Distributed Computing with Python: Harness the power of multiple computers using Python through this fast-paced informative guide
Language: English
Published by Packt Publishing, 2016
- Softcover
- New
Seller: California Books, Miami, FL, U.S.A.California Books
AbeBooks seller since October 27, 2023
Condition: New
£ 35.55
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Add to basketSeller Inventory # I-9781785889691
- Title
- Distributed Computing with Python: Harness the power of multiple computers using Python through this fast-paced informative guide
- Author
- Francesco Pierfederici
- Publisher
- Packt Publishing
- Publication year
- 2016
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1785889699
- ISBN 13
- 9781785889691
Harness the power of multiple computers using Python through this fast-paced informative guide
About This Book
- You'll learn to write data processing programs in Python that are highly available, reliable, and fault tolerant
- Make use of Amazon Web Services along with Python to establish a powerful remote computation system
- Train Python to handle data-intensive and resource hungry applications
Who This Book Is For
This book is for Python developers who have developed Python programs for data processing and now want to learn how to write fast, efficient programs that perform CPU-intensive data processing tasks.
What You Will Learn
- Get an introduction to parallel and distributed computing
- See synchronous and asynchronous programming
- Explore parallelism in Python
- Distributed application with Celery
- Python in the Cloud
- Python on an HPC cluster
- Test and debug distributed applications
In Detail
CPU-intensive data processing tasks have become crucial considering the complexity of the various big data applications that are used today. Reducing the CPU utilization per process is very important to improve the overall speed of applications.
This book will teach you how to perform parallel execution of computations by distributing them across multiple processors in a single machine, thus improving the overall performance of a big data processing task. We will cover synchronous and asynchronous models, shared memory and file systems, communication between various processes, synchronization, and more.
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About the Author
"About the title" may belong to another edition of this title.
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