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Hands-On GPU Computing with Python

Hands-On GPU Computing with Python

By : Avimanyu Bandyopadhyay
2 (1)
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Hands-On GPU Computing with Python

Hands-On GPU Computing with Python

2 (1)
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)
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Section 1: Computing with GPUs Introduction, Fundamental Concepts, and Hardware
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Section 2: Hands-On Development with GPU Programming
11
Section 3: Containerization and Machine Learning with GPU-Powered Python

Working with ROCm and PyOpenCL

In this chapter, we will continue with our hands-on experience with PyOpenCL. We will follow our CUDA program example that we discussed in the previous chapter and will try to make it a cross-platform venture with ROCm's HIP. In order to do this, we will use the hipify tool that we learned to build in Chapter 4, Fundamentals of GPU Programming. With hipify, we will try to create .cpp files from .cu files. With a practical approach, we will see the entire process of converting CUDA code into HIP code.

C programming enthusiasts will be encouraged to invoke AMD and NVIDIA GPUs within their program code with HIP, while Python programming enthusiasts will be motivated to use PyOpenCL to invoke AMD and NVIDIA GPUs within their program code. We will first start with the understanding of how a ROCm HIP-C program works. The fundamental concepts behind...

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