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Parallel Programming with Python

Parallel Programming with Python

By : Palach
3 (9)
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Parallel Programming with Python

Parallel Programming with Python

3 (9)
By: Palach

Overview of this book

Starting with the basics of parallel programming, you will proceed to learn about how to build parallel algorithms and their implementation. You will then gain the expertise to evaluate problem domains, identify if a particular problem can be parallelized, and how to use the Threading and Multiprocessor modules in Python. The Python Parallel (PP) module, which is another mechanism for parallel programming, is covered in depth to help you optimize the usage of PP. You will also delve into using Celery to perform distributed tasks efficiently and easily. Furthermore, you will learn about asynchronous I/O using the asyncio module. Finally, by the end of this book you will acquire an in-depth understanding about what the Python language has to offer in terms of built-in and external modules for an effective implementation of Parallel Programming. This is a definitive guide that will teach you everything you need to know to develop and maintain high-performance parallel computing systems using the feature-rich Python.
Table of Contents (10 chapters)
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9
Index

Taking care of Python GIL

GIL is a mechanism that is used in implementing standard Python, known as CPython, to avoid bytecodes that are executed simultaneously by different threads. The existence of GIL in Python is a reason for fiery discussion amongst users of this language. GIL was chosen to protect the internal memory used by the CPython interpreter, which does not implement mechanisms of synchronization for the concurrent access by threads. In any case, GIL results in a problem when we decide to use threads, and these tend to be CPU-bound. I/O Threads, for example, are out of GIL's scope. Maybe the mechanism brings more benefits to the evolution of Python than harm to it. Evidently, we could not consider only speed as a single argument to determine whether something is good or not.

There are cases in which the approach to the use of processes for tasks sided with message passing brings better relations among maintainability, scalability, and performance. Even so, there are cases in which there will be a real need for threads, which would be subdued to GIL. In these cases, what could be done is write such pieces of code as extensions in C language, and embed them into the Python program. Thus, there are alternatives; it is up to the developer to analyze the real necessity. So, there comes the question: is GIL, in a general way, a villain? It is important to remember that, the PyPy team is working on an STM implementation in order to remove GIL from Python. For more details about the project, visit http://pypy.org/tmdonate.html.

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