0 ratings
Hands-On GPU Programming with Python and CUDA: Explore high-performance parallel computing with CUDA
genuine image
Hands-On GPU Programming with Python and CUDA: Explore high-performance parallel computing with CUDA
Dr. Brian Tuomanen is an expert in CUDA and General-Purpose GPU Programming
Hands-On GPU Programming with Python and CUDA: Explore high-performance parallel computing with CUDA
Item #: 18642590

Hands-On GPU Programming with Python and CUDA: Explore high-performance parallel computing with CUDA

Item #: 18642590

CAD 98

Price Details

Excluding Shipping & Custom charges ( Shipping and custom charges will be calculated on checkout )

*All items will import from US

0 ratings Write a review
In stock
us Imported from USA store

QTY:

Order now and get it around Thursday, June 25
Secured transactionsecured transaction

Ubuy works hard to protect your security and privacy. Our advanced payment security system ensures confidentiality by encrypting your information during transmission using AES (Advanced Encryption Standards) and SSL (Secure Socket Layer) protocols. Your payment details are 100% secure as we do not share your payment details with third party sellers.

Our Top Logistics Partners
  • fedex
  • dhl
Dr. Brian Tuomanen is an expert in CUDA and General-Purpose GPU Programming
U-Care Warranty:
None
Select a Plan
buy now pay later

Buy Now Pay Later

fast shipping

Fast
Shipping

free return

Free
Return*

secure packaging

Secure Packaging

100% original products

100% Original Products

pci-dss

PCI DSS Compliance

iso certified

ISO 27001 Certified

paypal payment
afterpay payment
visa payment
mastercard payment

What Stands Out

Practical Approach
Provides hands-on projects enabling learners to apply GPU programming concepts immediately, ensuring better understanding and retention of Python and CUDA programming techniques.
Comprehensive Resources
Offers extensive resources, including code examples and case studies, making complex GPU programming more accessible for both beginners and experienced developers alike.
Expert Insights
Written by industry professionals, the book features insights and best practices from real-world applications, helping readers to stay updated with the latest trends in GPU programming.

Product Details

Shop Hands-On GPU Programming with Python and CUDA: Explore high-performance parallel computing with CUDA online at a best price in Canada. 1788993918
  • Written by Dr. Brian Tuomanen, an expert in CUDA and General-Purpose GPU Programming
  • The author has been working with CUDA since 2014 and has a Ph.D. in Mathematics
  • Explores high-performance parallel computing using Python and CUDA
  • Includes hands-on exercises to help readers learn and apply the concepts
  • Suitable for those interested in GPU programming and studying scientific problems
  • Provides insight into GPU integration and development for real-world applications
Item Weight1.2 lbs (540 grams)

Who Should Buy?

Suitable For
  • Data Scientists

    Ideal for data scientists looking to accelerate their computations and implement machine learning algorithms using GPU processing.

  • Students Learning

    Perfect for students who want to learn GPU programming and apply concepts using practical Python examples in CUDA.

  • AI Researchers

    Beneficial for AI researchers focused on optimizing deep learning models through the efficient use of GPU architectures.

Not Suitable For
  • Beginners in Programming

    Not suitable for complete programming novices without prior experience in Python or understanding of GPU concepts.

  • Casual Users

    Not recommended for casual users looking for basic computing solutions without the need for advanced programming skills.

  • Non-Technical Managers

    Unsuitable for managers or professionals who do not require hands-on programming or technical details involving GPU frameworks.

Product Description

Hands-On GPU Programming with Python and CUDA: Explore high-performance parallel computing with CUDA

Have any Query? Chat with us

Product Buying Guide

Hands-On GPU Programming with Python and CUDA is an essential guide for developers and data scientists seeking to leverage the power of Python and CUDA for high-performance parallel computing. This book provides in-depth knowledge and practical insights into GPU programming with Python, CUDA, and open-source libraries, making it an indispensable resource for those aiming to enhance their application's performance and efficiency.

Key Features

  • Detailed insight into GPU programming tools such as PyCUDA, scikit-cuda, and Nsight
  • Exploration of CUDA libraries including cuBLAS, cuFFT, and cuSolver
  • Application of GPU programming to modern data science scenarios

Usage Scenarios

  • Developing high-performing Python applications with GPU acceleration
  • Implementing GPU-based deep neural networks and exploring advanced GPU hardware features
  • Applying GPU programming to solve data science problems and improve computational efficiency

Usage Scenarios

This book competes with similar publications that focus on GPU programming with Python and CUDA, offering a comprehensive and practical approach to GPU-accelerated high-performance computing.

