Parallel and High Performance Programming with Python, Nelli F., 2026

Parallel and High Performance Programming with Python, Nelli F., 2026.

   This second edition of Parallel and High Performance Programming with Python was created to reflect how parallel and high-performance computing is practiced today. It introduces updated approaches, real-world examples, and thoughtful comparisons to the original structure. Six new chapters address practical needs that emerged over time, including cloud and serverless computing for elastic scaling and cost-efficient execution.
The book presents reusable patterns such as fan-out/fan-in, strategic caching, and idempotency, alongside production-focused checklists covering observability, cost control, and data sensitivity. It also clearly explains real-world limitations, including the GIL, I/O-bound workloads, shuffle costs, cold starts, serialization overhead, and memory constraints.




Python Thread Problem: The GIL.
Threads in Python cannot be executed simultaneously on two different cores. The fact is closely linked to the Python interpreter itself. In fact, the interpreter on which Python code has always been running was originally implemented in CPython. During its implementation, it was realized that the interpreter was not fully thread-safe. That is, when more threads tried to access a certain object in common (the memory is shared between the threads), the interpreter often ran into a state of inconsistency due to the phenomenon of the race condition. To avoid this huge problem, the Global Interpreter Lock (GIL) has now been included within the interpreter. The Python designers therefore decided that within a process, only one thread can be executed at a time, eliminating the parallelism of this type of entity (no multithreading).

GIL is only acquired by one thread at a time while all the other threads wait. As soon as the thread has finished its task, the GIL is released and is acquired by the next thread. Therefore, a real concurrent execution takes place. Concurrent programs are generally less costly in terms of resources than parallel programs, as creating new processes is much more expensive than creating threads. It should be considered, however, that the operations of acquisition and release of the lock slow down the execution of the entire program.

Contents.
Cover Page.
Title Page Copyright Page Dedication Page About the Author.
About the Technical Reviewer.
Acknowledgements.
Preface.
Get a Free eBook Errata.
Table of Contents.
1. Introduction to Parallel Programming.
2. Parallel Programming with Threads.
3. Parallel Programming with Processes.
4. Asynchronous Programming.
5. Distributed Python.
6. GPU Programming with Python.
7. Parallel Computing Applications.
8. Parallel Computing for Data Science.
9. Parallel Computing for Artificial Intelligence.
10. Future of Parallel Programming.
11. Modern Frameworks for Parallelism and Distribution: Ray and Modin.
12. PySpark.
13. Joblib.
14. Parallelization on Cloud and Serverless Systems.
15. Parallel Programming with FPGAs.
16. Introduction to Quantum Computing and Quantum Architectures.
Index.



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