It is interesting to note that neither NVIDIA or AMD identify itself as a GPU manufacturing company. In fact, NVIDIA identifies itself as an AI computing company, whereas AMD calls itself a semiconductor company. In the first chapter, we looked at various examples in diverse research areas to understand how GPUs empower science and AI. Let's revisit that perspective in terms of GPU-accelerated Python for AI computing. Through three specific examples in AI research, we’ll explore different endeavors to perform computational problem solving by deploying AI.
-
Book Overview & Buying
-
Table Of Contents
Hands-On GPU Computing with Python
By :
Hands-On GPU Computing with Python
By:
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)
Preface
Free Chapter
Section 1: Computing with GPUs Introduction, Fundamental Concepts, and Hardware
Introducing GPU Computing
Designing a GPU Computing Strategy
Setting Up a GPU Computing Platform with NVIDIA and AMD
Section 2: Hands-On Development with GPU Programming
Fundamentals of GPU Programming
Setting Up Your Environment for GPU Programming
Working with CUDA and PyCUDA
Working with ROCm and PyOpenCL
Working with Anaconda, CuPy, and Numba for GPUs
Section 3: Containerization and Machine Learning with GPU-Powered Python
Containerization on GPU-Enabled Platforms
Accelerated Machine Learning on GPUs
GPU Acceleration for Scientific Applications Using DeepChem
Other Books You May Enjoy
