necesario, esto se puede hacer, de forma segura y sobre la marcha, utilizando la función de miembro reset() .
reset() esperará a que se completen todas las tareas actualmente en ejecución, pero dejará el resto de las tareas en la cola. Luego destruirá el grupo de hilos y creará uno nuevo con el nuevo número deseado de hilos, como se especifica en el argumento de la función (o la concurrencia de hardware si no se da ningún argumento). El nuevo grupo de subprocesos reanudará la ejecución de las tareas que permanecieron en la cola y en cualquier tarea nueva enviada.
Si lo desea, la versión de esta biblioteca puede leerse durante el tiempo de compilación de las siguientes tres macros:
BS_THREAD_POOL_VERSION_MAJOR - indica la versión principal.BS_THREAD_POOL_VERSION_MINOR - Indica la versión menor.BS_THREAD_POOL_VERSION_PATCH : indica la versión de parche. std::cout << " Thread pool library version is " << BS_THREAD_POOL_VERSION_MAJOR << ' . ' << BS_THREAD_POOL_VERSION_MINOR << ' . ' << BS_THREAD_POOL_VERSION_PATCH << " . n " ;Salida de muestra:
Thread pool library version is 4.1.0.
Esto se puede usar, por ejemplo, para permitir que la misma base de código funcione con varias versiones incompatibles de la biblioteca utilizando las directivas #if .
Nota: Esta característica solo está disponible a partir de V4.0.1. Las versiones anteriores de esta biblioteca no definen estas macros.
En esta sección aprenderemos cómo enviar una tarea sin argumentos, pero potencialmente con un valor de devolución, a la cola. Una vez que se haya enviado una tarea, se ejecutará tan pronto como esté disponible un hilo. Las tareas se ejecutan en el orden en que se presentaron (primero en, primera vez), a menos que la prioridad de la tarea esté habilitada (ver más abajo).
Por ejemplo, si el grupo tiene 8 hilos y una cola vacía, y presentamos 16 tareas, entonces debemos esperar que las primeras 8 tareas se ejecuten en paralelo, con las tareas restantes que los subprocesos son una por una a medida que cada hilo finaliza la ejecución de su primera tarea, hasta que no queden tareas en la cola.
La función miembro submit_task() se utiliza para enviar tareas a la cola. Se necesita exactamente una entrada, la tarea para enviar. Esta tarea debe ser una función sin argumentos, pero puede tener un valor de retorno. El valor de retorno es un std::future asociado a la tarea.
Si la función enviada tiene un valor de retorno del tipo T , el futuro será de tipo std::future<T> , y se establecerá en el valor de retorno cuando la función termine su ejecución. Si la función enviada no tiene un valor de retorno, entonces el futuro será un std::future<void> , que no devolverá ningún valor, pero aún se puede usar para esperar a que la función finalice.
Usar auto para el valor de retorno de submit_task() significa que el compilador detectará automáticamente qué instancia de la plantilla std::future para usar. Sin embargo, se recomienda especificar el tipo particular std::future<T> , como en los ejemplos a continuación, para una mayor legibilidad.
Para esperar hasta que finalice la tarea, use la función miembro wait() del futuro. Para obtener el valor de retorno, use la función miembro get() , que también esperará automáticamente a que la tarea finalice si aún no lo ha hecho. Aquí hay un ejemplo simple:
# 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 ' ;
} En este ejemplo, enviamos la función the_answer() , que devuelve un int . La función miembro submit_task() del grupo, por lo tanto, devolvió un std::future<int> . Luego usamos la función de miembro get() del futuro para obtener el valor de retorno y la imprimimos.
Además de presentar una función predefinida, también podemos usar una expresión de lambda para definir rápidamente la tarea sobre la marcha. Reescribiendo el ejemplo anterior en términos de una expresión de lambda, obtenemos:
# 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 ' ;
} Aquí, la expresión lambda []{ return 42; } tiene dos partes:
[] . Esto significa al compilador que se está definiendo una expresión de lambda.{ return 42; } que simplemente devuelve el valor 42 .En general, es más simple y más rápido enviar expresiones lambda en lugar de funciones predefinidas, especialmente debido a la capacidad de capturar variables locales, que discutiremos en la siguiente sección.
