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| Author | SHA1 | Date | |
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 Barak Shoshany
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41637a8d1e | ||
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Barak Shoshany
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b448742337 | ||
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Barak Shoshany
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27b2dbc130 |
@@ -1,60 +1,113 @@
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# A simple but powerful C++17 thread pool class
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# A C++17 Thread Pool for High-Performance Scientific Computing
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**Barak Shoshany**\
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Department of Physics, Brock University,\
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1812 Sir Isaac Brock Way, St. Catharines, Ontario, L2S 3A1, Canada\
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[baraksh@gmail.com](mailto:baraksh@gmail.com) | [https://baraksh.com/](https://baraksh.com/)\
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Companion paper: [arXiv:2105.00613](https://arxiv.org/abs/2105.00613)
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<!-- TOC depthFrom:2 -->
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- [Abstract](#abstract)
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- [Introduction](#introduction)
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- [Features](#features)
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- [Basic usage](#basic-usage)
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||||
- [Motivation](#motivation)
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||||
- [Overview of features](#overview-of-features)
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||||
- [Compiling and compatibility](#compiling-and-compatibility)
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||||
- [Getting started](#getting-started)
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||||
- [Including the library](#including-the-library)
|
||||
- [Constructors](#constructors)
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||||
- [Submitting tasks to the queue](#submitting-tasks-to-the-queue)
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- [Parallelizing loops](#parallelizing-loops)
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- [Advanced usage](#advanced-usage)
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- [Getting and resetting the number of threads in the pool](#getting-and-resetting-the-number-of-threads-in-the-pool)
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- [Submitting and waiting for tasks](#submitting-and-waiting-for-tasks)
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||||
- [Submitting tasks to the queue with futures](#submitting-tasks-to-the-queue-with-futures)
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||||
- [Submitting tasks to the queue without futures](#submitting-tasks-to-the-queue-without-futures)
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||||
- [Manually waiting for all tasks to complete](#manually-waiting-for-all-tasks-to-complete)
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||||
- [Synchronizing printing to an output stream](#synchronizing-printing-to-an-output-stream)
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- [Motivation](#motivation)
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||||
- [The synced stream class](#the-synced-stream-class)
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||||
- [Compiling](#compiling)
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||||
- [Parallelizing loops](#parallelizing-loops)
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||||
- [Other features](#other-features)
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||||
- [Synchronizing printing to an output stream](#synchronizing-printing-to-an-output-stream)
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||||
- [Setting the worker function's sleep duration](#setting-the-worker-functions-sleep-duration)
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- [Monitoring the tasks](#monitoring-the-tasks)
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- [Pausing the workers](#pausing-the-workers)
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||||
- [Performance tests](#performance-tests)
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- [Measuring execution time](#measuring-execution-time)
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- [AMD Ryzen 9 3900X (24 threads)](#amd-ryzen-9-3900x-24-threads)
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- [Dual Intel Xeon Gold 6148 (80 threads)](#dual-intel-xeon-gold-6148-80-threads)
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- [Version history](#version-history)
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- [Feedback](#feedback)
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||||
- [Author and copyright](#author-and-copyright)
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||||
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||||
<!-- /TOC -->
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||||
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<a id="markdown-abstract" name="abstract"></a>
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## Abstract
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We present a modern C++17-compatible thread pool implementation, built from scratch with high-performance scientific computing in mind. The thread pool is implemented as a single lightweight and self-contained class, and does not have any dependencies other than the C++17 standard library, thus allowing a great degree of portability. In particular, our implementation does not utilize OpenMP or any other high-level multithreading APIs, and thus gives the programmer precise low-level control over the details of the parallelization, which permits more robust optimizations. The thread pool was extensively tested on both AMD and Intel CPUs with up to 40 cores and 80 threads. This paper provides motivation, detailed usage instructions, and performance tests. The code is freely available in the [GitHub repository](https://github.com/bshoshany/thread-pool). This `README.md` file contains roughly the same content as the [companion paper](https://arxiv.org/abs/2105.00613).
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<a id="markdown-introduction" name="introduction"></a>
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## Introduction
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<a id="markdown-motivation" name="motivation"></a>
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### Motivation
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|
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Multithreading is essential for modern high-performance computing. Since C++11, the C++ standard library has included built-in low-level multithreading support using constructs such as `std::thread`. However, `std::thread` creates a new thread each time it is called, which can have a significant performance overhead. Furthermore, it is possible to create more threads than the hardware can handle simultaneously, potentially resulting in a substantial slowdown.
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||||
This library contains a thread pool class, which avoids these issues by creating a fixed pool of threads once and for all, and then reusing the same threads to perform different tasks throughout the lifetime of the pool. By default, the number of threads in the pool is equal to the maximum number of threads that the hardware can run in parallel. The user submits tasks to be executed into a queue. Whenever a thread becomes available, it pops a task off the queue and executes it. If the task is a function with a return value, that value can be obtained later using an `std::future`.
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This library contains a thread pool class, `thread_pool`, which avoids these issues by creating a fixed pool of threads once and for all, and then reusing the same threads to perform different tasks throughout the lifetime of the pool. By default, the number of threads in the pool is equal to the maximum number of threads that the hardware can run in parallel.
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||||
In addition to `std::thread`, the C++ standard library also offers the higher-level construct `std::async`, which may internally utilize a thread pool - but this is not guaranteed, and in fact, currently only the MSVC implementation of `std::async` uses a thread pool, while GCC and Clang do not. Using a custom-made thread pool class instead of `std::async` allows the user more control, transparency, and portability.
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||||
The user submits tasks to be executed into a queue. Whenever a thread becomes available, it pops a task from the queue and executes it. Each task is automatically assigned an `std::future`, which can be used to wait for the task to finish executing and/or obtain its eventual return value.
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||||
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||||
<a id="markdown-features" name="features"></a>
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||||
## Features
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||||
In addition to `std::thread`, the C++ standard library also offers the higher-level construct `std::async`, which may internally utilize a thread pool - but this is not guaranteed, and in fact, currently only the MSVC implementation of `std::async` uses a thread pool, while GCC and Clang do not. Using our custom-made thread pool class instead of `std::async` allows the user more control, transparency, and portability.
|
||||
|
||||
High-level multithreading APIs, such as OpenMP, allow simple one-line automatic parallelization of C++ code, but they do not give the user precise low-level control over the details of the parallelization. The thread pool class presented here allows the programmer to perform and manage the parallelization at the lowest level, and thus permits more robust optimizations, which can be used to achieve considerably higher performance.
|
||||
|
||||
As demonstrated in the performance tests [below](#performance-tests), using our thread pool class we were able to saturate the upper bound of expected speedup for matrix multiplication and generation of random matrices. These performance tests were performed on 12-core / 24-thread and 40-core / 80-thread systems using GCC on Linux.
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||||
|
||||
<a id="markdown-overview-of-features" name="overview-of-features"></a>
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||||
### Overview of features
|
||||
|
||||
* **Fast:**
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||||
* Built from scratch with performance in mind.
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||||
* Compact code reduces both compilation time and binary size.
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||||
* Reusing threads avoids the overhead of creating and destroying them.
|
||||
* Built from scratch with maximum performance in mind.
|
||||
* Suitable for use in high-performance computing clusters with a very large number of CPU cores.
|
||||
* Compact code, to reduce both compilation time and binary size.
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||||
* Reusing threads avoids the overhead of creating and destroying them for individual tasks.
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||||
* A task queue ensures that there are never more threads running in parallel than allowed by the hardware.
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||||
* **Lightweight:**
|
||||
* Only ~180 lines of code, excluding comments and blank lines.
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* Single header file: simply `#include "thread_pool.hpp"`, and you're done.
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* Only ~160 lines of code, excluding comments, blank lines, and the two optional helper classes.
|
||||
* Single header file: simply `#include "thread_pool.hpp"`.
|
||||
* Header-only: no need to install or build the library.
|
||||
* Self-contained: no external requirements or dependencies. Does not require OpenMP or any other multithreading APIs. Only uses the C++ standard library, and works with any C++17-compliant compiler.
|
||||
* **Easy to use:**
|
||||
* Very simple operation, using a handful of member functions.
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||||
* Futures are automatically generated for every task submitted to the queue.
