thread pool
v4.1.0
BS::thread_pool : Perpustakaan C ++ 17 Thread Pool yang cepat, ringan, dan mudah digunakan Oleh Barak Shoshany
Email: [email protected]
Situs web: https://baraksh.com/
GitHub: https://github.com/bshoshany
Ini adalah dokumentasi lengkap untuk v4.1.0 dari perpustakaan, yang dirilis pada 2024-03-22.
BS::multi_future<T>BS::synced_streamBS::timerBS::signallerBS_thread_pool.hpp )BS::thread_poolBS::thread_poolBS::this_thread namespaceBS::multi_future<T>BS_thread_pool_utils.hpp )BS::signallerBS::synced_streamBS::timer Multithreading sangat penting untuk komputasi kinerja tinggi modern. Sejak C ++ 11, Perpustakaan Standar C ++ telah memasukkan dukungan multithreading tingkat rendah bawaan menggunakan konstruksi seperti std::thread . Namun, std::thread membuat utas baru setiap kali disebut, yang dapat memiliki overhead kinerja yang signifikan. Selain itu, dimungkinkan untuk membuat lebih banyak utas daripada perangkat keras yang dapat menangani secara bersamaan, berpotensi menghasilkan perlambatan yang substansial.
Perpustakaan yang disajikan di sini berisi kelas kumpulan utas C ++, BS::thread_pool , yang menghindari masalah ini dengan membuat kumpulan utas yang tetap sekali dan untuk semua, dan kemudian terus menggunakan kembali utas yang sama untuk melakukan tugas yang berbeda sepanjang masa pakai program. Secara default, jumlah utas di kumpulan sama dengan jumlah maksimum utas yang dapat dijalankan perangkat keras secara paralel.
Pengguna mengirimkan tugas yang akan dieksekusi ke dalam antrian. Setiap kali utas tersedia, ia mengambil tugas berikutnya dari antrian dan mengeksekusinya. Kumpulan secara otomatis menghasilkan std::future untuk setiap tugas, yang memungkinkan pengguna menunggu tugas menyelesaikan pelaksanaan dan/atau mendapatkan nilai pengembalian akhirnya, jika berlaku. Utas dan tugas dikelola secara mandiri oleh kumpulan di latar belakang, tanpa memerlukan input dari pengguna selain dari mengirimkan tugas yang diinginkan.
Desain perpustakaan ini dipandu oleh empat prinsip penting. Pertama, kekompakan : seluruh perpustakaan hanya terdiri dari satu file header mandiri, tanpa komponen atau dependensi lain, selain dari file header mandiri kecil dengan utilitas opsional. Kedua, portabilitas : Perpustakaan hanya menggunakan perpustakaan standar C ++ 17, tanpa mengandalkan ekstensi kompiler apa pun atau perpustakaan pihak ke-3, dan karenanya kompatibel dengan kompiler C ++ 17 yang memenuhi standar modern pada platform apa pun. Ketiga, kemudahan penggunaan : Perpustakaan didokumentasikan secara luas, dan programmer dari level apa pun harus dapat menggunakannya langsung dari kotak.
Prinsip panduan keempat dan terakhir adalah kinerja : masing -masing dan setiap baris kode di perpustakaan ini dirancang dengan hati -hati dengan kinerja maksimum dalam pikiran, dan kinerja diuji dan diverifikasi pada berbagai kompiler dan platform. Memang, perpustakaan awalnya dirancang untuk digunakan dalam proyek komputasi ilmiah yang intensif komputasi penulis sendiri, berjalan baik pada komputer desktop/laptop kelas atas dan node komputasi kinerja tinggi.
Perpustakaan multithreading lainnya yang lebih canggih dapat menawarkan lebih banyak fitur dan/atau kinerja yang lebih tinggi. Namun, mereka biasanya terdiri dari basis kode yang luas dengan banyak komponen dan ketergantungan, dan melibatkan API kompleks yang membutuhkan investasi waktu yang substansial untuk dipelajari. Perpustakaan ini tidak dimaksudkan untuk menggantikan perpustakaan yang lebih canggih ini; Sebaliknya, itu dirancang untuk pengguna yang tidak memerlukan fitur yang sangat canggih, dan lebih suka perpustakaan sederhana dan ringan yang mudah dipelajari dan digunakan dan dapat dengan mudah dimasukkan ke dalam proyek yang ada atau baru.