Por supuesto, las tareas no tienen que devolver los valores. En el siguiente ejemplo, enviamos una función sin valor de retorno y luego utilizando el futuro para esperar a que termine de ejecutar:
# 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 ' ;
} Aquí dividimos el Lambda en múltiples líneas para que sea más legible. El comando std::this_thread::sleep_for(std::chrono::milliseconds(500)) instruye a la tarea a simplemente dormir durante 500 milisegundos, simulando una tarea computacionalmente intensiva.
Como se indica en la sección anterior, las tareas enviadas usando submit_task() no pueden tener ningún argumento. Sin embargo, es fácil enviar tareas con argumentos, ya sea envolviendo la función en una lambda o usando capturas lambda directamente. Aquí hay dos ejemplos.
El siguiente es un ejemplo de enviar una función predefinida con argumentos envolviéndola con una lambda:
# 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 ' ;
} Como puede ver, para pasar los argumentos para multiply que simplemente llamamos multiply(6, 7) explícitamente dentro de una lambda. Si los argumentos no son literales, necesitamos usar la cláusula de captura Lambda para capturar los argumentos del alcance local:
# 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 ' ;
} Incluso podríamos deshacernos de la función multiply por completo y poner todo dentro de una lambda, si lo desea:
# 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 ' ;
} Por lo general, es mejor enviar una tarea a la cola usando submit_task() . Esto le permite esperar a que la tarea finalice y/o obtenga su valor de retorno más tarde. Sin embargo, a veces no se necesita un futuro, por ejemplo, cuando solo desea "establecer y olvidar" una determinada tarea, o si la tarea ya se comunica con el hilo principal o con otras tareas sin usar futuros, como las variables de condición.
En tales casos, es posible que desee evitar los gastos generales involucrados en la asignación de un futuro a la tarea para aumentar el rendimiento. Esto se llama "separar" la tarea, ya que la tarea se separa del hilo principal y se ejecuta de forma independiente.
Las tareas de separación se realizan utilizando la función miembro detach_task() , que le permite separar una tarea a la cola sin generar un futuro para ella. La tarea puede tener cualquier número de argumentos, pero no puede tener un valor de retorno, ya que no habría forma de que el hilo principal recupere ese valor.
Dado que detach_task() no devuelve un futuro, no hay una forma incorporada para que el usuario sepa cuándo la tarea termina de ejecutarse. Debe asegurarse manualmente de que la tarea finalice la ejecución antes de tratar de usar cualquier cosa que dependa de su salida. De lo contrario, ¡sucederán cosas malas!
BS::thread_pool proporciona la función miembro wait() para facilitar la espera de todas las tareas en la cola para completar, ya sea que estuvieran separadas o presentadas con un futuro. La función de miembro wait() funciona de manera similar a la función de miembro wait() de std::future . Considere, por ejemplo, el siguiente código:
# 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 ' ;
} Este programa primero define una variable local llamada result y la inicializa a 0 . Luego separa una tarea en forma de expresión de lambda. Tenga en cuenta que la lambda captura result por referencia , como lo indica & frente a él. Esto significa que la tarea puede modificar result , y cualquier modificación de este tipo se reflejará en el hilo principal. Los cambios de la tarea result 42 , pero primero duerme durante 100 milisegundos. Cuando el hilo principal imprime el valor del result , la tarea aún no ha tenido tiempo de modificar su valor, ya que todavía está durmiendo. Por lo tanto, el programa imprimirá el valor inicial 0 .
Para esperar a que la tarea se complete, debemos usar la función de miembro wait() después de separarla:
# 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 ' ;
} Ahora el programa imprimirá el valor 42 , como se esperaba. Tenga en cuenta, sin embargo, que wait() esperará todas las tareas en la cola, incluidas cualquier otra tarea que se haya enviado antes o después de la que nos importa. Si queremos esperar una sola tarea, submit_task() sería una mejor opción.
A veces es posible que desee esperar a que las tareas se completen, pero solo por un cierto período de tiempo, o hasta un punto específico en el tiempo. Por ejemplo, si las tareas aún no se han completado después de un tiempo, es posible que desee informar al usuario que hay un retraso.
Para la tarea enviada con futuros usando submit_task() , esto se puede lograr utilizando dos funciones miembros de std::future :
wait_for() espera a que se complete la tarea, pero deja de esperar después de la duración especificada, dada como un argumento de tipo std::chrono::duration , ha pasado.wait_until() espera a que se complete la tarea, pero deja de esperar después del punto de tiempo especificado, dado como un argumento de tipo std::chrono::time_point , se ha alcanzado. En ambos casos, las funciones devolverán future_status::ready si el futuro está listo, lo que significa que la tarea está terminada y su valor de retorno, si corresponde, se ha obtenido. Sin embargo, devolverá std::future_status::timeout si el futuro aún no está listo para cuando haya expirado el tiempo de espera.