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||||
* The code is thoroughly documented using Doxygen comments - not only the interface, but also the implementation, in case you would like to make modifications.
|
||||
* Every task submitted to the queue automatically generates an `std::future`, which can be used to wait for the task to finish executing and/or obtain its eventual return value.
|
||||
* Optionally, tasks may also be submitted without generating a future, sacrificing convenience for greater performance.
|
||||
* The code is thoroughly documented using Doxygen comments - not only the interface, but also the implementation, in case the user would like to make modifications.
|
||||
* **Additional features:**
|
||||
* Automatically parallelize a loop into any number of parallel tasks.
|
||||
* Synchronize output to a stream by multiple threads in parallel.
|
||||
* Easily wait for all tasks in the queue to complete.
|
||||
* Change the number of threads in the pool safely and on-the-fly as needed.
|
||||
* Fine-tune the sleep duration of each thread's worker function for optimal performance.
|
||||
* Monitor the number of queued and/or running tasks.
|
||||
* Pause and resume popping new tasks out of the queue.
|
||||
* Synchronize output to a stream from multiple threads in parallel using the `synced_stream` helper class.
|
||||
* Easily measure execution time for benchmarking purposes using the `timer` helper class.
|
||||
|
||||
<a id="markdown-basic-usage" name="basic-usage"></a>
|
||||
## Basic usage
|
||||
<a id="markdown-compiling-and-compatibility" name="compiling-and-compatibility"></a>
|
||||
### Compiling and compatibility
|
||||
|
||||
This library should successfully compile on any C++17 standard-compliant compiler, on all operating systems for which such a compiler is available. Compatibility was verified with a 12-core / 24-thread AMD Ryzen 9 3900X CPU at 3.8 GHz using the following compilers and platforms:
|
||||
|
||||
* GCC v10.2.0 on Windows 10 build 19042.928.
|
||||
* GCC v10.3.0 on Ubuntu 21.04.
|
||||
* Clang v11.0.0 on Windows 10 build 19042.928 and Ubuntu 21.04.
|
||||
* MSVC v14.28.29910 on Windows 10 build 19042.928.
|
||||
|
||||
In addition, this library was tested on a [Compute Canada](https://www.computecanada.ca/) node equipped with two 20-core / 40-thread Intel Xeon Gold 6148 CPUs at 2.4 GHz, for a total of 40 cores and 80 threads, running CentOS Linux 7.6.1810, using the following compilers:
|
||||
|
||||
* GCC v9.2.0
|
||||
* Intel C++ Compiler (ICC) v19.1.3.304
|
||||
|
||||
As this library requires C++17 features, the code must be compiled with C++17 support. For GCC, Clang, and ICC, use the `-std=c++17` flag. For MSVC, use `/std:c++17`. On Linux, you will also need to use the `-pthread` flag with GCC, Clang, or ICC to enable the POSIX threads library.
|
||||
|
||||
<a id="markdown-getting-started" name="getting-started"></a>
|
||||
## Getting started
|
||||
|
||||
<a id="markdown-including-the-library" name="including-the-library"></a>
|
||||
### Including the library
|
||||
@@ -84,8 +137,24 @@ Optionally, a number of threads different from the hardware concurrency can be s
|
||||
thread_pool pool(12);
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```
|
||||
|
||||
<a id="markdown-submitting-tasks-to-the-queue" name="submitting-tasks-to-the-queue"></a>
|
||||
### Submitting tasks to the queue
|
||||
If your program's main thread only submits tasks to the thread pool and waits for them to finish, and does not perform any computationally intensive tasks on its own, then it is recommended to use the default value for the number of threads. This ensures that all of the threads available in the hardware will be put to work while the main thread waits.
|
||||
|
||||
However, if your main thread does perform computationally intensive tasks on its own, then it is recommended to use the value `std::thread::hardware_concurrency() - 1` for the number of threads. In this case, the main thread plus the thread pool will together take up exactly all the threads available in the hardware.
|
||||
|
||||
<a id="markdown-getting-and-resetting-the-number-of-threads-in-the-pool" name="getting-and-resetting-the-number-of-threads-in-the-pool"></a>
|
||||
### Getting and resetting the number of threads in the pool
|
||||
|
||||
The member function `get_thread_count()` returns the number of threads in the pool. This will be equal to `std::thread::hardware_concurrency()` if the default constructor was used.
|
||||
|
||||
It is generally unnecessary to change the number of threads in the pool after it has been created, since the whole point of a thread pool is that you only create the threads once. However, if needed, this can be done, safely and on-the-fly, using the `reset()` member function.
|
||||
|
||||
`reset()` will wait for all currently running tasks to be completed, but will leave the rest of the tasks in the queue. Then it will destroy the thread pool and create a new one with the desired new number of threads, as specified in the function's argument (or the hardware concurrency if no argument is given). The new thread pool will then resume executing the tasks that remained in the queue and any new submitted tasks.
|
||||
|
||||
<a id="markdown-submitting-and-waiting-for-tasks" name="submitting-and-waiting-for-tasks"></a>
|
||||
## Submitting and waiting for tasks
|
||||
|
||||
<a id="markdown-submitting-tasks-to-the-queue-with-futures" name="submitting-tasks-to-the-queue-with-futures"></a>
|
||||
### Submitting tasks to the queue with futures
|
||||
|
||||
A task can be any function, with zero or more arguments, and with or without a return value. Once a task has been submitted to the queue, it will be executed as soon as a thread becomes available. Tasks are executed in the order that they were submitted (first-in, first-out).
|
||||
|
||||
@@ -100,23 +169,60 @@ auto my_future = pool.submit(task, arg);
|
||||
auto my_future = pool.submit(task, arg1, arg2);
|
||||
```
|
||||
|
||||
The value of the future depends on whether the function has a return value or not:
|
||||
Using `auto` for the return value of `submit()` is recommended, since it means the compiler will automatically detect which instance of the template `std::future` to use. The value of the future depends on whether the function has a return value or not:
|
||||
|
||||
* If the submitted function has a return value, then the future will be set to that value when the function finishes its execution.
|
||||
* If the submitted function does not have a return value, then the future is a `bool` that will be set to `true` when the function finishes its execution.
|
||||
* If the submitted function does not have a return value, then the future will be a `bool` that will be set to `true` when the function finishes its execution.
|
||||
|
||||
To wait until the future's value becomes available, use the member function `wait()`. To obtain the value itself (and automatically wait for it if it is not yet ready), use the member function `get()`. For example:
|
||||
To wait until the future's value becomes available, use the member function `wait()`. To obtain the value itself, use the member function `get()`, which will also automatically wait for the future if it's not ready yet. For example:
|
||||
|
||||
```cpp
|
||||
// Submit a task and get a future.
|
||||
auto my_future = pool.submit(task);
|
||||
// Do some other stuff while the task is executing.
|
||||
do_stuff();
|
||||
// Get the task's return value from the future.
|
||||
// Get the task's return value from the future, waiting for it to finish running if needed.
|
||||
auto my_return_value = my_future.get();
|
||||
```
|
||||
|
||||
Please see [the C++ reference entry for `std::future`](https://en.cppreference.com/w/cpp/thread/future) for more information about using futures.
|
||||
<a id="markdown-submitting-tasks-to-the-queue-without-futures" name="submitting-tasks-to-the-queue-without-futures"></a>
|
||||
### Submitting tasks to the queue without futures
|
||||
|
||||
Usually, it is best to submit a task to the queue using `submit()`. This allows you to wait for the task to finish and/or get its return value later. However, sometimes a future is not needed, for example when you just want to "set and forget" a certain task, or if the task already communicates with the main thread or with other tasks without using futures, such as via references or pointers. In such cases, you may wish to avoid the overhead involved in assigning a future to the task in order to increase performance.
|
||||
|
||||
The member function `push_task()` allows you to submit a task to the queue without generating a future for it. The task can have any number of arguments, but it cannot have a return value. For example:
|
||||
|
||||
```cpp
|
||||
// Submit a task without arguments or return value to the queue.
|
||||
pool.push_task(task);
|
||||
// Submit a task with one argument and no return value to the queue.
|
||||
pool.push_task(task, arg);
|
||||
// Submit a task with two arguments and no return value to the queue.
|
||||
pool.push_task(task, arg1, arg2);
|
||||
```
|
||||
|
||||
<a id="markdown-manually-waiting-for-all-tasks-to-complete" name="manually-waiting-for-all-tasks-to-complete"></a>
|
||||
### Manually waiting for all tasks to complete
|
||||
|
||||
To wait for a **single** submitted task to complete, use `submit()` and then use the `wait()` or `get()` member functions of the obtained future. However, in cases where you need to wait until **all** submitted tasks finish their execution, or if the tasks have been submitted without futures using `push_task()`, you can use the member function `wait_for_tasks()`.