#include "BS_thread_pool.hpp" dan Anda siap!submit_task() secara otomatis menghasilkan std::future , yang dapat digunakan untuk menunggu tugas menyelesaikan pelaksanaan dan/atau mendapatkan nilai pengembalian akhirnya.submit_loop() , yang mengembalikan BS::multi_future yang dapat digunakan untuk melacak pelaksanaan semua tugas paralel sekaligus.detach_task() , dan loop dapat diparalelkan menggunakan detach_loop() - mengorbankan kenyamanan untuk kinerja yang lebih besar. Dalam hal ini, wait() , wait_for() , dan wait_until() dapat digunakan untuk menunggu semua tugas dalam antrian selesai.BS_thread_pool_test.cpp dapat digunakan untuk melakukan tes dan tolok ukur otomatis yang lengkap, dan juga berfungsi sebagai contoh komprehensif tentang cara menggunakan perpustakaan dengan benar. Skrip PowerShell yang disertakan BS_thread_pool_test.ps1 menyediakan cara portabel untuk menjalankan tes dengan beberapa kompiler.BS_thread_pool_utils.hpp berisi beberapa kelas utilitas yang berguna.BS::signaller .BS::synced_stream .BS::timer .detach_sequence() dan submit_sequence() .reset() .get_tasks_queued() , get_tasks_running() , dan get_tasks_total() fungsi anggota.get_thread_count() .pause() , unpause() , dan is_paused() ; Saat dijeda, utas tidak mengambil tugas baru dari antrian.purge() .submit_task() atau submit_loop() dari utas utama melalui masa depan mereka.BS::this_thread::get_index() dan pointer ke kolam yang memiliki utas menggunakan BS::this_thread::get_pool() .get_thread_ids() atau pegangan utas yang ditentukan implementasi menggunakan fungsi anggota get_native_handles() opsional.Perpustakaan ini harus berhasil dikompilasi pada kompiler C ++ 17 yang sesuai dengan standar, pada semua sistem operasi dan arsitektur yang tersedia kompiler tersebut. Kompatibilitas diverifikasi dengan 24-core (8p+16e) / 32-thread Intel I9-13900K CPU menggunakan kompiler dan platform berikut:
Selain itu, perpustakaan ini diuji pada aliansi penelitian digital dari node Kanada yang dilengkapi dengan dua CPU 20-core / 40-thread Intel Xeon Gold 6148 (untuk total 40 core dan 80 utas), menjalankan Centos Linux 7.9.2009, menggunakan GCC V13.2.0.
Program pengujian BS_thread_pool_test.cpp dikompilasi tanpa peringatan (dengan bendera peringatan -Wall -Wextra -Wconversion -Wsign-conversion -Wpedantic -Weffc++ -Wshadow di atas dan cairan di atas dan /W4 dalam msvc), dieksekusi, dan dijalankan dengan sukses.
Karena pustaka ini membutuhkan fitur C ++ 17, kode harus dikompilasi dengan dukungan C ++ 17:
-std=c++17 . Di Linux, Anda juga perlu menggunakan bendera -pthread untuk mengaktifkan pustaka utas POSIX./std:c++17 , dan juga /permissive- untuk memastikan kesesuaian standar.Untuk kinerja maksimum, disarankan untuk dikompilasi dengan semua optimasi kompiler yang tersedia:
-O3 ./O2 . Sebagai contoh, untuk mengkompilasi program pengujian BS_thread_pool_test.cpp dengan peringatan dan optimisasi, disarankan untuk menggunakan perintah berikut:
g++ BS_thread_pool_test.cpp -std=c++17 -O3 -Wall -Wextra -Wconversion -Wsign-conversion -Wpedantic -Weffc++ -Wshadow -pthread -o BS_thread_pool_testg++ dengan clang++ .-o BS_thread_pool_test dengan -o BS_thread_pool_test.exe dan hapus -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 Untuk menginstal BS::thread_pool , cukup unduh rilis terbaru dari repositori github, tempatkan file header BS_thread_pool.hpp dari folder include di folder yang diinginkan, dan sertakan dalam program Anda:
# include " BS_thread_pool.hpp " Kumpulan utas sekarang akan dapat diakses melalui kelas BS::thread_pool . Untuk instalasi yang lebih cepat, Anda dapat mengunduh file header itu sendiri secara langsung di URL ini.