Aquí hay un ejemplo:
# 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 ;
}
}La salida debe parecer similar a esto:
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!
Para tareas separadas, ya que no tenemos un futuro para ellas, no podemos usar este método. Sin embargo, BS::thread_pool tiene dos funciones miembros, también nombradas wait_for() y wait_until() , que esperan de manera similar una duración especificada o hasta un punto de tiempo especificado, pero hágalo para todas las tareas (ya sea enviadas o separadas). En lugar de un std::future_status , las funciones de espera del grupo de subprocesos devuelven true si todas las tareas terminan de ejecutarse, o false si la duración expiró o se alcanzó el tiempo, pero algunas tareas aún se están ejecutando.
Aquí está el mismo ejemplo que el anterior, usando detach_task() y pool.wait_for() ::
# 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 ;
}
}Consideremos el siguiente programa:
# 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 ' ;
} Este programa crea un nuevo objeto flag_object de la clase flag_class , establece el indicador en true usando la función de miembro setter set_flag() y luego imprime el valor de la bandera usando la función de miembro getter get_flag() .
¿Qué pasa si queremos enviar la función miembro set_flag() como una tarea para el grupo de subprocesos? Simplemente envolvemos toda la instrucción flag_object.set_flag(true); desde la línea en un lambda y pase flag_object a la lambda por referencia, como en este ejemplo:
# 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 ' ;
} Por supuesto, esto también funcionará con detach_task() , si llamamos wait() en el grupo en lugar de en el futuro devuelto.
Tenga en cuenta que en este ejemplo, en lugar de obtener un futuro de submit_task() y luego esperando ese futuro, simplemente llamamos wait() en ese futuro de inmediato. Esta es una forma común de esperar a que se complete una tarea si no tenemos nada más que hacer mientras tanto. Tenga en cuenta también que pasamos flag_object por referencia a la Lambda, ya que queremos establecer el indicador en ese mismo objeto, no una copia de él (pasar por valor no habría funcionado de todos modos, ya que las variables capturadas por valor son implícitamente const ).
Otra cosa que puede hacer es llamar a una función de miembro desde el propio objeto, es decir, de otra función de miembro. Esto sigue una sintaxis similar, excepto que también debe capturar this (es decir, un puntero al objeto actual) en la lambda. Aquí hay un ejemplo:
# 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 ' ;
} Tenga en cuenta que en este ejemplo definimos el grupo de subprocesos como un objeto global, de modo que sea accesible fuera de la función main() .
Uno de los métodos de paralelización más comunes y efectivos es dividir un bucle en bucles más pequeños y ejecutarlos en paralelo. Es más efectivo en los cálculos "vergonzosamente paralelos", como las operaciones vectoriales o de matriz, donde cada iteración del bucle es completamente independiente de cualquier otra iteración.
Por ejemplo, si estamos resumiendo dos vectores de 1000 elementos cada uno, y tenemos 10 hilos, podríamos dividir la suma en 10 bloques de 100 elementos cada uno, y ejecutar todos los bloques en paralelo, potencialmente aumentando un rendimiento en un factor de 10.
BS::thread_pool puede paralelizar automáticamente los bucles. Para ver cómo funciona esto, considere el siguiente bucle genérico:
for (T i = start; i < end; ++i)
loop (i);dónde:
T es cualquier tipo de entero firmado o sin firmar.[start, end) , es decir, inclusive el start pero exclusivo del end .loop() es una operación realizada para cada índice de bucle i , como modificar una matriz con elementos end - start . Este bucle se puede paralelizar automáticamente y enviarse a la cola del grupo de subprocesos utilizando la función miembro submit_loop() , que tiene la siguiente sintaxis:
pool.submit_loop(start, end, loop, num_blocks);dónde:
start es el primer índice en el rango.end es el índice después del último índice en el rango, de modo que el rango completo es [start, end) . En otras palabras, el bucle será equivalente al anterior si start y end son los mismos.start and end debe ser del mismo tipo entero T Vea a continuación ejemplos de qué hacer cuando no son del mismo tipo.end <= start , no sucederá nada.loop() es la función que debe ejecutarse en cada iteración del bucle, y toma un argumento, el índice de bucle.num_blocks es el número de bloques de la forma [a, b) para dividir el bucle en. Por ejemplo, si el rango es [0, 9) y hay 3 bloques, entonces los bloques serán los rangos [0, 3) , [3, 6) y [6, 9) .[0, 100) se divide en 15 bloques, el resultado será de 10 bloques de tamaño 7, que se ejecutará primero y 5 bloques de tamaño 6.Cada bloque se enviará a la cola del grupo de subprocesos como una tarea separada. Por lo tanto, un bucle que se divide en 3 bloques se dividirá en 3 tareas individuales, que pueden ejecutarse en paralelo. Si solo hay un bloque, entonces todo el bucle se ejecutará como una tarea, y no se producirá paralelización.