|
||||
|
||||
Consider, for example, the following code:
|
||||
|
||||
```cpp
|
||||
thread_pool pool;
|
||||
size_t a[100];
|
||||
for (size_t i = 0; i < 100; i++)
|
||||
pool.push_task([&a, i] { a[i] = i * i; });
|
||||
std::cout << a[50];
|
||||
```
|
||||
|
||||
The output will most likely be garbage, since the task that modifies `a[50]` has not yet finished executing by the time we try to access that element (in fact, that task is probably still waiting in the queue). One solution would be to use `submit()` instead of `push_task()`, but perhaps we don't want the overhead of generating 100 different futures. Instead, simply adding the line
|
||||
|
||||
```cpp
|
||||
pool.wait_for_tasks();
|
||||
```
|
||||
|
||||
after the `for` loop will ensure - as efficiently as possible - that all tasks have finished running before we attempt to access any elements of the array `a`, and the code will print out the value `2500` as expected. (Note, however, that `wait_for_tasks()` will wait for **all** the tasks in the queue, including those that are unrelated to the `for` loop. Using `parallelize_loop()` would make much more sense in this particular case, as it will wait only for the tasks related to the loop.)
|
||||
|
||||
<a id="markdown-parallelizing-loops" name="parallelizing-loops"></a>
|
||||
### Parallelizing loops
|
||||
@@ -135,14 +241,14 @@ where:
|
||||
* `end` is the last index to loop over (inclusive).
|
||||
* `loop()` is a function that takes exactly one argument, the loop index, and has no return value.
|
||||
|
||||
This loop may be automatically parallelized and submitted to the thread pool's queue using the `parallelize_loop()` member function as follows:
|
||||
This loop may be automatically parallelized and submitted to the thread pool's queue using the member function `parallelize_loop()` as follows:
|
||||
|
||||
```cpp
|
||||
// Equivalent to the above loop, but will be automatically parallelized.
|
||||
pool.parallelize_loop(start, end, loop);
|
||||
```
|
||||
|
||||
The loop will be parallelized into a number of tasks equal to the number of threads in the pool, with each task executing the function `loop()` for a roughly equal number of indices. The calling thread will block and wait until all tasks generated by `parallelize_loop` (and only them) finish executing.
|
||||
The loop will be parallelized into a number of tasks equal to the number of threads in the pool, with each task executing the function `loop()` for a roughly equal number of indices. The main thread will then wait until all tasks generated by `parallelize_loop()` finish executing (and only those tasks - not any other tasks that also happen to be in the queue).
|
||||
|
||||
If desired, the number of parallel tasks may be manually specified using a fourth argument:
|
||||
|
||||
@@ -151,62 +257,35 @@ If desired, the number of parallel tasks may be manually specified using a fourt
|
||||
pool.parallelize_loop(start, end, loop, 12);
|
||||
```
|
||||
|
||||
For best performance, it is recommended to do your own benchmarks to find the optimal number of tasks for each loop. Using less tasks than there are threads may be preferred if you are also running other tasks in parallel. Using more tasks than there are threads may improve performance in some cases (see the documentation for [my multithreaded matrix class template](https://github.com/bshoshany/multithreaded-matrix) for analysis).
|
||||
For best performance, it is recommended to do your own benchmarks to find the optimal number of tasks for each loop (you can use the `timer` helper class - see [below](#measuring-execution-time)). Using less tasks than there are threads may be preferred if you are also running other tasks in parallel. Using more tasks than there are threads may improve performance in some cases.
|
||||
|
||||
<a id="markdown-advanced-usage" name="advanced-usage"></a>
|
||||
## Advanced usage
|
||||
|
||||
<a id="markdown-getting-and-resetting-the-number-of-threads-in-the-pool" name="getting-and-resetting-the-number-of-threads-in-the-pool"></a>
|
||||
### Getting and resetting the number of threads in the pool
|
||||
|
||||
The member function `get_thread_count()` returns the number of threads in the pool. This will be equal to `std::thread::hardware_concurrency()` if the default constructor has been used.
|
||||
|
||||
It is generally unnecessary to change the number of threads in the pool after it has been created, since the whole point of a thread pool is that you only create the threads once. However, if desired, this can be done using the `reset()` member function, which waits for all submitted tasks to be completed, then destroys all threads and creates a new thread pool with the given number of threads. As for the constructor, if no argument is given, the number of threads will be the hardware concurrency.
|
||||
|
||||
<a id="markdown-submitting-tasks-to-the-queue-without-futures" name="submitting-tasks-to-the-queue-without-futures"></a>
|
||||
### Submitting tasks to the queue without futures
|
||||
|
||||
Usually, it is best to submit a task to the queue using `submit()`. This allows you to wait for the task to finish and/or get its return value later. However, sometimes a future is not needed, for example when you just want to "set and forget" a certain task, or if the task already communicates with other tasks in other ways, without using futures. In such cases, you may wish to avoid the overhead involved in assigning the future to the task.
|
||||
|
||||
The member function `push_task()` allows you to submit a task to the queue without getting a future for it. The task can have any number of arguments, but it cannot have a return value. For example:
|
||||
As a simple example, the following code will calculate the squares of all integers from 0 to 99. Since there are 10 threads, the loop will be divided into 10 tasks, each calculating 10 squares:
|
||||
|
||||
```cpp
|
||||
// Submit a task without arguments or return value to the queue.
|
||||
pool.push_task(task);
|
||||
// Submit a task with one argument and no return value to the queue.
|
||||
pool.push_task(task, arg);
|
||||
// Submit a task with two arguments and no return value to the queue.
|
||||
pool.push_task(task, arg1, arg2);
|
||||
#include "thread_pool.hpp"
|
||||
|
||||
int main()
|
||||
{
|
||||
thread_pool pool(10);
|
||||
size_t squares[100];
|
||||
pool.parallelize_loop(0, 99, [&squares](size_t i) { squares[i] = i * i; });
|
||||
std::cout << "16^2 = " << squares[16] << '\n';
|
||||
std::cout << "32^2 = " << squares[32] << '\n';
|
||||
}
|
||||
```
|
||||
|
||||
<a id="markdown-manually-waiting-for-all-tasks-to-complete" name="manually-waiting-for-all-tasks-to-complete"></a>
|
||||
### Manually waiting for all tasks to complete
|
||||
The output should be:
|
||||
|
||||
To wait for a **single** submitted task to complete, use `submit()` and then use the `wait()` or `get()` member functions of the obtained future. However, in cases where you need to wait until **all** submitted tasks finish their execution, or if the tasks have been submitted without futures using `push_task`, you may use the member function `wait_for_tasks()` of the thread pool.
|
||||
|
||||
Consider, for example, the following code:
|
||||
|
||||
```cpp
|
||||
thread_pool pool;
|
||||
size_t a[100];
|
||||
for (size_t i = 0; i < 100; i++)
|
||||
pool.push_task([&a, i] { a[i] = i * i; });
|
||||
std::cout << a[50];
|
||||
```none
|
||||
16^2 = 256
|
||||
32^2 = 1024
|
||||
```
|
||||
|
||||
The output will most likely be garbage, since the task that modifies `a[50]` has not yet finished executing by the time we try to access that element (in fact, that task is probably still in the queue). One solution would be to use `submit` instead of `push_task`, but perhaps we don't want the overhead of waiting for 100 different futures. Instead, simply adding the line
|
||||
|
||||
```cpp
|
||||
pool.wait_for_tasks();
|
||||
```
|
||||
|
||||
after the `for` loop will ensure - as efficiently as possible - that all tasks have finished running before we attempt to access any elements of the array `a`, and the code will print out `2500` as expected. (Note, however, that `wait_for_tasks()` will wait for **all** tasks in the queue, including those that are unrelated to the `for` loop. Using `parallelize_loop()` would make much more sense in this case, as it will wait only for tasks related to the loop.)
|
||||
<a id="markdown-other-features" name="other-features"></a>
|
||||
## Other features
|
||||
|
||||
<a id="markdown-synchronizing-printing-to-an-output-stream" name="synchronizing-printing-to-an-output-stream"></a>
|
||||
## Synchronizing printing to an output stream
|
||||
|
||||
<a id="markdown-motivation" name="motivation"></a>
|
||||
### Motivation
|
||||
### Synchronizing printing to an output stream
|
||||
|
||||
When printing to an output stream from multiple threads in parallel, the output may become garbled. For example, consider this code:
|
||||
|
||||
@@ -230,10 +309,7 @@ Task no. 1 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.
|
||||
|
||||
<a id="markdown-the-synced-stream-class" name="the-synced-stream-class"></a>
|
||||
### The synced stream class
|
||||
|
||||
The class `synced_stream` is designed to avoid 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:
|
||||
The helper class `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:
|
||||
|
||||
```cpp
|
||||
// Construct a synced stream that will print to std::cout.
|
||||
@@ -244,7 +320,7 @@ 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 `synced_stream` object must wait until the previous call has finished.
