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Hands-On GPU Computing with Python

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

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789341072
Length 452 pages
Edition 1st Edition
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Avimanyu Bandyopadhyay Avimanyu Bandyopadhyay
Author Profile Icon Avimanyu Bandyopadhyay
Avimanyu Bandyopadhyay
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Computing with GPUs Introduction, Fundamental Concepts, and Hardware
2. Introducing GPU Computing FREE CHAPTER 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 Appendix A

Comparing PyOpenCL to HIP and OpenCL – revisiting the reduction perspective

As in the previous chapter, let's compare PyOpenCL to HIP and OpenCL in terms of simplicity in parallelization before we write our first PyCUDA programs on PyCharm.

In the following table, we are exploring the scope of PyOpenCL with respect to HIP and OpenCL to note when to prefer PyOpenCL over OpenCL:

HIP

OpenCL

PyOpenCL

Based on C/C++ programming language.

Based on C/C++ programming language.

Based on Python programming language.

Includes both device and host code in a single .c or .cpp file.

Requires creation of a separate .cl file for device code along with a .c or .cpp file that calls the same code from the host for parallelization.

Includes both device and host code in a single .py file.

Uses C/C++ combined with specialized code to accelerate computations.

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