Para paralelizar el bucle genérico anterior, usamos los siguientes comandos:
BS::multi_future< void > loop_future = pool.submit_loop(start, end, loop, num_blocks);
loop_future.wait(); submit_loop() Devuelve un objeto de la plantilla de clase ayudante BS::multi_future . Esto es esencialmente una especialización de std::vector<std::future<T>> con funciones de miembros adicionales. Cada uno de los bloques num_blocks tendrá un std::future asignado a él, y todos estos futuros se almacenarán dentro del BS::multi_future devuelto. Cuando se llama a loop_future.wait() , el hilo principal esperará hasta que todas las tareas generadas por submit_loop() terminen de ejecutar, y solo esas tareas, no cualquier otra tarea que también esté en la cola. Este es esencialmente el papel de la clase BS::multi_future : esperar un grupo específico de tareas , en este caso las tareas que ejecutan los bloques de bucle.
¿Qué valor debe usar para num_blocks ? Omitir este argumento, para que el número de bloques sea igual al número de hilos en el grupo, es típicamente una buena opción. Para el mejor rendimiento, se recomienda hacer sus propios puntos de referencia para encontrar el número óptimo de bloques para cada bucle (puede usar la clase de utilidad BS::timer ). Se puede preferir el uso de menos tareas de las que hay hilos si también está ejecutando otras tareas en paralelo. El uso de más tareas de los que hay hilos pueden mejorar el rendimiento en algunos casos, pero la paralelización con demasiadas tareas sufrirá rendimientos decrecientes.
Como ejemplo simple, el siguiente código calcula e imprime una tabla de cuadrados de todos los enteros de 0 a 99:
# 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 " );
}Podemos paralelizarlo de la siguiente manera:
# 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 " );
} Dado que hay 10 hilos, y omitimos el argumento num_blocks , el bucle se dividirá en 10 bloques, cada uno calculando 10 cuadrados.
Tenga en cuenta que submit_loop() se ejecutó con el parámetro de plantilla explícita <unsigned int> . La razón es que los dos índices de bucle deben ser del mismo tipo. Sin embargo, aquí max es un unsigned int , mientras que 0 es un int , por lo que los tipos no coinciden, y el código no se compilará a menos que obligemos a los 0 a ser del tipo correcto. Esto se puede hacer más elegante especificando el tipo de índices explícitamente utilizando el parámetro de plantilla.
La razón por la que esto no se hace automáticamente (por ejemplo, usando std::common_type es que puede dar como resultado un lanza accidentalmente índices negativos a un tipo sin firmar o índices enteros a un tipo entero demasiado estrecho, lo que puede conducir a un rango de bucle incorrecto.
También podríamos lanzar el 0 explícitamente a Unsigned int, pero eso no se ve tan bien:
pool.submit_loop( static_cast < unsigned int >( 0 ), max, /* ... */ );O podríamos usar un elenco de estilo C:
pool.submit_loop(( unsigned int )( 0 ), max, /* ... */ );O podríamos usar un sufijo literal entero:
pool.submit_loop< size_t >( 0U , max, ...);Como nota al margen, observe que aquí es paralelo al cálculo de los cuadrados, pero no paralelizamos la impresión de los resultados. Esto es por dos razones:
Al igual que en el caso de detach_task() vs. submit_task() , a veces es posible que desee paralelizar un bucle, pero no necesita que devuelva un BS::multi_future . En este caso, puede guardar la sobrecarga de generar los futuros (que pueden ser significativos, dependiendo del número de bloques) mediante el uso de detach_loop() en lugar de submit_loop() , con los mismos argumentos.
Por ejemplo, podríamos separar el bucle de cuadrados de cuadrados arriba de la siguiente manera:
# 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.Por ejemplo:
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. Vea a continuación para obtener más detalles.
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. Por ejemplo:
# 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.
