Hands-On GPU Computing With Python by Bandyopadhyay, Avimanyu -- Read -- Imperial Library of Trantor

Index

Title Page Copyright and Credits

Hands-On GPU Computing with Python

Dedication About Packt

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Contributors

About the author About the reviewer Packt is searching for authors like you

Preface

Who this book is for What this book covers To get the most out of this book

Download the example code files Download the color images Code in Action Conventions used

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

The world of GPU computing beyond PC gaming

What is a GPU?

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 rise of AI and the need for GPUs

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

The C language – a short prologue From C to Python The simplicity of Python as a programming language – why many researchers and scientists prefer it The power of GPUs

Ray tracing Artificial intelligence (AI) Programmable shading

RTX-OPS

Latest GPUs at the time of writing this book (can be subject to change)

NVIDIA GeForce RTX 2070 NVIDIA GeForce RTX 2080 NVIDIA GeForce RTX 2080 Ti NVIDIA Titan RTX Radeon RX Vega 56 Radeon RX Vega 64 Radeon VII

Significance of FP64 in GPU computing The dual advantage – Python and GPUs, a powerful combination

How GPUs empower science and AI in current times

Bioinformatics workflow management Magnetic Resonance Imaging (MRI) reconstruction techniques Digital-signal processing for communication receivers Studies on the brain – neuroscience research Large-scale molecular dynamics simulations GPU-powered AI and self-driving cars Research work posited by AI scientists

Deep learning on commodity Android devices Motif discovery with deep learning Structural biology meets data science Heart-rate estimation on modern wearable devices Drug target discovery Deep learning for computational chemistry

The social impact of GPUs

Archaeological restoration/reconstruction Numerical weather prediction Composing music Real-time segmentation of sports players Creating art Security Agriculture Economics

Summary Further reading

Designing a GPU Computing Strategy

Getting started with the hardware

The significance of compatible hardware for your GPU

Beginners Intermediate users Advanced users

Motherboard Case Power supply unit (PSU) CPU RAM Hard-disk drive (HDD) Solid-state drive (SSD) Monitor

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

Scope of hardware scalability Branded desktops Do it yourself (DIY) desktops

Beginner range Mid range High-end range

Liquid cooling – should you consider it?

The temperature factor Airflow management Thermal paste Conventional air cooling Stock coolers Overclocking So, what are custom/aftermarket coolers? Liquid cooling The specific heat capacity of cooling agents Why is water the best liquid coolant?

Branded GPU-enabled PCs

Purpose Feasibility Upgradeability Refining an effective budget Warranty Bundled monitors Ready-to-deploy GPU systems GPU solutions for individuals Branded solutions in liquid cooling

Why not DIY?

GPU CPU Motherboard RAM Storage PSU Uninterrupted power supply (UPS) Thermal paste Heat sink Radiator Types of cooling fans Bottlenecking Estimating the build and performing compatibility checks Purpose Feasibility Upgradeability Refining an effective budget Warranty for individual components DIY solutions in liquid cooling Assembling your system Connecting all the power and case cables in place Installing CUDA on a fresh Ubuntu installation

Entry-level budget Mid-range budget High-end budget Summary Further reading

Setting Up a GPU Computing Platform with NVIDIA and AMD

GPU manufacturers

First generation Second generation Third generation Fourth generation Fifth generation Sixth generation Seventh generation and beyond

Computing on NVIDIA GPUs

GeForce platforms Quadro platforms Tesla platforms GPUDirect SXM and NVLink NVIDIA CUDA

Computing on AMD APUs and GPUs

Accelerated processing units (APUs) The GPU in the APU – the significance of APU design AMD GPUs – programmable platforms

Radeon platforms Radeon Pro platforms Radeon Instinct platforms

AMD ROCm

Comparing GPU programmable platforms on NVIDIA and AMD

GPUOpen The significance of double precision in scientific computing from a GPU perspective

Current models from both brands that are ideal for GPU computing

AMD Radeon VII GPU – the new people's champion NVIDIA Titan V GPU – raw compute power

An enthusiast's guide to GPU computing hardware Summary Further reading

Section 2: Hands-On Development with GPU Programming Fundamentals of GPU Programming

GPU-programmable platforms Basic CUDA concepts

Installing and testing Compute capability Threads, blocks, and grids

Threads Blocks Grids

Managing memory Unified Memory Access (UMA) Dynamic parallelism Predefined libraries OpenCL

Basic ROCm concepts

Installation procedure and testing

Official deprecation notice for HCC from AMD

Generating chips ROCm components (APIs), including OpenCL CUDA-like memory management with HIP hipify Predefined libraries OpenCL

The Anaconda Python distribution for package management and deployment

Installing the Anaconda Python distribution on Ubuntu 18.04 Application-specific usage

GPU-enabled Python programming

The dual advantage PyCUDA PyOpenCL CuPy Numba (formerly Accelerate)

Summary Further reading

Setting Up Your Environment for GPU Programming

Choosing a suitable IDE for your Python code PyCharm – an IDE exclusively made for Python

Different versions of PyCharm

The Community edition The Professional edition The Educational edition – PyCharm Edu

Features for learners Features for educators

PyCharm for Anaconda

Installing PyCharm

First run EduTools plugin for existing PyCharm users

Alternative IDEs for Python – PyDev and Jupyter Installing the PyDev Python IDE for Eclipse Installing Jupyter Notebook and Jupyter Lab Summary Further reading

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

Anaconda-based installation of PyCUDA pip – system-wide Python-based installation of PyCUDA

Configuring PyCUDA on your Python IDE

Conda-based virtual environment pip-based system-wide environment

How computing in PyCUDA works on Python Comparing PyCUDA to CUDA – an introductory perspective on reduction

What is reduction?

