In this recipe, we will verify the impact of the GIL, evaluating the performance of a multithread application. The GIL, as described in the previous chapter, is the lock introduced by the CPython interpreter. The GIL prevents parallel execution of multiple threads in the interpreter. Before being executed each thread must wait for the GIL to release the thread that is running. In fact, the interpreter forces the executing thread to acquire the GIL before it accesses anything on the interpreter itself as the stack and instances of Python objects. This is precisely the purpose of GIL—it prevents concurrent access to Python objects from different threads. The GIL then protects the memory of the interpreter and makes the garbage work in the right manner. The fact is that the GIL prevents the programmer from improving the performance by executing threads in parallel. If we remove the GIL from the CPython interpreter, the threads would be...
Python Parallel Programming Cookbook
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Python Parallel Programming Cookbook
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Overview of this book
This book will teach you parallel programming techniques using examples in Python and will help you explore the many ways in which you can write code that allows more than one process to happen at once. Starting with introducing you to the world of parallel computing, it moves on to cover the fundamentals in Python. This is followed by exploring the thread-based parallelism model using the Python threading module by synchronizing threads and using locks, mutex, semaphores queues, GIL, and the thread pool.
Next you will be taught about process-based parallelism where you will synchronize processes using message passing along with learning about the performance of MPI Python Modules. You will then go on to learn the asynchronous parallel programming model using the Python asyncio module along with handling exceptions. Moving on, you will discover distributed computing with Python, and learn how to install a broker, use Celery Python Module, and create a worker.
You will understand anche Pycsp, the Scoop framework, and disk modules in Python. Further on, you will learnGPU programming withPython using the PyCUDA module along with evaluating performance limitations.
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Table of Contents (13 chapters)
Python Parallel Programming Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Getting Started with Parallel Computing and Python
Thread-based Parallelism
Process-based Parallelism
Asynchronous Programming
Distributed Python
GPU Programming with Python
Index
Customer Reviews