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Questions and Answers
What is the primary purpose of the Lock class in the threading module?
What is the main limitation of Python's threading module for CPU-bound tasks?
What is the purpose of the Semaphore class in the threading module?
Which module provides a high-level interface for parallelism using threads or processes?
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What is the primary advantage of using the multiprocessing module?
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What is the purpose of the Condition class in the threading module?
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What is the purpose of the RLock class in the threading module?
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What is the primary advantage of using the joblib library?
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What is the purpose of creating multiple threads in Python?
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What is the primary purpose of the Thread class in the threading module?
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Study Notes
Threading Module
- The
threadingmodule in Python allows for the creation of multiple threads of execution, which can run concurrently. - Threads are lightweight and share the same memory space, making them suitable for I/O-bound tasks.
- The
threadingmodule provides the following classes:-
Thread: represents a single thread of execution. -
Lock: a synchronization primitive for protecting shared resources. -
RLock: a reentrant lock that can be acquired multiple times by the same thread. -
Semaphore: a counter that limits the number of threads accessing a shared resource. -
Condition: a synchronization primitive that allows threads to wait for a specific condition.
-
- Example of creating a thread:
import threading
def worker():
print("Thread is working")
t = threading.Thread(target=worker)
t.start()
Parallel Processing
- Parallel processing in Python is the ability to execute multiple tasks simultaneously, taking advantage of multiple CPU cores.
- Python's Global Interpreter Lock (GIL) prevents true parallel execution of threads, making it suitable for I/O-bound tasks only.
- For CPU-bound tasks, other approaches are needed:
-
multiprocessingmodule: uses multiple processes to achieve parallelism, bypassing the GIL. -
concurrent.futuresmodule: provides a high-level interface for parallelism using threads or processes. -
jobliblibrary: provides a simple way to parallelize loops and functions.
-
- Example of parallel processing using
multiprocessing:
import multiprocessing
def worker(num):
print("Worker", num)
if __name__ == '__main__':
processes = []
for i in range(5):
p = multiprocessing.Process(target=worker, args=(i,))
processes.append(p)
p.start()
- Example of parallel processing using
concurrent.futures:
import concurrent.futures
def worker(num):
print("Worker", num)
with concurrent.futures.ThreadPoolExecutor() as executor:
futures = [executor.submit(worker, i) for i in range(5)]
concurrent.futures.wait(futures)
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Description
Learn about Python's threading module and how to achieve parallel processing using multiple threads and processes. Understand the limitations of Python's Global Interpreter Lock and how to bypass it.