|
||||
|
||||
For example, this code:
|
||||
As an example, this code:
|
||||
|
||||
```cpp
|
||||
synced_stream sync_out;
|
||||
@@ -265,20 +341,380 @@ Task no. 4 executing.
|
||||
Task no. 5 executing.
|
||||
```
|
||||
|
||||
<a id="markdown-compiling" name="compiling"></a>
|
||||
## Compiling
|
||||
**Warning:** Always create the `synced_stream` object **before** the `thread_pool` object, as we did in this example. When the `thread_pool` object goes out of scope, it waits for the remaining tasks to be executed. If the `synced_stream` object goes out of scope before the `thread_pool` object, then any tasks using the `synced_stream` will crash. Since objects are destructed in the opposite order of construction, creating the `synced_stream` object before the `thread_pool` object ensures that the `synced_stream` is always available to the tasks, even while the pool is destructing.
|
||||
|
||||
This library was tested on the following compilers and platforms:
|
||||
<a id="markdown-setting-the-worker-functions-sleep-duration" name="setting-the-worker-functions-sleep-duration"></a>
|
||||
### Setting the worker function's sleep duration
|
||||
|
||||
* GCC v10.2.0 on Windows 10 build 19042.746 and Ubuntu 20.04.1 LTS.
|
||||
* Clang 11.0.0 on Windows 10 build 19042.746 and Ubuntu 20.04.1 LTS.
|
||||
* MSVC v14.28.29333 on Windows 10 build 19042.746.
|
||||
The **worker function** is the function that controls the execution of tasks by each thread. It loops continuously, and with each iteration of the loop, checks if there are any tasks in the queue. If it finds a task, it pops it out of the queue and executes it. If it does not find a task, it will wait for a bit, by calling `std::this_thread::sleep_for()`, and then check the queue again. The public member variable `sleep_duration` controls the duration, in microseconds, that the worker function sleeps for when it cannot find a task in the queue.
|
||||
|
||||
As this library requires C++17 features, the code must be compiled with C++17 support. For GCC and Clang, use the `-std=c++17` flag. For MSVC, use `/std:c++17`. On Linux, you may need to pass `-pthread` to GCC and Clang to enable the POSIX threads library.
|
||||
The default value of `sleep_duration` is `1000` microseconds, or `1` millisecond. In our benchmarks, lower values resulted in high CPU usage when the workers were idle. The value of `1000` microseconds was roughly the minimum value needed to reduce the idle CPU usage to a negligible amount.
|
||||
|
||||
In addition, in our benchmarks this value resulted in moderately improved performance compared to lower values, since the workers check the queue - which is a costly process - less frequently. On the other hand, increasing the value even more could potentially cause the workers to spend too much time sleeping and not pick up tasks from the queue quickly enough, so `1000` is the "sweet spot".
|
||||
|
||||
However, please note that this value is likely unique to the particular system our benchmarks were performed on, and your own optimal value would depend on factors such as your OS and C++ implementation, the type, complexity, and average duration of the tasks submitted to the pool, and whether there are any other programs running at the same time. Therefore, it is strongly recommended to do your own benchmarks and find the value that works best for you.
|
||||
|
||||
If `sleep_duration` is set to `0`, then the worker function will execute `std::this_thread::yield()` instead of sleeping if there are no tasks in the queue. This will suggest to the OS that it should put this thread on hold and allow other threads to run instead. However, this also causes the worker functions to have high CPU usage when idle. On the other hand, for some applications this setting may provide better performance than sleeping - again, do your own benchmarks and find what works best for you.
|
||||
|
||||
<a id="markdown-monitoring-the-tasks" name="monitoring-the-tasks"></a>
|
||||
### Monitoring the tasks
|
||||
|
||||
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.
|
||||
* Note that `get_tasks_running() == get_tasks_total() - get_tasks_queued()`.
|
||||
|
||||
These functions are demonstrated in the following program:
|
||||
|
||||
```cpp
|
||||
#include "thread_pool.hpp"
|
||||
|
||||
void sleep_half_second(const size_t &i, synced_stream *sync_out)
|
||||
{
|
||||
std::this_thread::sleep_for(std::chrono::milliseconds(500));
|
||||
sync_out->println("Task ", i, " done.");
|
||||
}
|
||||
|
||||
void monitor_tasks(const thread_pool *pool, synced_stream *sync_out)
|
||||
{
|
||||
sync_out->println(pool->get_tasks_total(),
|
||||
" tasks total, ",
|
||||
pool->get_tasks_running(),
|
||||
" tasks running, ",
|
||||
pool->get_tasks_queued(),
|
||||
" tasks queued.");
|
||||
}
|
||||
|
||||
int main()
|
||||
{
|
||||
synced_stream sync_out;
|
||||
thread_pool pool(4);
|
||||
for (size_t i = 0; i < 12; i++)
|
||||
pool.push_task(sleep_half_second, i, &sync_out);
|
||||
monitor_tasks(&pool, &sync_out);
|
||||
std::this_thread::sleep_for(std::chrono::milliseconds(750));
|
||||
monitor_tasks(&pool, &sync_out);
|
||||
std::this_thread::sleep_for(std::chrono::milliseconds(500));
|
||||
monitor_tasks(&pool, &sync_out);
|
||||
std::this_thread::sleep_for(std::chrono::milliseconds(500));
|
||||
monitor_tasks(&pool, &sync_out);
|
||||
}
|
||||
```
|
||||
|
||||
Assuming you have at least 4 hardware threads (so that 4 tasks can run concurrently), the output will be similar to:
|
||||
|
||||
```none
|
||||
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.
|
||||
```
|
||||
|
||||
<a id="markdown-pausing-the-workers" name="pausing-the-workers"></a>
|
||||
### Pausing the workers
|
||||
|
||||
Sometimes you may wish to temporarily pause the execution of tasks, or perhaps you want to submit tasks to the queue but only start executing them at a later time. You can do this using the public member variable `paused`.
|
||||
|
||||
When `paused` is set to `true`, the workers will temporarily stop popping 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 popping tasks, set `paused` back to its default value of `false`.
|
||||
|
||||
Here is an example:
|
||||
|
||||
```cpp
|
||||
#include "thread_pool.hpp"
|
||||
|
||||
void sleep_half_second(const size_t &i, synced_stream *sync_out)
|
||||
{
|
||||
std::this_thread::sleep_for(std::chrono::milliseconds(500));
|
||||
sync_out->println("Task ", i, " done.");
|
||||
}
|
||||
|
||||
int main()
|
||||
{
|
||||
synced_stream sync_out;
|
||||
thread_pool pool(4);
|
||||
for (size_t i = 0; i < 8; i++)
|
||||
pool.push_task(sleep_half_second, i, &sync_out);
|
||||
sync_out.println("Submitted 8 tasks.");
|
||||
std::this_thread::sleep_for(std::chrono::milliseconds(250));
|
||||
pool.paused = true;
|
||||
sync_out.println("Pool paused.");
|
||||
std::this_thread::sleep_for(std::chrono::milliseconds(1000));
|
||||
sync_out.println("Still paused...");
|
||||
std::this_thread::sleep_for(std::chrono::milliseconds(1000));
|
||||
for (size_t i = 8; i < 12; i++)
|
||||
pool.push_task(sleep_half_second, i, &sync_out);
|
||||
sync_out.println("Submitted 4 more tasks.");
|
||||
sync_out.println("Still paused...");
|
||||
std::this_thread::sleep_for(std::chrono::milliseconds(1000));
|
||||
pool.paused = false;
|
||||
sync_out.println("Pool resumed.");
|
||||
}
|
||||
```
|
||||
|
||||
Assuming you have at least 4 hardware threads, the output will be similar to:
|
||||
|
||||
```none
|
||||
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 resumed.
|
||||
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 resumed, the remaining 4 initial tasks were executed, followed by the 4 new tasks.
