Book Image

Hands-On GPU Computing with Python

By : Avimanyu Bandyopadhyay
Book Image

Hands-On GPU Computing with Python

By: Avimanyu Bandyopadhyay

Overview of this book

GPUs are proving to be excellent general purpose-parallel computing solutions for high-performance tasks such as deep learning and scientific computing. This book will be your guide to getting started with GPU computing. It begins by introducing GPU computing and explaining the GPU architecture and programming models. You will learn, by example, how to perform GPU programming with Python, and look at using integrations such as PyCUDA, PyOpenCL, CuPy, and Numba with Anaconda for various tasks such as machine learning and data mining. In addition to this, you will get to grips with GPU workflows, management, and deployment using modern containerization solutions. Toward the end of the book, you will get familiar with the principles of distributed computing for training machine learning models and enhancing efficiency and performance. By the end of this book, you will be able to set up a GPU ecosystem for running complex applications and data models that demand great processing capabilities, and be able to efficiently manage memory to compute your application effectively and quickly.
Table of Contents (17 chapters)
Free Chapter
1
Section 1: Computing with GPUs Introduction, Fundamental Concepts, and Hardware
5
Section 2: Hands-On Development with GPU Programming
11
Section 3: Containerization and Machine Learning with GPU-Powered Python

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...