


C++ concurrent programming: how to perform task scheduling and thread pool management?
May 06, 2024 am 10:15 AMTask scheduling and thread pool management are the keys to improving efficiency and scalability in C concurrent programming. Task scheduling: Use std::thread to create new threads. Use the join() method to join a thread. Thread pool management: Create a ThreadPool object and specify the number of threads. Add tasks using the add_task() method. Call the join() or stop() method to shut down the thread pool.
C Concurrent Programming: Task Scheduling and Thread Pool Management
Introduction
In concurrent programming, task scheduling and thread pool management are crucial to improve the efficiency and scalability of applications. This article walks you through the concepts of task scheduling in C and shows how to manage thread pools using std::thread
and std::mutex
from the C 11 standard.
Task Scheduling
Task scheduling involves allocating and executing asynchronous tasks. In C, you can use std::thread
to create a new thread:
1 2 3 |
|
To join a thread, use the join()
method:
1 |
|
Thread pool management
The thread pool is a pre-created and managed collection of threads that can be used to process tasks. Using a thread pool avoids the overhead of repeatedly creating and destroying threads.
Here's how to create and manage a thread pool in C:
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|
To use a thread pool, you can perform the following steps:
- Create a thread pool object, Specify the number of threads to create.
- Use the
add_task()
method to add tasks to the thread pool. - Call the
join()
orstop()
method to close the thread pool and wait for all tasks to complete.
Practical case
The following is an example of using a thread pool to perform concurrent tasks on a multi-core system:
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|
Conclusion
Concurrent tasks in C can be effectively managed by using std::thread
and thread pools. Whether it's scientific computing on multi-core systems or web services that need to handle a large number of requests, thread scheduling and thread pool management are key to improving code efficiency and scalability.
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