|
||||
|
||||
While the workers are paused, `wait_for_tasks()` will wait for the running tasks instead of all tasks (otherwise it would wait forever). This is demonstrated by the following program:
|
||||
|
||||
```cpp
|
||||
#include "thread_pool.hpp"
|
||||
|
||||
void sleep_half_second(const size_t &i, synced_stream *sync_out)
|
||||
{
|
||||
std::this_thread::sleep_for(std::chrono::milliseconds(500));
|
||||
sync_out->println("Task ", i, " done.");
|
||||
}
|
||||
|
||||
int main()
|
||||
{
|
||||
synced_stream sync_out;
|
||||
thread_pool pool(4);
|
||||
for (size_t i = 0; i < 8; i++)
|
||||
pool.push_task(sleep_half_second, i, &sync_out);
|
||||
sync_out.println("Submitted 8 tasks. Waiting for them to complete.");
|
||||
pool.wait_for_tasks();
|
||||
for (size_t i = 8; i < 20; i++)
|
||||
pool.push_task(sleep_half_second, i, &sync_out);
|
||||
sync_out.println("Submitted 12 more tasks.");
|
||||
std::this_thread::sleep_for(std::chrono::milliseconds(250));
|
||||
pool.paused = true;
|
||||
sync_out.println("Pool paused. Waiting for the ", pool.get_tasks_running(), " running tasks to complete.");
|
||||
pool.wait_for_tasks();
|
||||
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.paused = false;
|
||||
std::this_thread::sleep_for(std::chrono::milliseconds(250));
|
||||
sync_out.println("Pool resumed. Waiting for the remaining ", pool.get_tasks_total(), " tasks (", pool.get_tasks_running(), " running and ", pool.get_tasks_queued(), " queued) to complete.");
|
||||
pool.wait_for_tasks();
|
||||
sync_out.println("All tasks completed.");
|
||||
}
|
||||
```
|
||||
|
||||
The output should be similar to:
|
||||
|
||||
```none
|
||||
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 resumed. 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.
|
||||
```
|
||||
|
||||
The first `wait_for_tasks()`, which was called with `paused == false`, waited for all 8 tasks, both running and queued. The second `wait_for_tasks()`, which was called with `paused == true`, 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_for_tasks()`, which was called with `paused == false`, 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.
|
||||
|
||||
<a id="markdown-performance-tests" name="performance-tests"></a>
|
||||
## Performance tests
|
||||
|
||||
<a id="markdown-measuring-execution-time" name="measuring-execution-time"></a>
|
||||
### Measuring execution time
|
||||
|
||||
If 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 under different conditions. In the context of the thread pool class, you would probably be interested in finding the optimal number of threads in the pool and the optimal sleep duration for the worker functions.
|
||||
|
||||
The helper class `timer` provides a simple way to measure execution time. It is very straightforward to use:
|
||||
|
||||
1. Create a new `timer` object.
|
||||
2. Immediately before you execute the computation that you want to time, call the `start()` member function.
|
||||
3. Immediately after the computation ends, call the `stop()` member function.
|
||||
4. Use the member function `ms()` to obtain the elapsed time for the computation in milliseconds.
|
||||
|
||||
For example:
|
||||
|
||||
```cpp
|
||||
timer tmr;
|
||||
tmr.start();
|
||||
do_something();
|
||||
tmr.stop();
|
||||
std::cout << "The elapsed time was " << tmr.ms() << " ms.\n";
|
||||
```
|
||||
|
||||
To benchmark the performance of our thread pool class, we measured the execution time of various parallelized operations on large matrices, using a custom-built matrix class template. The test code makes use of version 1.3 of the `thread_pool` class and implements a generalization of its `parallelize_loop()` member function adapted specifically for parallelizing matrix operations. Execution time was measured using the `timer` helper class.
|
||||
|
||||
For each matrix operation, we parallelized the computation into blocks. Each block consists of a number of atomic operations equal to the block size, and was submitted as a separate task to the thread pool's queue, such that the number of blocks equals the total number of tasks. We tested 6 different block sizes for each operation in order to compare their execution time.
|
||||
|
||||
<a id="markdown-amd-ryzen-9-3900x-24-threads" name="amd-ryzen-9-3900x-24-threads"></a>
|
||||
### AMD Ryzen 9 3900X (24 threads)
|
||||
|
||||
The first test was performed on a computer equipped with a 12-core / 24-thread AMD Ryzen 9 3900X CPU at 3.8 GHz, compiled using GCC v10.3.0 on Ubuntu 21.04 with the `-O3` compiler flag. The thread pool consisted of 24 threads, making full use of the CPU's hyperthreading capabilities.
|
||||
|
||||
The output of our test program was as follows:
|
||||
|
||||
```none
|
||||
Adding two 4800x4800 matrices:
|
||||
With block size of 23040000 ( 1 block ), execution took 37 ms.
|
||||
With block size of 3840000 ( 6 blocks), execution took 17 ms.
|
||||
With block size of 1920000 (12 blocks), execution took 16 ms.
|
||||
With block size of 960000 (24 blocks), execution took 17 ms.
|
||||
With block size of 480000 (48 blocks), execution took 17 ms.
|
||||
With block size of 240000 (96 blocks), execution took 17 ms.
|
||||
|
||||
Generating random 4800x4800 matrix:
|
||||
With block size of 23040000 ( 1 block ), execution took 291 ms.
|
||||
With block size of 3840000 ( 6 blocks), execution took 52 ms.
|
||||
With block size of 1920000 (12 blocks), execution took 27 ms.
|
||||
With block size of 960000 (24 blocks), execution took 25 ms.
|
||||
With block size of 480000 (48 blocks), execution took 20 ms.
|
||||
With block size of 240000 (96 blocks), execution took 17 ms.
|
||||
|
||||
Transposing one 4800x4800 matrix:
|
||||
With block size of 23040000 ( 1 block ), execution took 129 ms.
|
||||
With block size of 3840000 ( 6 blocks), execution took 24 ms.
|
||||
With block size of 1920000 (12 blocks), execution took 19 ms.
|
||||
With block size of 960000 (24 blocks), execution took 17 ms.
|
||||
With block size of 480000 (48 blocks), execution took 16 ms.
|
||||
With block size of 240000 (96 blocks), execution took 15 ms.
|
||||
|
||||
Multiplying two 800x800 matrices:
|
||||
With block size of 640000 ( 1 block ), execution took 431 ms.
|
||||
With block size of 106666 ( 6 blocks), execution took 88 ms.
|
||||
With block size of 53333 (12 blocks), execution took 61 ms.
|
||||
With block size of 26666 (24 blocks), execution took 42 ms.
|
||||
With block size of 13333 (48 blocks), execution took 37 ms.
|
||||
With block size of 6666 (96 blocks), execution took 32 ms.
|
||||
```
|
||||
|
||||
In this test, we find a speedup by roughly a factor of 2 for addition, 9 for transposition, 13 for matrix multiplication, and 17 for random matrix generation. Here are some lessons we can learn from these results:
|
||||
|
||||
* For simple element-wise operations such as addition, multithreading improves performance very modestly, only by a factor of 2, even when utilizing every available hardware thread. This is because compiler optimizations already parallelize simple loops fairly well on their own. Omitting the `-O3` optimization flag, we observed a factor of 9 speedup for addition. However, the user will most likely be compiling with optimizations turned on anyway.
|
||||
* Matrix multiplication and random matrix generation, which are more complicated operations that cannot be automatically parallelized by compiler optimizations, gain the most out of multithreading - with a very significant speedup by a factor of 15 on average. Given that the test CPU only has 12 physical cores, and hyperthreading can generally produce no more than a 30% performance improvement, a factor of 15 speedup is about as good as can be expected.
|
||||
* Transposition also enjoys a factor of 9 speedup with multithreading. Note that transposition requires reading memory is non-sequential order, jumping between the rows of the source matrix, which is why, compared to sequential operations such as addition, it is much slower when single-threaded, but benefits more from multithreading, especially when split into smaller blocks.
|
||||
* Even though the test CPU only has 24 threads, there is still a small but consistent benefit to dividing the computation into 48 or even 96 parallel blocks. This is especially significant in multiplication, where we get roughly a 25% speedup with 96 blocks (4 blocks per thread) compared to 24 blocks (1 block per thread).
|
||||
|
||||
<a id="markdown-dual-intel-xeon-gold-6148-80-threads" name="dual-intel-xeon-gold-6148-80-threads"></a>
|
||||
### Dual Intel Xeon Gold 6148 (80 threads)
|
||||
|
||||
The second test was performed on a [Compute Canada](https://www.computecanada.ca/) node equipped with dual 20-core / 40-thread Intel Xeon Gold 6148 CPUs at 2.4 GHz, for a total of 40 cores and 80 threads, compiled using GCC v9.2.0 on CentOS Linux 7.6.1810 with the `-O3` compiler flag. The thread pool consisted of 80 threads, making full use of the hyperthreading capabilities of both CPUs.
|
||||
|
||||
We adjusted the block sizes compared to the previous test, to match the larger number of threads. The output of our test program was as follows:
|
||||
|
||||
```none
|
||||
Adding two 4800x4800 matrices:
|
||||
With block size of 23040000 ( 1 block ), execution took 73 ms.
|
||||
With block size of 1152000 ( 20 blocks), execution took 9 ms.
|
||||
With block size of 576000 ( 40 blocks), execution took 7 ms.
|
||||
With block size of 288000 ( 80 blocks), execution took 7 ms.
|
||||
With block size of 144000 (160 blocks), execution took 8 ms.
|
||||
With block size of 72000 (320 blocks), execution took 10 ms.