Perpustakaan ini juga dilengkapi dengan file header utilitas independen BS_thread_pool_utils.hpp , yang tidak diharuskan menggunakan kumpulan utas, tetapi menyediakan beberapa kelas utilitas yang mungkin bermanfaat untuk multithreading. File header ini juga berada di folder include . Ini dapat diunduh langsung di URL ini.
Perpustakaan ini juga tersedia di berbagai manajer paket dan sistem build, termasuk VCPKG, Conan, Meson, dan CMake dengan CPM. Silakan lihat di bawah untuk detail lebih lanjut.
Konstruktor default membuat kumpulan utas dengan sebanyak mungkin utas yang dapat ditangani oleh perangkat keras secara bersamaan, seperti yang dilaporkan oleh implementasi melalui std::thread::hardware_concurrency() . Ini biasanya ditentukan oleh jumlah inti dalam CPU. Jika inti adalah hyperthreaded, itu akan dihitung sebagai dua utas. Misalnya:
// Constructs a thread pool with as many threads as available in the hardware.
BS::thread_pool pool;Secara opsional, sejumlah utas yang berbeda dari konkurensi perangkat keras dapat ditentukan sebagai argumen untuk konstruktor. Namun, perhatikan bahwa menambahkan lebih banyak utas daripada yang dapat ditangani perangkat keras tidak akan meningkatkan kinerja, dan pada kenyataannya kemungkinan besar akan menghambatnya. Opsi ini ada untuk memungkinkan penggunaan lebih sedikit utas daripada konkurensi perangkat keras, dalam kasus di mana Anda ingin meninggalkan beberapa utas yang tersedia untuk proses lain. Misalnya:
// Constructs a thread pool with only 12 threads.
BS::thread_pool pool ( 12 );Biasanya, ketika kumpulan utas digunakan, utas utama program hanya boleh mengirimkan tugas ke kumpulan utas dan menunggu mereka selesai, dan tidak boleh melakukan tugas intensif komputasi sendiri. Dalam hal ini, disarankan untuk menggunakan nilai default untuk jumlah utas. Ini memastikan bahwa semua utas yang tersedia dalam perangkat keras akan digunakan sementara utas utama menunggu.
Fungsi anggota get_thread_count() Mengembalikan jumlah utas di kumpulan. Ini akan sama dengan std::thread::hardware_concurrency() Jika konstruktor default digunakan.
Umumnya tidak perlu mengubah jumlah utas di kolam setelah dibuat, karena seluruh titik kumpulan utas adalah Anda hanya membuat utas sekali. Namun, jika perlu, ini dapat dilakukan, dengan aman dan on-the-fly, menggunakan fungsi anggota reset() .
reset() akan menunggu semua tugas yang sedang berjalan untuk diselesaikan, tetapi akan meninggalkan sisa tugas dalam antrian. Maka itu akan menghancurkan kumpulan utas dan membuat yang baru dengan jumlah utas baru yang diinginkan, seperti yang ditentukan dalam argumen fungsi (atau konkurensi perangkat keras jika tidak ada argumen yang diberikan). Kumpulan utas baru kemudian akan melanjutkan menjalankan tugas yang tetap ada dalam antrian dan tugas baru yang diajukan.
