Who is this book suitable for?

This book is suitable for intermediate to advanced Python developers, data scientists, or anyone working on performance-critical applications. Readers should have a good understanding of Python basics and be eager to explore advanced topics such as concurrent programming and GPU computing. Whether you work in academia, industry, or startups, this book can enhance your coding skills and improve project outcomes by optimizing performance.

What programming techniques can I learn from this book?

You will learn various programming techniques including multithreading, multiprocessing, and GPU acceleration using CUDA with Python. The book also covers frameworks like PyTorch to perform high-performance computations efficiently. By mastering these techniques, you'll be empowered to handle larger datasets, improve application responsiveness, and significantly reduce processing time, relevant for fields like AI and big data analytics.

Is prior experience with CUDA necessary to benefit from this book?

No prior experience with CUDA is required; the book begins with foundational concepts and gradually introduces you to CUDA programming in Python. It effectively guides you through installation, setup, and practical examples. This approach allows readers to build their skills step-by-step, making it accessible for developers transitioning into high-performance computing environments.

Are there practical examples included in the book?

Yes, the book is rich in practical examples demonstrating the implementation of parallel and concurrent programming concepts. Each chapter includes hands-on projects and code snippets that show real-world applications of the techniques discussed. This makes it easier to understand and apply the concepts in your own projects, enhancing learning through practical engagement.

Can this book help with machine learning applications?

Absolutely! The book emphasizes high-performance computing techniques relevant to machine learning. It teaches you how to leverage PyTorch for building neural networks efficiently and how to parallelize training workflows. This is crucial for anyone involved in developing machine learning applications, as optimizing these processes can lead to faster model training and better overall performance.

What tools and libraries will I learn to use?

You will learn to use several key tools and libraries, including Python's built-in threading and multiprocessing modules, CUDA for GPU programming, and PyTorch for deep learning applications. These tools are essential for implementing parallel computing strategies, allowing you to enhance the performance of your applications and handle intensive computational tasks with ease.

How does multithreading improve application performance?

Multithreading allows an application to perform multiple operations simultaneously, thereby improving performance by effectively utilizing CPU resources. This is especially useful for I/O-bound tasks or when waiting for user input. By implementing multithreading as taught in the book, you can create responsive applications that handle multiple tasks, leading to a better user experience.

What prerequisites should I have before reading this book?

You should have a strong grasp of Python basics, including functions, data structures, and object-oriented programming. Familiarity with basic programming concepts such as loops and conditionals is also beneficial. Although prior experience with parallel programming is not necessary, a willingness to learn and experiment with advanced topics will help you make the most of the material covered.

Where can I buy Parallel and High Performance Programming with Python?

You can purchase Parallel and High Performance Programming with Python through Ubuy in Bosnia and Herzegovina. Ubuy offers a convenient online shopping experience, ensuring that you receive your copy promptly. By choosing Ubuy, you also have access to a wide range of resources and reviews to help you make informed purchasing decisions.

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Parallel and High Performance Programming with Python: Unlock parallel and concurrent programming in Python using multithreading, CUDA, Pytorch and ... Programming Parallel Systems Path)

Unleash the capabilities of Python and its libraries for solving high performance computational problems.

Artikal br.: 86915013

Parallel and High Performance Programming with Python: Unlock parallel and concurrent programming in Python using multithreading, CUDA, Pytorch and

Q: What is Parallel and High Performance Programming with Python?

Parallel and High Performance Programming with Python is a comprehensive guide that helps developers leverage Python's capabilities in parallel and concurrent programming. It delves into utilizing multithreading, CUDA, and libraries like PyTorch to optimize performance. This book is particularly beneficial for those looking to speed up processing tasks and efficiently manage multiple threads, essential for data science, machine learning, and real-time applications.

88% of respondents would recommend this to a friend

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What Stands Out

Multithreading Insights

Gain essential skills in multithreading to optimize performance. This book offers practical examples to enhance your Python applications, making them faster and more efficient.

CUDA Integration

Utilize CUDA with Python for high-performance computations. Unlock the power of GPUs, enabling complex data processing and machine learning enhancements in realistic settings.

Dask Utilization

Learn to leverage Dask for parallel computing in Python. This feature provides scalable and efficient solutions, ideal for handling large datasets and enhancing workflow tremendously.

