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

Comparing CuPy to NumPy and CUDA

Let's compare CuPy to NumPy and CUDA in terms of simplicity in parallelization. In the following table, we explore the scope of CuPy with respect to NumPy and CUDA so as to understand the scenarios when CuPy could be advantageous to both. Here are some of the differences:

CUDA

NumPy

CuPy

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.

Fundamental package for scientific computing with Python on conventional CPUs.

Uses NumPy syntax but can be used for GPUs.

Casting behaviors from float to integer are defined in CUDA specification.

Casting behaviors from float to integer are defined in C++ specification.

Casting behaviors from float to integer are not defined in C++ specification...