Jika diinginkan, versi perpustakaan ini dapat dibaca selama waktu kompilasi dari tiga makro berikut:
BS_THREAD_POOL_VERSION_MAJOR - menunjukkan versi utama.BS_THREAD_POOL_VERSION_MINOR - menunjukkan versi minor.BS_THREAD_POOL_VERSION_PATCH - menunjukkan versi patch. std::cout << " Thread pool library version is " << BS_THREAD_POOL_VERSION_MAJOR << ' . ' << BS_THREAD_POOL_VERSION_MINOR << ' . ' << BS_THREAD_POOL_VERSION_PATCH << " . n " ;Output sampel:
Thread pool library version is 4.1.0.
Ini dapat digunakan, misalnya, untuk memungkinkan basis kode yang sama bekerja dengan beberapa versi perpustakaan yang tidak kompatibel dengan menggunakan arahan #if .
Catatan: Fitur ini hanya tersedia dimulai dengan v4.0.1. Rilis sebelumnya dari perpustakaan ini tidak mendefinisikan makro ini.
Di bagian ini kita akan belajar cara mengirimkan tugas tanpa argumen, tetapi berpotensi dengan nilai pengembalian, ke antrian. Setelah tugas telah diajukan, itu akan dieksekusi segera setelah utas tersedia. Tugas dieksekusi dalam urutan bahwa mereka diajukan (pertama, pertama-keluar), kecuali prioritas tugas diaktifkan (lihat di bawah).
Misalnya, jika kolam memiliki 8 utas dan antrian kosong, dan kami mengirimkan 16 tugas, maka kami harus mengharapkan 8 tugas pertama untuk dieksekusi secara paralel, dengan tugas yang tersisa diambil oleh utas satu per satu karena setiap utas menyelesaikan tugas pertama, sampai tidak ada tugas yang dibiarkan dalam antrian.
Fungsi Anggota submit_task() digunakan untuk mengirimkan tugas ke antrian. Dibutuhkan tepat satu input, tugas untuk dikirim. Tugas ini harus menjadi fungsi tanpa argumen, tetapi dapat memiliki nilai pengembalian. Nilai pengembalian adalah std::future yang terkait dengan tugas.
Jika fungsi yang dikirimkan memiliki nilai pengembalian tipe T , maka masa depan akan tipe std::future<T> , dan akan diatur ke nilai pengembalian ketika fungsi menyelesaikan eksekusi. Jika fungsi yang dikirim tidak memiliki nilai pengembalian, maka masa depan akan menjadi std::future<void> , yang tidak akan mengembalikan nilai apa pun tetapi masih dapat digunakan untuk menunggu fungsi selesai.
Menggunakan auto untuk Nilai Pengembalian submit_task() berarti kompiler akan secara otomatis mendeteksi instance mana dari Template std::future untuk digunakan. Namun, menentukan tipe tertentu std::future<T> , seperti pada contoh di bawah ini, direkomendasikan untuk meningkatkan keterbacaan.
Untuk menunggu sampai tugas selesai, gunakan fungsi anggota wait() di masa depan. Untuk mendapatkan nilai pengembalian, gunakan fungsi anggota get() , yang juga akan secara otomatis menunggu tugas menyelesaikan jika belum. Berikut adalah contoh sederhana:
# 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 ' ;
} Dalam contoh ini kami mengirimkan fungsi the_answer() , yang mengembalikan int . Fungsi Anggota submit_task() Oleh karena itu kumpulan mengembalikan std::future<int> . Kami kemudian menggunakan fungsi anggota get() di masa depan untuk mendapatkan nilai pengembalian, dan mencetaknya.
Selain mengirimkan fungsi yang telah ditentukan sebelumnya, kami juga dapat menggunakan ekspresi Lambda untuk dengan cepat menentukan tugas saat-terbang. Menulis ulang contoh sebelumnya dalam hal ekspresi lambda, kami mendapatkan:
# 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 ' ;
} Di sini, ekspresi lambda []{ return 42; } memiliki dua bagian:
[] . Ini menandakan kompiler bahwa ekspresi lambda sedang didefinisikan.{ return 42; } Itu hanya mengembalikan nilai 42 .Secara umum lebih sederhana dan lebih cepat untuk mengirimkan ekspresi lambda daripada fungsi yang telah ditentukan sebelumnya, terutama karena kemampuan untuk menangkap variabel lokal, yang akan kita bahas di bagian selanjutnya.