Detalji o proizvodu

Unleash the capabilities of Python and its libraries for solving high performance computational problems.Key Features ● Explores parallel programming concepts and techniques for high-performance computing.● Covers parallel algorithms, multiprocessing, distributed computing, and GPU programming.● Provides practical use of popular Python libraries/tools like NumPy, Pandas, Dask, and TensorFlow.Book DescriptionThis book will teach you everything about the powerful techniques and applications of parallel computing, from the basics of parallel programming to the cutting-edge innovations shaping the future of computing.The book starts with an introduction to parallel programming and the different types of parallelism, including parallel programming with threads and processes. The book then delves into asynchronous programming, distributed Python, and GPU programming with Python, providing you with the tools you need to optimize your programs for distributed and high-performance computing.The book also covers a wide range of applications for parallel computing, including data science, artificial intelligence, and other complex scientific simulations. You will learn about the challenges and opportunities presented by parallel computing for these applications and how to overcome them.By the end of the book, you will have insights into the future of parallel computing, the latest research and developments in the field, and explore the exciting possibilities that lie ahead.What you will learn● Build faster, smarter, and more efficient applications for data analysis, machine learning, and scientific computing● Implement parallel algorithms in Python● Best practices for designing, implementing, and scaling parallel programs in PythonWho is this book for?This book is aimed at software developers who wish to take their careers to the next level by improving their skills and learning about concurrent and parallel programming. It is also intended for Python developers who aspire to write fast and efficient programs, and for students who wish to learn the fundamentals of parallel computing and its practical uses.Table of Contents1. Introduction to Parallel Programming2. Building Multithreaded Programs3. Working with Multiprocessing and mpi4py Library4. Asynchronous Programming with AsyncIO5. Realizing Parallelism with Distributed Systems6. Maximizing Performance with GPU Programming using CUDA7. Embracing the Parallel Computing Revolution8. Scaling Your Data Science Applications with Dask9. Exploring the Potential of AI with Parallel Computing10. Hands-on Applications of Parallel ComputingAbout the AuthorFabio Nelli holds a Master's Degree in Chemistry and a Bachelor's Degree in IT and Automation Engineering. He currently works at various research institutes and private companies, where he delivers educational courses on data analysis and data visualization technologies. He contributes to writing articles on the web and writes in-depth books on the subject.

Publisher	AVA
Publication date	April 14, 2023
Language	English
Print length	392 pages
ISBN-10	9388590732
ISBN-13	978-9388590730
Item Weight	1.49 pounds (680 grams)
Dimensions	7.5 x 0.89 x 9.25 inches (19.1 x 2.3 x 23.5 cm)

Who Should Buy?

Python Developers
Ideal for Python developers seeking to enhance their skills in parallel and concurrent programming techniques.
Data Scientists
Useful for data scientists wanting to leverage powerful libraries like CUDA and Pytorch for data processing and analysis.
Students & Learners
Excellent for students in computer science or related fields looking to grasp efficient programming concepts.

Beginners in Programming
Not suitable for absolute beginners unfamiliar with programming concepts and Python basics.

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Pitanje: Je li Parallel and High Performance Programming with dostupan za online kupovinu u Bosnia and Herzegovina?

Odgovor: Da, u Ubuy Bosnia and Herzegovina ovaj proizvod je dostupan za kupovinu po razumnoj cijeni.. Parallel and High Performance Programming with nije dostupan lokalno, ali možete nam povjeriti naše usluge ekspresne dostave.
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Cloud Computing Editorial Review

Parallel and High Performance Programming with Python: Unlock parallel and concurrent programming in Python using multithreading, CUDA, Pytorch and ... Programming Parallel Systems Path offers an extensive exploration of advanced programming techniques in Python. This comprehensive guide spans 392 pages, providing insights into multithreading and utilizing technologies like CUDA and Pytorch for high-performance computing. With its recent publication date of April 14, 2023, the book is relevant for developers eager to enhance their programming skills in parallel systems. Readers have found the content engaging and informative, making it an excellent choice for those looking to deepen their understanding of performance programming in Python.

Customer Reviews & Ratings

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Pros

Comprehensive guide with 392 pages of content
Covers multithreading and CUDA programming
Relevant for both beginners and advanced users
Engaging and informative writing style
Publisher AVA is known for quality technical books

Cons

Could benefit from more practical examples

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Learn parallel and concurrent programming with Python.
Explore high-performance computing techniques and algorithms.
Practical applications using popular libraries like NumPy and TensorFlow.
Ideal for software developers aiming to enhance their skills.
Covers key areas such as multiprocessing and distributed computing.
Gain insights into the future of parallel computing.

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