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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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Author (1):
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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

Useful exercise on computational problem solving

You must have heard about the Body Mass Index (BMI). It is very useful for monitoring a person's health, especially in the cases of obesity and diabetes. You might have already undergone a process to determine your BMI during a routine medical checkup. BMI can also be quite significant in understanding other medical conditions. It is calculated as follows:

So, we divide the person's weight in kilograms by their squared height (in meters).

Now, suppose we have a database of millions of people with a record of their weights, along with their corresponding heights. Through GPU-accelerated computing, we can conveniently deduce the average BMI in a particular region.

Before you try to computationally solve the problem, remember to follow the four steps we discussed earlier for computing the solution.

So, our first dataset...

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