Tentu saja, tugas tidak harus mengembalikan nilai. Dalam contoh berikut, kami mengirimkan fungsi tanpa nilai pengembalian dan kemudian menggunakan masa depan untuk menunggu untuk menyelesaikannya:
# 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 ' ;
} Di sini kami membagi lambda menjadi beberapa baris untuk membuatnya lebih mudah dibaca. Perintah std::this_thread::sleep_for(std::chrono::milliseconds(500)) menginstruksikan tugas untuk hanya tidur selama 500 milidetik, mensimulasikan tugas intensif komputasi.
Seperti yang dinyatakan pada bagian sebelumnya, tugas yang dikirimkan menggunakan submit_task() tidak dapat memiliki argumen. Namun, mudah untuk mengirimkan tugas dengan argumen baik dengan membungkus fungsi dalam lambda atau menggunakan tangkapan lambda secara langsung. Berikut adalah dua contoh.
Berikut ini adalah contoh mengirimkan fungsi yang telah ditentukan dengan argumen dengan membungkusnya dengan 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 ' ;
} Seperti yang Anda lihat, untuk meneruskan argumen untuk multiply yang kita sebut multiply(6, 7) secara eksplisit di dalam lambda. Jika argumennya bukan literal, kita perlu menggunakan klausul penangkapan lambda untuk menangkap argumen dari ruang lingkup lokal:
# 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 ' ;
} Kami bahkan dapat menyingkirkan fungsi multiply sepenuhnya dan meletakkan semuanya di dalam lambda, jika diinginkan:
# 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 ' ;
} Biasanya, yang terbaik adalah mengirimkan tugas ke antrian menggunakan submit_task() . Ini memungkinkan Anda untuk menunggu tugas menyelesaikan dan/atau mendapatkan nilai pengembaliannya nanti. Namun, kadang -kadang masa depan tidak diperlukan, misalnya ketika Anda hanya ingin "mengatur dan melupakan" tugas tertentu, atau jika tugas sudah berkomunikasi dengan utas utama atau dengan tugas lain tanpa menggunakan futures, seperti via variabel kondisi.
Dalam kasus seperti itu, Anda mungkin ingin menghindari overhead yang terlibat dalam menetapkan masa depan untuk tugas untuk meningkatkan kinerja. Ini disebut "melepas" tugas, karena tugas melepaskan dari utas utama dan berjalan secara mandiri.
Tugas pelepasan dilakukan dengan menggunakan fungsi anggota detach_task() , yang memungkinkan Anda untuk melepaskan tugas ke antrian tanpa menghasilkan masa depan untuk itu. Tugas dapat memiliki sejumlah argumen, tetapi tidak dapat memiliki nilai pengembalian, karena tidak akan ada cara bagi utas utama untuk mengambil nilai itu.
Karena detach_task() tidak mengembalikan masa depan, tidak ada cara bawaan bagi pengguna untuk mengetahui kapan tugas selesai dieksekusi. Anda harus secara manual memastikan bahwa tugas selesai dieksekusi sebelum mencoba menggunakan apa pun yang tergantung pada outputnya. Kalau tidak, hal -hal buruk akan terjadi!
BS::thread_pool menyediakan fungsi anggota wait() untuk memfasilitasi menunggu semua tugas dalam antrian untuk diselesaikan, apakah mereka terlepas atau diserahkan dengan masa depan. Fungsi anggota wait() berfungsi serupa dengan fungsi anggota wait() dari std::future . Pertimbangkan, misalnya, kode berikut:
# 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 ' ;
} Program ini pertama kali mendefinisikan variabel lokal bernama result dan menginisialisasi ke 0 . Kemudian melepaskan tugas dalam bentuk ekspresi lambda. Perhatikan bahwa lambda menangkap result dengan referensi , seperti yang ditunjukkan oleh & di depannya. Ini berarti bahwa tugas dapat memodifikasi result , dan modifikasi semacam itu akan tercermin di utas utama. Tugas berubah result menjadi 42 , tetapi pertama kali tidur selama 100 milidetik. Ketika utas utama mencetak nilai result , tugas belum punya waktu untuk memodifikasi nilainya, karena masih tidur. Oleh karena itu, program akan mencetak nilai awal 0 .