|
||||
|
||||
Generating random 4800x4800 matrix:
|
||||
With block size of 23040000 ( 1 block ), execution took 423 ms.
|
||||
With block size of 1152000 ( 20 blocks), execution took 29 ms.
|
||||
With block size of 576000 ( 40 blocks), execution took 15 ms.
|
||||
With block size of 288000 ( 80 blocks), execution took 13 ms.
|
||||
With block size of 144000 (160 blocks), execution took 11 ms.
|
||||
With block size of 72000 (320 blocks), execution took 10 ms.
|
||||
|
||||
Transposing one 4800x4800 matrix:
|
||||
With block size of 23040000 ( 1 block ), execution took 167 ms.
|
||||
With block size of 1152000 ( 20 blocks), execution took 18 ms.
|
||||
With block size of 576000 ( 40 blocks), execution took 11 ms.
|
||||
With block size of 288000 ( 80 blocks), execution took 9 ms.
|
||||
With block size of 144000 (160 blocks), execution took 10 ms.
|
||||
With block size of 72000 (320 blocks), execution took 12 ms.
|
||||
|
||||
Multiplying two 800x800 matrices:
|
||||
With block size of 640000 ( 1 block ), execution took 771 ms.
|
||||
With block size of 32000 ( 20 blocks), execution took 57 ms.
|
||||
With block size of 16000 ( 40 blocks), execution took 24 ms.
|
||||
With block size of 8000 ( 80 blocks), execution took 21 ms.
|
||||
With block size of 4000 (160 blocks), execution took 17 ms.
|
||||
With block size of 2000 (320 blocks), execution took 15 ms.
|
||||
```
|
||||
|
||||
In this test, we find a speedup by roughly a factor of 10 for addition, 19 for transposition, 42 for random matrix generation, and 51 for matrix multiplication. The last result again matches the estimation of a 30% improvement in performance due to hyperthreading, which indicates that we are once again saturating the maximum possible performance of our system.
|
||||
|
||||
An interesting point to notice is that for **single-threaded** calculations (1 block), the dual Xeon CPUs actually perform worse by up to a factor of 2 compared to the single Ryzen CPU. This is due to the base clock speed of the Ryzen (3.8 GHz) being considerably higher than the base clock speed of the Xeon (2.4 GHz). Since each core of the Xeon is slower than each core of the Ryzen, we need more parallelization to achieve the same overall speed. However, with full parallelization (24 threads on the Ryzen, 80 threads on the Xeon), the latter is faster by about a factor of 2.
|
||||
|
||||
<a id="markdown-version-history" name="version-history"></a>
|
||||
## Version history
|
||||
|
||||
* Version 1.4 (2021-05-05)
|
||||
* Added three new public member functions to monitor the tasks submitted to the pool:
|
||||
* `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.
|
||||
* Note that `get_tasks_running() == get_tasks_total() - get_tasks_queued()`.
|
||||
* Renamed the private member variable `tasks_waiting` to `tasks_total` to make its purpose clearer.
|
||||
* Added an option to temporarily pause the workers:
|
||||
* When public member variable `paused` is set to `true`, the workers temporarily stop popping new tasks out of the queue, although any tasks already executed will keep running until they are done. Set to `false` again to resume popping tasks.
|
||||
* While the workers are paused, `wait_for_tasks()` will wait for the running tasks instead of all tasks (otherwise it would wait forever).
|
||||
* By utilizing the new pausing mechanism, `reset()` can now change the number of threads on-the-fly while there are still tasks waiting in the queue. The new thread pool will resume executing tasks from the queue once it is created.
|
||||
* `parallelize_loop()` and `wait_for_tasks()` now have the same behavior as the worker function with regards to waiting for tasks to complete. If the relevant tasks are not yet complete, then before checking again, they will sleep for `sleep_duration` microseconds, unless that variable is set to zero, in which case they will call `std::this_thread::yield()`. This should improve performance and reduce CPU usage.
|
||||
* Merged [this commit](https://github.com/bshoshany/thread-pool/pull/8): Fixed weird error when using MSVC and including `windows.h`.
|
||||
* The `README.md` file has been reorganized and expanded.
|
||||
* Version 1.3 (2021-05-03)
|
||||
* Fixed [this issue](https://github.com/bshoshany/thread-pool/issues/3): Removed `std::move` from the `return` statement in `push_task()`. This previously generated a `-Wpessimizing-move` warning in Clang. The assembly code generated by the compiler seems to be the same before and after this change, presumably because the compiler eliminates the `std::move` automatically, but this change gets rid of the Clang warning.
|
||||
* Fixed [this issue](https://github.com/bshoshany/thread-pool/issues/5): Removed a debugging message printed to `std::cout`, which was left in the code by mistake.
|
||||
* Fixed [this issue](https://github.com/bshoshany/thread-pool/issues/6): `parallelize_loop()` no longer sends references for the variables `start` and `stop` when calling `push_task()`, which may lead to undefined behavior.
|
||||
* A companion paper is now published at <a href="https://arxiv.org/abs/2105.00613">arXiv:2105.00613</a>, including additional information such as performance tests on systems with up to 80 hardware threads. The `README.md` has been updated, and it is now roughly identical in content to the paper.
|
||||
* Version 1.2 (2021-04-29)
|
||||
* The worker function, which controls the execution of tasks by each thread, now sleeps by default instead of yielding. Previously, when the worker could not find any tasks in the queue, it called `std::this_thread::yield()` and then tried again. However, this caused the workers to have high CPU usage when idle, [as reported by some users](https://github.com/bshoshany/thread-pool/issues/1). Now, when the worker function cannot find a task to run, it instead sleeps for a duration given by the public member variable `sleep_duration` (in microseconds) before checking the queue again. The default value is `1000` microseconds, which I found to be optimal in terms of both CPU usage and performance, but your own optimal value may be different.
|
||||
* If the constructor is called with an argument of zero for the number of threads, then the default value, `std::thread::hardware_concurrency()`, is used instead.
|
||||
* Added a simple helper class, `timer`, which can be used to measure execution time for benchmarking purposes.
|
||||
* Improved and expanded the documentation.
|
||||
* Version 1.1 (2021-04-24)
|
||||
* Cosmetic changes only. Fixed a typo in the Doxygen comments and added a link to the GitHub repository.
|
||||
* Version 1.0 (2021-01-15)
|
||||
@@ -287,11 +723,11 @@ As this library requires C++17 features, the code must be compiled with C++17 su
|
||||
<a id="markdown-feedback" name="feedback"></a>
|
||||
## Feedback
|
||||
|
||||
If you would like a request any additional features, or if you encounter any bugs, please feel free to open a new issue!
|
||||
If you would like a request any additional features, or if you encounter any bugs, please feel free to [open a new issue](https://github.com/bshoshany/thread-pool/issues)!
|
||||
|
||||
<a id="markdown-author-and-copyright" name="author-and-copyright"></a>
|
||||
## Author and copyright
|
||||
|
||||
Copyright (c) 2021 [Barak Shoshany](http://baraksh.com) (baraksh@gmail.com). Licensed under the [MIT license](LICENSE.txt).
|
||||
Copyright (c) 2021 [Barak Shoshany](http://baraksh.com). Licensed under the [MIT license](LICENSE.txt). If you found this code useful, please consider providing a link to the [GitHub repository](https://github.com/bshoshany/thread-pool) and/or citing the [companion paper](https://arxiv.org/abs/2105.00613):
|
||||
|
||||
If you use this class in your code, please acknowledge the author and provide a link to the [GitHub repository](https://github.com/bshoshany/thread-pool). Thank you!
|
||||
* Barak Shoshany, "A C++17 Thread Pool for High-Performance Scientific Computing", [arXiv:2105.00613](https://arxiv.org/abs/2105.00613) (May 2021)
|
||||
|
||||
+180
-48
@@ -3,18 +3,19 @@
|
||||
/**
|
||||
* @file thread_pool.hpp
|
||||
* @author Barak Shoshany (baraksh@gmail.com) (http://baraksh.com)
|
||||
* @version 1.1
|
||||
* @date 2021-04-24
|
||||
* @copyright Copyright (c) 2021 Barak Shoshany. Licensed under the MIT license.
|
||||
* @version 1.4
|
||||
* @date 2021-05-05
|
||||
* @copyright Copyright (c) 2021 Barak Shoshany. Licensed under the MIT license. If you found this code useful, please consider providing a link to the GitHub repository: https://github.com/bshoshany/thread-pool and/or citing the companion paper: https://arxiv.org/abs/2105.00613
|
||||
*
|
||||
* @brief A simple but powerful C++17 thread pool class. Please visit the GitHub repository at https://github.com/bshoshany/thread-pool for documentation and updates, or to submit feature requests and bug reports.