Some User Review

  • Readers appreciated the practical insights and hands-on approach to GPU programming using Python and CUDA.
  • The comprehensive coverage of CUDA libraries and practical examples received positive feedback from users.
  • Many found the book helpful for enhancing their understanding of GPU-based deep learning and data science applications.

Competitors

Hands-On GPU Programming with Python and CUDA offers excellent value considering its comprehensive coverage of GPU programming with Python and CUDA, making it a worthy investment for developers and data scientists who are looking to harness the power of GPU acceleration.

Buying Considerations

  • Consider the edition and format based on your preference for paperback or digital format.
  • Evaluate your current knowledge of Python and CUDA programming to ensure it aligns with the book's target audience.
  • Assess how the book's content maps to your learning goals and practical applications within your domain.

Conclusion

With its in-depth coverage of GPU programming tools, practical examples, and application scenarios, Hands-On GPU Programming with Python and CUDA is an invaluable resource for developers and data scientists aiming to optimize their Python applications through GPU acceleration and high-performance computing.

View Less

Hands-On GPU Programming with Python and CUDA is an essential guide for developers and data scientists seeking to leverage the power of Python and CUDA for high-performance parallel computing. This book provides in-depth knowledge and practical insights into GPU programming with Python, CUDA, and open-source libraries, making it an indispensable resource for those aiming to enhance their application's performance and efficiency. Continue Reading

Customer Questions & Answers

  • Question: What prerequisites do I need to start with Hands-On GPU Programming with Python and CUDA?

    Answer: To effectively engage with Hands-On GPU Programming with Python and CUDA, having a background in Python programming and a basic understanding of computer architecture is essential. Familiarity with linear algebra concepts can also enhance your learning experience, as these concepts are often applied in GPU programming. This foundational knowledge will help you grasp the intricacies of parallel programming techniques and how to optimize code for better performance on GPU architectures.
  • Question: Is this book suitable for beginners in GPU programming?

    Answer: Yes, this book is suitable for beginners who have a basic Python programming knowledge. The content is structured to guide readers through fundamental concepts of GPU programming and CUDA. It introduces core principles and gradually builds upon them, with practical examples to reinforce learning, making it an excellent choice for those starting out and looking to deepen their understanding of GPU technology.
  • Question: What topics are covered in this book?

    Answer: Hands-On GPU Programming with Python and CUDA covers a wide range of topics including the fundamentals of GPU architecture, setting up the CUDA development environment, parallel programming concepts, optimization techniques, and hands-on projects. Additionally, it dives into real-world applications of GPU programming in fields such as deep learning, computer vision, and scientific computing, enabling readers to apply their skills in various practical scenarios.
  • Question: How does this book facilitate hands-on learning?

    Answer: The book is designed with a hands-on approach, featuring coding examples, exercises, and projects that encourage practical implementation. Readers can follow along with the examples while working through exercises that reinforce their understanding. This interactive learning style enables learners to not only read and comprehend theoretical aspects but also directly apply their knowledge by coding real GPU-accelerated applications.
  • Question: Can I use this book for self-study?

    Answer: Absolutely! This book is specifically crafted for self-study, allowing learners to progress at their own pace. With clear explanations, practical examples, and review questions at the end of each chapter, independent learners can effectively grasp GPU programming concepts. Whether you're coming from a software development background or are new to the field, the structure facilitates a self-contained educational experience.
  • Question: What programming languages are primarily used in the book?

    Answer: The primary programming language used in this book is Python, along with CUDA for GPU programming. The combination allows readers to leverage Python’s simplicity and flexibility while harnessing the power of CUDA to optimize performance. This dual-language approach caters to Python developers who want to step into the world of GPU programming, making it accessible without overwhelming newcomers with complex syntax.
  • Question: Are there any online resources or communities associated with this book?

    Answer: Yes, readers can find online resources such as forums, tutorials, and relevant coding communities that discuss GPU programming and CUDA techniques. Joining platforms like GitHub or Stack Overflow can provide additional support and exchange ideas with peers. Engaging with these communities enhances the learning experience with real-world discussions and additional project opportunities that relate to the book's content.
  • Question: What kind of projects can I expect to create after reading this book?