Untuk menunggu tugas menyelesaikan, kita harus menggunakan fungsi anggota wait() setelah melepaskannya:
# 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 ' ;
} Sekarang program akan mencetak nilai 42 , seperti yang diharapkan. Perhatikan, bagaimanapun, bahwa wait() akan menunggu semua tugas dalam antrian, termasuk tugas lain yang berpotensi diajukan sebelum atau setelah yang kita pedulikan. Jika kita ingin menunggu hanya satu tugas, submit_task() akan menjadi pilihan yang lebih baik.
Terkadang Anda mungkin ingin menunggu tugas diselesaikan, tetapi hanya untuk waktu tertentu, atau sampai titik waktu tertentu. Misalnya, jika tugas belum selesai setelah beberapa waktu, Anda mungkin ingin memberi tahu pengguna bahwa ada penundaan.
Untuk tugas yang dikirimkan dengan futures menggunakan submit_task() , ini dapat dicapai dengan menggunakan dua fungsi anggota std::future :
wait_for() menunggu tugas selesai, tetapi berhenti menunggu setelah durasi yang ditentukan, diberikan sebagai argumen tipe std::chrono::duration , telah berlalu.wait_until() menunggu tugas selesai, tetapi berhenti menunggu setelah titik waktu yang ditentukan, diberikan sebagai argumen tipe std::chrono::time_point , telah tercapai. Dalam kedua kasus, fungsi akan mengembalikan future_status::ready jika masa depan siap, artinya tugas selesai dan nilai pengembaliannya, jika ada, telah diperoleh. Namun, itu akan mengembalikan std::future_status::timeout jika masa depan belum siap pada saat batas waktu telah berakhir.
Inilah contohnya:
# 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 ;
}
}Output harus terlihat mirip dengan ini:
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!
Untuk tugas yang terpisah, karena kami tidak memiliki masa depan untuk mereka, kami tidak dapat menggunakan metode ini. Namun, BS::thread_pool memiliki dua fungsi anggota, juga bernama wait_for() dan wait_until() , yang juga menunggu untuk durasi yang ditentukan atau sampai titik waktu yang ditentukan, tetapi lakukan untuk semua tugas (apakah dikirimkan atau dilepas). Alih -alih std::future_status , fungsi tunggu kumpulan utas kembali true jika semua tugas selesai berjalan, atau false jika durasi berakhir atau titik waktu tercapai tetapi beberapa tugas masih berjalan.
Berikut adalah contoh yang sama seperti di atas, menggunakan detach_task() dan 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 ;
}
}Mari kita pertimbangkan program berikut:
# 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 ' ;
} Program ini membuat objek baru flag_object dari kelas flag_class , mengatur flag ke true menggunakan fungsi setter function set_flag() , dan kemudian mencetak nilai flag menggunakan fungsi anggota getter get_flag() .
Bagaimana jika kita ingin mengirimkan fungsi anggota set_flag() sebagai tugas ke kumpulan utas? Kami cukup membungkus seluruh pernyataan flag_object.set_flag(true); Dari garis dalam lambda, dan lulus flag_object ke lambda dengan referensi, seperti dalam contoh ini:
# 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 ' ;
} Tentu saja, ini juga akan bekerja dengan detach_task() , jika kita menelepon wait() di kolam itu sendiri alih -alih di masa depan yang dikembalikan.
Perhatikan bahwa dalam contoh ini, alih -alih mendapatkan masa depan dari submit_task() dan kemudian menunggu masa depan itu, kami hanya menelepon wait() di masa depan itu langsung. Ini adalah cara umum untuk menunggu tugas diselesaikan jika kita tidak memiliki hal lain untuk dilakukan sementara itu. Perhatikan juga bahwa kami lulus flag_object dengan mengacu pada lambda, karena kami ingin mengatur bendera pada objek yang sama, bukan salinannya (melewati nilai tidak akan berfungsi, karena variabel yang ditangkap berdasarkan nilai secara implisit const ).