|
||||
* @brief A C++17 thread pool for high-performance scientific computing.
|
||||
* @details A modern C++17-compatible thread pool implementation, built from scratch with high-performance scientific computing in mind. The thread pool is implemented as a single lightweight and self-contained class, and does not have any dependencies other than the C++17 standard library, thus allowing a great degree of portability. In particular, this implementation does not utilize OpenMP or any other high-level multithreading APIs, and thus gives the programmer precise low-level control over the details of the parallelization, which permits more robust optimizations. The thread pool was extensively tested on both AMD and Intel CPUs with up to 40 cores and 80 threads. Other features include automatic generation of futures and easy parallelization of loops. Two helper classes enable synchronizing printing to an output stream by different threads and measuring execution time for benchmarking purposes. Please visit the GitHub repository for documentation and updates, or to submit feature requests and bug reports.
|
||||
*/
|
||||
|
||||
#include <algorithm> // std::max
|
||||
#include <atomic> // std::atomic
|
||||
#include <cstdint> // std::uint_fast32_t
|
||||
#include <chrono> // std::chrono
|
||||
#include <cstdint> // std::int_fast64_t, std::uint_fast32_t
|
||||
#include <functional> // std::function
|
||||
#include <future> // std::promise
|
||||
#include <future> // std::future, std::promise
|
||||
#include <iostream> // std::cout, std::ostream
|
||||
#include <memory> // std::shared_ptr, std::unique_ptr
|
||||
#include <mutex> // std::mutex, std::scoped_lock
|
||||
@@ -23,8 +24,11 @@
|
||||
#include <type_traits> // std::decay_t, std::enable_if_t, std::is_void_v, std::invoke_result_t
|
||||
#include <utility> // std::move, std::swap
|
||||
|
||||
// ============================================================================================= //
|
||||
// Begin class thread_pool //
|
||||
|
||||
/**
|
||||
* @brief A simple but powerful thread pool class. Maintains a queue of tasks, which are executed by threads in the pool as they become available.
|
||||
* @brief A C++17 thread pool class. The user submits tasks to be executed into a queue. Whenever a thread becomes available, it pops a task from the queue and executes it. Each task is automatically assigned a future, which can be used to wait for the task to finish executing and/or obtain its eventual return value.
|
||||
*/
|
||||
class thread_pool
|
||||
{
|
||||
@@ -38,16 +42,16 @@ public:
|
||||
/**
|
||||
* @brief Construct a new thread pool.
|
||||
*
|
||||
* @param _thread_count The number of threads to use. Default value is the total number of hardware threads available, as reported by the implementation. With a hyperthreaded CPU, this will be twice the number of CPU cores. If the argument is zero, 1 thread will be used.
|
||||
* @param _thread_count The number of threads to use. The default value is the total number of hardware threads available, as reported by the implementation. With a hyperthreaded CPU, this will be twice the number of CPU cores. If the argument is zero, the default value will be used instead.
|
||||
*/
|
||||
thread_pool(const ui32 &_thread_count = std::thread::hardware_concurrency())
|
||||
: thread_count(std::max<ui32>(_thread_count, 1)), threads(new std::thread[std::max<ui32>(_thread_count, 1)])
|
||||
: thread_count(_thread_count ? _thread_count : std::thread::hardware_concurrency()), threads(new std::thread[_thread_count ? _thread_count : std::thread::hardware_concurrency()])
|
||||
{
|
||||
create_threads();
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Destruct the thread pool. Waits for all submitted tasks to be completed, then destroys all threads.
|
||||
* @brief Destruct the thread pool. Waits for all tasks to complete, then destroys all threads. Note that if the variable paused is set to true, then any tasks still in the queue will never be executed.
|
||||
*/
|
||||
~thread_pool()
|
||||
{
|
||||
@@ -60,6 +64,37 @@ public:
|
||||
// Public member functions
|
||||
// =======================
|
||||
|
||||
/**
|
||||
* @brief Get the number of tasks currently waiting in the queue to be executed by the threads.
|
||||
*
|
||||
* @return The number of queued tasks.
|
||||
*/
|
||||
size_t get_tasks_queued() const
|
||||
{
|
||||
const std::scoped_lock lock(queue_mutex);
|
||||
return tasks.size();
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Get the number of tasks currently being executed by the threads.
|
||||
*
|
||||
* @return The number of running tasks.
|
||||
*/
|
||||
ui32 get_tasks_running() const
|
||||
{
|
||||
return tasks_total - (ui32)get_tasks_queued();
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Get the total number of unfinished tasks - either still in the queue, or running in a thread.
|
||||
*
|
||||
* @return The total number of tasks.
|
||||
*/
|
||||
ui32 get_tasks_total() const
|
||||
{
|
||||
return tasks_total;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Get the number of threads in the pool.
|
||||
*
|
||||
@@ -71,14 +106,14 @@ public:
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Parallelize a loop by splitting it into blocks, submitting each block separately to the thread pool, and waiting for all blocks to finish executing. The loop will be equivalent to "for (T i = first_index; i <= last_index; i++) loop(i);".
|
||||
* @brief Parallelize a loop by splitting it into blocks, submitting each block separately to the thread pool, and waiting for all blocks to finish executing. The loop will be equivalent to: for (T i = first_index; i <= last_index; i++) loop(i);
|
||||
*
|
||||
* @tparam T The type of the loop index. Should be a signed or unsigned integer.
|
||||
* @tparam F The type of the function to loop through.
|
||||
* @param first_index The first index in the loop (inclusive).
|
||||
* @param last_index The last index in the loop (inclusive).
|
||||
* @param loop The function to loop through. Should take exactly one argument, the loop index.
|
||||
* @param num_tasks The maximum number of tasks to split the loop into. Default is to use the number of threads in the pool.
|
||||
* @param num_tasks The maximum number of tasks to split the loop into. The default is to use the number of threads in the pool.
|
||||
*/
|
||||
template <typename T, typename F>
|
||||
void parallelize_loop(T first_index, T last_index, const F &loop, ui32 num_tasks = 0)
|
||||
@@ -92,23 +127,22 @@ public:
|
||||
if (block_size == 0)
|
||||
{
|
||||
block_size = 1;
|
||||
num_tasks = std::max((ui32)1, (ui32)total_size);
|
||||
num_tasks = (ui32)total_size > 1 ? (ui32)total_size : 1;
|
||||
}
|
||||
std::atomic<ui32> blocks_running = 0;
|
||||
for (ui32 t = 0; t < num_tasks; t++)
|
||||
{
|
||||
T start = (T)(t * block_size + first_index);
|
||||
T end = (t == num_tasks - 1) ? last_index : (T)((t + 1) * block_size + first_index - 1);
|
||||
std::cout << start << '-' << end << '\n';
|
||||
blocks_running++;
|
||||
push_task([&start, &end, &loop, &blocks_running] {
|
||||
push_task([start, end, &loop, &blocks_running] {
|
||||
for (T i = start; i <= end; i++)
|
||||
loop(i);
|
||||
blocks_running--;
|
||||
});
|
||||
while (blocks_running != 0)
|
||||
{
|
||||
std::this_thread::yield();
|
||||
sleep_or_yield();
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -122,10 +156,10 @@ public:
|
||||
template <typename F>
|
||||
void push_task(const F &task)
|
||||
{
|
||||
tasks_waiting++;
|
||||
tasks_total++;
|
||||
{
|
||||
const std::scoped_lock lock(queue_mutex);
|
||||
tasks.push(std::move(std::function<void()>(task)));
|
||||
tasks.push(std::function<void()>(task));
|
||||
}
|
||||
}
|
||||
|
||||
@@ -145,19 +179,22 @@ public:
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Reset the number of threads in the pool. Waits for all submitted tasks to be completed, then destroys all threads and creates a new thread pool with the new number of threads.
|
||||
* @brief Reset the number of threads in the pool. 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.
|
||||
*
|
||||
* @param _thread_count The number of threads to use. Default value is the total number of hardware threads available, as reported by the implementation. With a hyperthreaded CPU, this will be twice the number of CPU cores. If the argument is zero, 1 thread will be used.
|
||||
* @param _thread_count The number of threads to use. The default value is the total number of hardware threads available, as reported by the implementation. With a hyperthreaded CPU, this will be twice the number of CPU cores. If the argument is zero, the default value will be used instead.
