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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

Section 1: Computing with GPUs Introduction, Fundamental Concepts, and Hardware

Section 1: Computing with GPUs Introduction, Fundamental Concepts, and Hardware

Introducing GPU Computing

Introducing GPU Computing

The world of GPU computing beyond PC gaming

Conventional CPU computing – before the advent of GPUs

How the gaming industry made GPU computing affordable for individuals

The emergence of full-fledged GPU computing

The simplicity of Python code and the power of GPUs – a dual advantage

How GPUs empower science and AI in current times

The social impact of GPUs

Further reading

Designing a GPU Computing Strategy

Designing a GPU Computing Strategy

Getting started with the hardware

Building your first GPU-enabled parallel computer – minimum system requirements

Liquid cooling – should you consider it?

Branded GPU-enabled PCs

Entry-level budget

Mid-range budget

High-end budget

Further reading

Setting Up a GPU Computing Platform with NVIDIA and AMD

Setting Up a GPU Computing Platform with NVIDIA and AMD

GPU manufacturers

Computing on NVIDIA GPUs

Computing on AMD APUs and GPUs

Comparing GPU programmable platforms on NVIDIA and AMD

Current models from both brands that are ideal for GPU computing

An enthusiast's guide to GPU computing hardware

Further reading

Section 2: Hands-On Development with GPU Programming

Section 2: Hands-On Development with GPU Programming

Fundamentals of GPU Programming

Fundamentals of GPU Programming

GPU-programmable platforms

Basic CUDA concepts

Basic ROCm concepts

The Anaconda Python distribution for package management and deployment

GPU-enabled Python programming

Further reading

Setting Up Your Environment for GPU Programming

Setting Up Your Environment for GPU Programming

Choosing a suitable IDE for your Python code

PyCharm – an IDE exclusively made for Python

Installing PyCharm

Alternative IDEs for Python – PyDev and Jupyter

Installing the PyDev Python IDE for Eclipse

Installing Jupyter Notebook and Jupyter Lab

Further reading

Working with CUDA and PyCUDA

Working with CUDA and PyCUDA

Technical requirements

Understanding how CUDA-C/C++ works via a simple example

Installing PyCUDA for Python within an existing CUDA environment

Configuring PyCUDA on your Python IDE

How computing in PyCUDA works on Python

Comparing PyCUDA to CUDA – an introductory perspective on reduction

Writing your first PyCUDA programs to compute a general-purpose solution

Useful exercise on computational problem solving

Further reading

Working with ROCm and PyOpenCL

Working with ROCm and PyOpenCL

Technical requirements

Understanding how ROCm-C/C++ works with hipify, HIP, and OpenCL

Installing PyOpenCL for Python (AMD and NVIDIA)

Configuring PyOpenCL on your Python IDE

How computing in PyOpenCL works on Python

Comparing PyOpenCL to HIP and OpenCL – revisiting the reduction perspective

Writing your first PyOpenCL programs to compute a general-purpose solution

Useful exercise on computational problem solving

Further reading

Working with Anaconda, CuPy, and Numba for GPUs

Working with Anaconda, CuPy, and Numba for GPUs

Technical requirements

Understanding how Anaconda works with CuPy and Numba

Installing CuPy and Numba for Python within an existing Anaconda environment

Configuring CuPy on your Python IDE

How computing in CuPy works on Python

Configuring Numba on your Python IDE

How computing in Numba works on Python

Writing your first CuPy and Numba enabled accelerated programs to compute GPGPU solutions

Interoperability between CuPy and Numba within a single Python program

Comparing CuPy to NumPy and CUDA

Comparing Numba to NumPy, ROCm, and CUDA

Useful exercise on computational problem solving

Further reading

Section 3: Containerization and Machine Learning with GPU-Powered Python

Section 3: Containerization and Machine Learning with GPU-Powered Python

Containerization on GPU-Enabled Platforms

Containerization on GPU-Enabled Platforms

Programmable environments

System-wide (open) environments

Virtual (closed) environments

Local containers

Cloud containers

Further reading

Accelerated Machine Learning on GPUs

Accelerated Machine Learning on GPUs

Technical requirements

The significance of Python in AI – the dual advantage

Exploring machine learning training modules

Introducing machine learning frameworks

Introducing TensorFlow

Introducing PyTorch

Installing TensorFlow and PyTorch for GPUs

Configuring TensorFlow on PyCharm and Google Colab

Configuring PyTorch on PyCharm and Google Colab

Machine learning with TensorFlow and PyTorch

Writing your first GPU-accelerated machine learning programs

Revisiting our computational exercises with a machine learning approach

Further reading

GPU Acceleration for Scientific Applications Using DeepChem

GPU Acceleration for Scientific Applications Using DeepChem

Technical requirements

Decoding scientific concepts for DeepChem

Multiple ways to install DeepChem

Configuring DeepChem on PyCharm

Testing an example from the DeepChem repository

Developing your own deep learning framework like DeepChem – a brief outlook

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Appendix A

GPU-accelerated machine learning in Python – benchmark research

GPU-accelerated machine learning with Python applied to cancer research

Deep Learning with GPU-accelerated Python for applied computer vision – Pavement Distress

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Comparing PyCUDA to CUDA – an introductory perspective on reduction

Let's compare PyCUDA to CUDA in terms of simplicity in parallelization before we write our first PyCUDA program on PyCharm.

In the following table, we can explore the scope of PyCUDA with respect to CUDA so as to understand scenarios when PyCUDA could be advantageous to CUDA:

CUDA	PyCUDA
Based on C/C++ programming language	Based on the Python programming language
Uses C/C++ combined with specialized code to accelerate computations	Uses Python for GPUs to interface CUDA and accelerate computations
Reduction is a key feature in CUDA that is extremely important to maximize parallelization and efficiently harness threads.	Reduction in PyCUDA is much simpler to use than CUDA, considering the significance of reduction.

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