Hal lain yang mungkin ingin Anda lakukan adalah memanggil fungsi anggota dari dalam objek itu sendiri, yaitu, dari fungsi anggota lain. Ini mengikuti sintaks yang sama, kecuali bahwa Anda juga harus menangkap this (yaitu pointer ke objek saat ini) di lambda. Inilah contohnya:
# 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 ' ;
} Perhatikan bahwa dalam contoh ini kami mendefinisikan kumpulan utas sebagai objek global, sehingga dapat diakses di luar fungsi main() .
Salah satu metode paralelisasi yang paling umum dan efektif adalah membagi loop menjadi loop yang lebih kecil dan menjalankannya secara paralel. Ini paling efektif dalam perhitungan "memalukan paralel", seperti operasi vektor atau matriks, di mana setiap iterasi loop sepenuhnya independen dari setiap iterasi lainnya.
Misalnya, jika kita menyimpulkan dua vektor masing -masing dari 1000 elemen, dan kita memiliki 10 utas, kita dapat membagi penjumlahan menjadi 10 blok masing -masing 100 elemen, dan menjalankan semua blok secara paralel, berpotensi meningkatkan kinerja hingga faktor 10.
BS::thread_pool dapat secara otomatis memparalelkan loop. Untuk melihat cara kerjanya, pertimbangkan loop generik berikut:
for (T i = start; i < end; ++i)
loop (i);Di mana:
T adalah tipe integer yang ditandatangani atau tidak ditandatangani.[start, end) , yaitu termasuk start tetapi tidak termasuk end .loop() adalah operasi yang dilakukan untuk setiap indeks loop i , seperti memodifikasi array dengan elemen end - start . Loop ini dapat secara otomatis diparalelkan dan diserahkan ke antrian kumpulan utas menggunakan fungsi anggota submit_loop() , yang memiliki sintaks berikut:
pool.submit_loop(start, end, loop, num_blocks);Di mana:
start adalah indeks pertama dalam kisaran.end adalah indeks setelah indeks terakhir dalam kisaran, sehingga rentang penuh [start, end) . Dengan kata lain, loop akan setara dengan yang di atas jika start dan end adalah sama.start dan end keduanya harus memiliki integer tipe T yang sama. Lihat di bawah untuk contoh apa yang harus dilakukan ketika mereka tidak memiliki tipe yang sama.end <= start , tidak ada yang akan terjadi.loop() adalah fungsi yang harus dijalankan dalam setiap iterasi loop, dan mengambil satu argumen, indeks loop.num_blocks adalah jumlah blok dari bentuk [a, b) untuk membagi loop menjadi. Misalnya, jika rentangnya [0, 9) dan ada 3 blok, maka blok akan menjadi rentang [0, 3) , [3, 6) , dan [6, 9) .[0, 100) dibagi menjadi 15 blok, hasilnya akan 10 blok ukuran 7, yang akan dieksekusi terlebih dahulu, dan 5 blok ukuran 6.Setiap blok akan diserahkan ke antrian kumpulan utas sebagai tugas terpisah. Oleh karena itu, loop yang dibagi menjadi 3 blok akan dibagi menjadi 3 tugas individu, yang dapat berjalan secara paralel. Jika hanya ada satu blok, maka seluruh loop akan berjalan sebagai satu tugas, dan tidak ada paralelisasi yang akan terjadi.