Writing your first PyCUDA programs to compute a general-purpose solution Useful exercise on computational problem solving

Exercise

Summary Further reading

Working with ROCm and PyOpenCL

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

Converting CUDA code into cross-platform HIP code with hipify Understanding how ROCm-C/C++ works with HIP

Output on an NVIDIA platform Output on an AMD platform

Understanding how OpenCL works

Installing PyOpenCL for Python (AMD and NVIDIA)

Anaconda-based installation of PyOpenCL pip – system-wide Python base installation of PyOpenCL

Configuring PyOpenCL on your Python IDE

Conda-based virtual environment pip-based system-wide environment

How computing in PyOpenCL works on Python Comparing PyOpenCL to HIP and OpenCL – revisiting the reduction perspective

Reduction with HIP, OpenCL, and PyOpenCL

Writing your first PyOpenCL programs to compute a general-purpose solution Useful exercise on computational problem solving

Solution assistance

Summary Further reading

Working with Anaconda, CuPy, and Numba for GPUs

Technical requirements Understanding how Anaconda works with CuPy and Numba

Conda CuPy Numba

GPU-accelerated Numba on Python

Installing CuPy and Numba for Python within an existing Anaconda environment

Coupling Python with CuPy Conda-based installation of CuPy pip-based installation of CuPy Coupling Python with Numba for CUDA and ROCm

Installing Numba with Conda for NVIDIA CUDA GPUs Installing Numba with Conda for AMD ROC GPUs System-wide installation of Numba with pip (optional)

Configuring CuPy on your Python IDE How computing in CuPy works on Python

Implementing multiple GPUs with CuPy

Configuring Numba on your Python IDE How computing in Numba works on Python

Using vectorize Explicit kernels

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 Summary Further reading

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

Programmable environments

Programmable environments – system-wide and virtual Specific situations of usage

Preferring virtual over system-wide Preferring system-wide over virtual

System-wide (open) environments

$HOME directory System directories Advantages of open environments Disadvantages of open environments

Virtual (closed) environments

$HOME directory Virtual system directories Advantages of closed environments Disadvantages of closed environments

Virtualization

Virtualenv

Installing virtualenv on Ubuntu Linux system Using Virtualenv to create and manage a virtual environment Key benefits of using Virtualenv

VirtualBox

Installing VirtualBox GPU passthrough

Local containers

Docker

Installing Docker Community Edition (CE) on Ubuntu 18.04 NVIDIA Docker

Installing NVIDIA Docker

ROCm Docker

Kubernetes

Cloud containers

An overview on GPU computing with Google Colab

Summary Further reading

Accelerated Machine Learning on GPUs

Technical requirements The significance of Python in AI – the dual advantage

The need for big data management Using Python for machine learning

Exploring machine learning training modules

The advent of deep learning

Introducing machine learning frameworks

Tensors by example

Introducing TensorFlow

Dataflow programming Differentiable programming TensorFlow on GPUs

Introducing PyTorch

The two primary features of PyTorch

Installing TensorFlow and PyTorch for GPUs

Installing cuDNN Coupling Python with TensorFlow for GPUs Coupling Python with PyTorch for GPUs

Configuring TensorFlow on PyCharm and Google Colab

Using TensorFlow on PyCharm Using TensorFlow on Google Colab

Configuring PyTorch on PyCharm and Google Colab

Using PyTorch on PyCharm Using PyTorch on Google Colab

Machine learning with TensorFlow and PyTorch

MNIST Fashion-MNIST CIFAR-10 Keras Dataset downloads

Downloading Fashion-MNIST with Keras Downloading CIFAR-10 with PyTorch

Writing your first GPU-accelerated machine learning programs

Fashion-MNIST prediction with TensorFlow TensorFlow output on the PyCharm console Training Fashion-MNIST for 100 epochs CIFAR-10 prediction with PyTorch PyTorch output on a PyCharm console

Revisiting our computational exercises with a machine learning approach

Solution assistance

Summary Further reading

GPU Acceleration for Scientific Applications Using DeepChem

Technical requirements Decoding scientific concepts for DeepChem

Atom Molecule Protein molecule Biological cell Medicinal drug – a small molecule Ki Crystallographic structures Assays Histogram Open Source Drug Discovery (OSDD) Convolution Ensemble Random Forest (RF) Graph convolutional neural networks (GCN) One-shot learning

Multiple ways to install DeepChem

Installing Google Colab Conda on your local PyCharm IDE NVIDIA Docker-based deployment

Configuring DeepChem on PyCharm Testing an example from the DeepChem repository

How medicines reach their targets in our body Alzheimer's disease IC50 The Beta-Site APP-Cleaving Enzyme (BACE) A DeepChem programming example Output on the PyCharm console

Developing your own deep learning framework like DeepChem – a brief outlook Summary Final thoughts References

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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