本文探讨了Python中的并发编程，包括多进程与多线程的区别、全局解释器锁(GIL)的影响、并行处理中的序列化问题及解决方案。此外，还讨论了如何利用多核优势进行高效编程，并提供了使用Python多进程库进行并行编程的实际案例。
Q
- Is
multiprocessingthe ultimate solution for concurrency? - What are the patterns of concurrent programs?
GIL issues
On multi-core machines, threads will race each other to get the GIL, which make the performance even worse.
Serialization
- What can be pickled and unpickled?
- Parallelism and Serialization : how poor pickling breaks multiprocessing
- Python Pickling and multiprocessing
- What can multiprocessing and dill do together?
- Multiprocessing: using Pool.map on a function defined in a class
- Multiprocessing: using Pool.map on a function defined in a class
- Python multiprocessing with pathos
communication
- Multiprocessing, shared memory vs. pickled copies
- Python multiprocess share memory vs using arguments
- Python multiprocessing: shared memory and pickle issue
- What are the different use cases of joblib versus pickle?
- Python’s Hardest Problem, Revisited
- Shared-memory objects in python multiprocessing
- Python multiprocessing shared memory
Tutorials
- Parallel programming with Python’s multiprocessing library
- An introduction to parallel programming: using Python’s multiprocessing module
- Python - parallelizing CPU-bound tasks with multiprocessing
- A Brief Introduction To Multiprocessing
- Python Multithreading Tutorial: Concurrency and Parallelism
- PARALLELISING PYTHON WITH THREADING AND MULTIPROCESSING
- Python Multiprocessing Tutorial - Asynchronous Programming
- Multiprocessing in Python: a guided tour with examples
- Learning Python’s Multiprocessing Module
- Threading in Python [closed]
- Secrets of the Multiprocessing Module
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