What considerations should be made when optimizing Python code that is intended to be run in a multi-threaded environment?
- a) Ensure global variables are used extensively for sharing data.
- b) Avoid using the Global Interpreter Lock (GIL) in Python.
- c) Use multi-threading for I/O-bound tasks and multi-processing for CPU-bound tasks.
- d) Use the same thread for all tasks to minimize context switching.
When optimizing Python code for multi-threading, it's important to understand the Global Interpreter Lock (GIL) (b) and how it can impact performance. Using global variables (a) for sharing data in multi-threaded code can lead to synchronization issues and should generally be avoided. The choice between multi-threading and multi-processing (c) depends on the nature of the tasks. Using the same thread for all tasks (d) would not take advantage of multi-threading.
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