Hands-On GPU Computing with Python: Explore the capabilities of GPUs for solving high performance computational problems

Book Description

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 will start with introducing GPU computing and explain the architecture and programming models for GPUs. You will learn, by example, how to perform GPU programming with Python, and you’ll look at using integrations such as PyCUDA, PyOpenCL, CuPy and Numba with Anaconda for various tasks such as machine learning and data mining. Going further, you will get to grips with GPU work flows, 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.

What you will learn

• Utilize Python libraries and frameworks for GPU acceleration
• Set up a GPU-enabled programmable machine learning environment on your system with Anaconda
• Deploy your machine learning system on cloud containers with illustrated examples
• Explore PyCUDA and PyOpenCL and compare them with platforms such as CUDA, OpenCL and ROCm.
• Perform data mining tasks with machine learning models on GPUs
• Extend your knowledge of GPU computing in scientific applications

Who this book is for

Data Scientist, Machine Learning enthusiasts and professionals who wants to get started with GPU computation and perform the complex tasks with low-latency. Intermediate knowledge of Python programming is assumed.

Table of Contents

1. Introduction to GPU computing
2. Designing A GPU Computing Strategy
3. Setting up a GPU Computing Platform with NVIDIA and AMD
4. Fundamentals of GPU programming
5. Setting up your environment for GPU programming
6. Working with PyCUDA
7. Working with PyOpenCL
8. Working with Anaconda and Anaconda Accelerate
9. Containerization on GPU enabled platforms
10. Machine Learning on GPUs: Use cases
11. GPU Acceleration for Scientific Applications using Deepchem