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You're reading from Hands-On GPU Computing with Python Explore the capabilities of GPUs for solving high performance computational problems

Product type Paperback

Published in May 2019

Publisher Packt

ISBN-13 9781789341072

Length 452 pages

Edition 1st Edition

Languages

Python

Tools

CUDA

Concepts

Data Processing

Author (1):

Avimanyu Bandyopadhyay

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Table of Contents (17) Chapters

Preface

1. Section 1: Computing with GPUs Introduction, Fundamental Concepts, and Hardware FREE CHAPTER

2. Introducing GPU Computing

3. Designing a GPU Computing Strategy

4. Setting Up a GPU Computing Platform with NVIDIA and AMD

5. Section 2: Hands-On Development with GPU Programming

6. Fundamentals of GPU Programming

7. Setting Up Your Environment for GPU Programming

8. Working with CUDA and PyCUDA

9. Working with ROCm and PyOpenCL

10. Working with Anaconda, CuPy, and Numba for GPUs

11. Section 3: Containerization and Machine Learning with GPU-Powered Python

12. Containerization on GPU-Enabled Platforms

13. Accelerated Machine Learning on GPUs

14. GPU Acceleration for Scientific Applications Using DeepChem

15. Other Books You May Enjoy

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Appendix A

Comparing Numba to NumPy, ROCm, and CUDA

Let's now compare Numba to NumPy, ROCm, and CUDA in terms of simplicity in parallelization. In the following table, we explore the scope of Numba with respect to NumPy, ROCm, and CUDA to understand the scenarios when Numba could be advantageous to both. Some of the differences are as follows:

CUDA	ROCm	NumPy	Numba
Based on C/C++ programming language.	Based on C/C++ programming language.	Based on Python programming language.	Based on Python programming language.
Uses C/C++ combined with specialized code to accelerate computations.	Uses C/C++ combined with specialized code to accelerate computations for HCC and HIP.	Fundamental package for scientific computing with Python on conventional CPUs.	Natively understands NumPy arrays, shapes, and dtypes and can index a NumPy array without relying on Python (close...

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Authors (1)

Avimanyu Bandyopadhyay

Avimanyu Bandyopadhyay is currently pursuing a PhD degree in Bioinformatics based on applied GPU computing in Computational Biology at Heritage Institute of Technology, Kolkata, India. Since 2014, he developed a keen interest in GPU computing, and used CUDA for his master's thesis. He has experience as a systems administrator as well, particularly on the Linux platform. Avimanyu is also a scientific writer, technology communicator, and a passionate gamer. He has published technical writing on open source computing and has actively participated in NVIDIA's GPU computing conferences since 2016. A big-time Linux fan, he strongly believes in the significance of Linux and an open source approach in scientific research. Deep learning with GPUs is his new passion!

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