|
||||
*/
|
||||
void reset(const ui32 &_thread_count = std::thread::hardware_concurrency())
|
||||
{
|
||||
bool was_paused = paused;
|
||||
paused = true;
|
||||
wait_for_tasks();
|
||||
running = false;
|
||||
destroy_threads();
|
||||
thread_count = std::max<ui32>(_thread_count, 1);
|
||||
threads.reset(new std::thread[std::max<ui32>(_thread_count, 1)]);
|
||||
running = true;
|
||||
thread_count = _thread_count ? _thread_count : std::thread::hardware_concurrency();
|
||||
threads.reset(new std::thread[thread_count]);
|
||||
paused = was_paused;
|
||||
create_threads();
|
||||
running = true;
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -196,23 +233,45 @@ public:
|
||||
{
|
||||
std::shared_ptr<std::promise<R>> promise(new std::promise<R>);
|
||||
std::future<R> future = promise->get_future();
|
||||
push_task([task, args..., promise] {
|
||||
promise->set_value(task(args...));
|
||||
});
|
||||
push_task([task, args..., promise] { promise->set_value(task(args...)); });
|
||||
return future;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Wait for all submitted tasks to be completed - both those that are currently being executed by threads, and those that are still waiting in the queue. To wait for a specific task, use push_task_future instead.
|
||||
* @brief 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 variable paused is set to true, this function only waits for the currently running tasks (otherwise it would wait forever). To wait for a specific task, use submit() instead, and call the wait() member function of the generated future.
|
||||
*/
|
||||
void wait_for_tasks()
|
||||
{
|
||||
while (tasks_waiting != 0)
|
||||
while (true)
|
||||
{
|
||||
std::this_thread::yield();
|
||||
if (!paused)
|
||||
{
|
||||
if (tasks_total == 0)
|
||||
break;
|
||||
}
|
||||
else
|
||||
{
|
||||
if (get_tasks_running() == 0)
|
||||
break;
|
||||
}
|
||||
sleep_or_yield();
|
||||
}
|
||||
}
|
||||
|
||||
// ===========
|
||||
// Public data
|
||||
// ===========
|
||||
|
||||
/**
|
||||
* @brief An atomic variable indicating to the workers to pause. When set to true, the workers temporarily stop popping new tasks out of the queue, although any tasks already executed will keep running until they are done. Set to false again to resume popping tasks.
|
||||
*/
|
||||
std::atomic<bool> paused = false;
|
||||
|
||||
/**
|
||||
* @brief The duration, in microseconds, that the worker function should sleep for when it cannot find any tasks in the queue. If set to 0, then instead of sleeping, the worker function will execute std::this_thread::yield() if there are no tasks in the queue. The default value is 1000.
|
||||
*/
|
||||
ui32 sleep_duration = 1000;
|
||||
|
||||
private:
|
||||
// ========================
|
||||
// Private member functions
|
||||
@@ -260,21 +319,33 @@ private:
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief A worker function to be assigned to each thread in the pool. Pops tasks out of the queue and executes them, until the atomic variable running is set to false.
|
||||
* @brief Sleep for sleep_duration microseconds. If that variable is set to zero, yield instead.
|
||||
*
|
||||
*/
|
||||
void sleep_or_yield()
|
||||
{
|
||||
if (sleep_duration)
|
||||
std::this_thread::sleep_for(std::chrono::microseconds(sleep_duration));
|
||||
else
|
||||
std::this_thread::yield();
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief A worker function to be assigned to each thread in the pool. Continuously pops tasks out of the queue and executes them, as long as the atomic variable running is set to true.
|
||||
*/
|
||||
void worker()
|
||||
{
|
||||
while (running)
|
||||
{
|
||||
std::function<void()> task;
|
||||
if (pop_task(task))
|
||||
if (!paused and pop_task(task))
|
||||
{
|
||||
task();
|
||||
tasks_waiting--;
|
||||
tasks_total--;
|
||||
}
|
||||
else
|
||||
{
|
||||
std::this_thread::yield();
|
||||
sleep_or_yield();
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -283,21 +354,16 @@ private:
|
||||
// Private data
|
||||
// ============
|
||||
|
||||
/**
|
||||
* @brief An atomic variable indicating to the workers to keep running.
|
||||
*/
|
||||
std::atomic<bool> running = true;
|
||||
|
||||
/**
|
||||
* @brief An atomic variable to keep track of how many tasks are currently waiting to finish - either still in the queue, or running in a thread.
|
||||
*/
|
||||
std::atomic<ui32> tasks_waiting = 0;
|
||||
|
||||
/**
|
||||
* @brief A mutex to synchronize access to the task queue by different threads.
|
||||
*/
|
||||
mutable std::mutex queue_mutex;
|
||||
|
||||
/**
|
||||
* @brief An atomic variable indicating to the workers to keep running. When set to false, the workers permanently stop working.
|
||||
*/
|
||||
std::atomic<bool> running = true;
|
||||
|
||||
/**
|
||||
* @brief A queue of tasks to be executed by the threads.
|
||||
*/
|
||||
@@ -312,10 +378,21 @@ private:
|
||||
* @brief A smart pointer to manage the memory allocated for the threads.
|
||||
*/
|
||||
std::unique_ptr<std::thread[]> threads;
|
||||
|
||||
/**
|
||||
* @brief An atomic variable to keep track of the total number of unfinished tasks - either still in the queue, or running in a thread.
|
||||
*/
|
||||
std::atomic<ui32> tasks_total = 0;
|
||||
};
|
||||
|
||||
// End class thread_pool //
|
||||
// ============================================================================================= //
|
||||
|
||||
// ============================================================================================= //
|
||||
// Begin class synced_stream //
|
||||
|
||||
/**
|
||||
* @brief A class to synchronize printing to an output stream by different threads.
|
||||
* @brief A helper class to synchronize printing to an output stream by different threads.
|
||||
*/
|
||||
class synced_stream
|
||||
{
|
||||
@@ -323,13 +400,13 @@ public:
|
||||
/**
|
||||
* @brief Construct a new synced stream.
|
||||
*
|
||||
* @param _out_stream The output stream to sync to. Default is std::cout.
|
||||
* @param _out_stream The output stream to print to. The default value is std::cout.
|
||||
*/
|
||||
synced_stream(std::ostream &_out_stream = std::cout)
|
||||
: out_stream(_out_stream){};
|
||||
|
||||
/**
|
||||
* @brief Print any number of items into the output stream. Ensures that no other threads print to this stream simultaneously, as long as they all use this synced_stream object to print.
|
||||
* @brief 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 this synced_stream object to print.
|
||||
*
|
||||
* @tparam T The types of the items
|
||||
* @param items The items to print.
|
||||
@@ -342,7 +419,7 @@ public:
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Print any number of items into the output stream, followed by a newline character. Ensures that no other threads print to this stream simultaneously, as long as they all use this synced_stream object to print.
|
||||
* @brief Print any number of items into the output stream, followed by a newline character. Ensures that no other threads print to this stream simultaneously, as long as they all exclusively use this synced_stream object to print.
|
||||
*
|
||||
* @tparam T The types of the items
|
||||
* @param items The items to print.
|
||||
@@ -364,3 +441,58 @@ private:
|
||||
*/
|
||||
std::ostream &out_stream;
|
||||
};
|
||||
|
||||
// End class synced_stream //
|
||||
// ============================================================================================= //
|
||||
|
||||
// ============================================================================================= //
|
||||
// Begin class timer //
|
||||
|
||||
/**
|
||||
* @brief A helper class to measure execution time for benchmarking purposes.
|
||||
*/
|
||||
class timer
|
||||
{
|
||||
typedef std::int_fast64_t i64;
|
||||
|
||||
public:
|
||||
/**
|
||||
* @brief Start (or restart) measuring time.
|
||||
*/
|
||||
void start()
|
||||
{
|
||||
start_time = std::chrono::steady_clock::now();
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Stop measuring time and store the elapsed time since start().
|
||||
*/
|
||||
void stop()
|
||||
{
|
||||
elapsed_time = std::chrono::steady_clock::now() - start_time;
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Get the number of milliseconds that have elapsed between start() and stop().
|
||||
*
|
||||
* @return The number of milliseconds.
|
||||
*/
|
||||
i64 ms() const
|
||||
{
|
||||
return (std::chrono::duration_cast<std::chrono::milliseconds>(elapsed_time)).count();
|
||||
}
|
||||
|
||||
private:
|
||||
/**
|
||||
* @brief The time point when measuring started.
|
||||
*/
|
||||
std::chrono::time_point<std::chrono::steady_clock> start_time = std::chrono::steady_clock::now();
|
||||
|
||||
/**
|
||||
* @brief The duration that has elapsed between start() and stop().
|
||||
*/
|
||||
std::chrono::duration<double> elapsed_time = std::chrono::duration<double>::zero();
|
||||
};
|
||||
|
||||
// End class timer //
|
||||
// ============================================================================================= //
|
||||
|
||||
Reference in New Issue
Block a user