Untuk memparalelkan loop generik di atas, kami menggunakan perintah berikut:
BS::multi_future< void > loop_future = pool.submit_loop(start, end, loop, num_blocks);
loop_future.wait(); submit_loop() mengembalikan objek template kelas pembantu BS::multi_future . Ini pada dasarnya adalah spesialisasi std::vector<std::future<T>> dengan fungsi anggota tambahan. Masing -masing blok num_blocks akan memiliki std::future yang ditugaskan untuk itu, dan semua masa depan ini akan disimpan di dalam BS::multi_future . Ketika loop_future.wait() dipanggil, utas utama akan menunggu sampai semua tugas yang dihasilkan oleh submit_loop() selesai dieksekusi, dan hanya tugas -tugas itu - bukan tugas lain yang juga berada dalam antrian. Ini pada dasarnya adalah peran kelas BS::multi_future : untuk menunggu kelompok tugas tertentu, dalam hal ini tugas yang menjalankan blok loop.
Nilai apa yang harus Anda gunakan untuk num_blocks ? Menghilangkan argumen ini, sehingga jumlah blok akan sama dengan jumlah utas di kolam, biasanya merupakan pilihan yang baik. Untuk kinerja terbaik, disarankan untuk melakukan tolok ukur sendiri untuk menemukan jumlah blok optimal untuk setiap loop (Anda dapat menggunakan kelas utilitas BS::timer ). Menggunakan tugas yang lebih sedikit daripada ada utas yang mungkin lebih disukai jika Anda juga menjalankan tugas lain secara paralel. Menggunakan lebih banyak tugas daripada ada utas yang dapat meningkatkan kinerja dalam beberapa kasus, tetapi paralelisasi dengan terlalu banyak tugas akan menderita pengembalian yang semakin berkurang.
Sebagai contoh sederhana, kode berikut menghitung dan mencetak tabel kotak semua bilangan bulat dari 0 hingga 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 " );
}Kita dapat memparalelkannya sebagai berikut:
# 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 " );
} Karena ada 10 utas, dan kami menghilangkan argumen num_blocks , loop akan dibagi menjadi 10 blok, masing -masing menghitung 10 kotak.
Perhatikan bahwa submit_loop() dieksekusi dengan parameter template eksplisit <unsigned int> . Alasannya adalah bahwa kedua indeks loop harus dari jenis yang sama. Namun, di sini max adalah unsigned int , sedangkan 0 adalah int (ditandatangani) int , sehingga tipe tidak cocok, dan kode tidak akan dikompilasi kecuali kita memaksa 0 menjadi tipe yang tepat. Ini dapat dilakukan dengan paling elegan dengan menentukan jenis indeks secara eksplisit menggunakan parameter templat.
Alasan ini tidak dilakukan secara otomatis (misalnya menggunakan std::common_type adalah bahwa hal itu dapat mengakibatkan secara tidak sengaja melemparkan indeks negatif ke jenis yang tidak ditandatangani, atau indeks integer ke jenis integer yang terlalu sempit, yang dapat menyebabkan rentang loop yang salah.
Kami juga bisa memberikan 0 secara eksplisit ke int unsigned, tetapi itu tidak terlihat bagus:
pool.submit_loop( static_cast < unsigned int >( 0 ), max, /* ... */ );Atau kita bisa menggunakan cast c-style:
pool.submit_loop(( unsigned int )( 0 ), max, /* ... */ );Atau kita bisa menggunakan sufiks literal integer:
pool.submit_loop< size_t >( 0U , max, ...);Sebagai catatan, perhatikan bahwa di sini kami memparalelkan perhitungan kotak, tetapi kami tidak memparalelkan pencetakan hasil. Ini karena dua alasan:
Sama seperti dalam kasus detach_task() vs. submit_task() , kadang -kadang Anda mungkin ingin memparalelkan loop, tetapi Anda tidak perlu mengembalikan BS::multi_future . Dalam hal ini, Anda dapat menyimpan overhead menghasilkan masa depan (yang bisa signifikan, tergantung pada jumlah blok) dengan menggunakan detach_loop() alih -alih submit_loop() , dengan argumen yang sama.
Misalnya, kita dapat melepaskan loop dari contoh kotak di atas sebagai berikut:
# 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.
Berikut adalah contoh sederhana:
# 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.Misalnya:
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. Lihat di bawah untuk detail lebih lanjut.
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. Misalnya:
# 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.
Berikut adalah contoh sederhana:
# 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 .
Berikut adalah contoh sederhana:
# 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.