    Answer: After completing the book, readers can expect to create various projects that leverage GPU acceleration. This includes applications in machine learning, real-time image processing, or scientific simulations. By applying the knowledge gained from the book, you'll be able to tackle projects that require intensive computation and can produce significant performance improvements by utilizing GPU capabilities.
  • Question: Can this book help me in my career in data science or AI?

    Answer: Definitely! With the increasing reliance on GPU acceleration in data science and artificial intelligence, knowledge of GPU programming is a valuable asset. This book equips you with essential skills to implement machine learning models and optimize data processing tasks, enhancing your resume. Proficiency in these areas is in high demand, making you a more competitive candidate in technology-focused roles.
  • Question: Where can I buy Hands-On GPU Programming with Python and CUDA in Canada?

    Answer: You can buy Hands-On GPU Programming with Python and CUDA at Ubuy. Ubuy offers a seamless shopping experience, providing access to various educational resources, including programming books, through an intuitive platform. Browse Ubuy to find this title and expand your library with essential programming knowledge.

Parallel Programming Editorial Review

**Editorial Review** "Hands-On GPU Programming with Python and CUDA" has been well-received by its users, particularly for its practical application and comprehensive coverage of GPU programming. Reviewers Consistently emphasize the book's clarity and thoroughness, making it an invaluable resource for both beginner programmers and seasoned engineers. The step-by-step approach allows users to seamlessly implement high-performance heterogeneous computing programs in Python, thereby enhancing productivity and illustrating the power of GPU acceleration without compromising the user-friendly nature of Python's development environment. Many users noted that the book utilizes PyCUDA, which facilitates programming on platforms such as Google Colab with GPU runtimes—an attractive feature for those who might not have access to NVIDIA GPUs. While the current code is predominantly in Python 2, several reviewers acknowledged the need for adaptation to Python 3, along with existing resources to assist with this transition. The author's commitment to customer support has also been highlighted positively, particularly when a reviewer reached out for help while waiting for an updated edition. This responsiveness adds a personal touch that fosters a sense of community among readers. Importantly, beyond the practical implementation of code, the book delves into the theoretical Constructs of CUDA C and the principles of massively parallelized systems design, appealing to a wide range of readers, including researchers and application developers. Overall, the book has been lauded as an essential guide for anyone aiming to enhance performance in GPU-centric applications or gain a deep understanding of GPU programming concepts. **

Customer Reviews & Ratings

5.0
1 customers ratings
  • 5 Star
    100%
  • 4 Star
    0%
  • 3 Star
    0%
  • 2 Star
    0%
  • 1 Star
    0%

Review this product

Share your thoughts with other customers

Pros

  • Comprehensive and clear explanations of GPU programming fundamentals.
  • Practical hands-on coding examples using PyCUDA.
  • Suitable for both beginners and experienced programmers.
  • Excellent support and communication from the author.
  • Ideal for maximizing performance in GPU applications.

Cons

  • Primarily written in Python 2, which may require users to adapt code for Python 3.

Product Price History

Important information

  • Limitations : For products shipped internationally, please note that any manufacturer warranty may not be valid; manufacturer service options may not be available; product manuals, instructions, and safety warnings may not be in destination country languages; the products (and accompanying materials) may not be designed in accordance with destination country standards, specifications, and labeling requirements; and the products may not conform to destination country voltage and other electrical standards (requiring use of an adapter or converter if appropriate). The recipient is responsible for assuring that the product can be lawfully imported to the destination country. When ordering from Ubuy or its affiliates, the recipient is the importer of record and must comply with all laws and regulations of the destination country.
  • Not all the products listed on Ubuy are for sale, as Ubuy is a global search engine. Products are subject to export/trade regulations.

CAD 98

Order now and get it around Thursday, June 25

QTY:

Secured transactionsecured transaction

Ubuy works hard to protect your security and privacy. Our advanced payment security system ensures confidentiality by encrypting your information during transmission using AES (Advanced Encryption Standards) and SSL (Secure Socket Layer) protocols. Your payment details are 100% secure as we do not share your payment details with third party sellers.

Our Top Logistics Partners
  • fedex
  • dhl
Fastest cross-border delivery

Features & Benefits

  • Bachelor of Science in Electrical Engineering from the University of Washington
  • Completed his PhD in Mathematics at the University of Missouri
  • Has spoken at the US Army Research Lab about GPU programming
  • Lead GPU integration and development at a Maryland based startup
  • Currently lives and works in the Seattle area