Hands On Gpu Programming With Python And Cuda

Hands On GPU Programming with Python and CUDA PDF
Author: Dr. Brian Tuomanen
Publisher: Packt Publishing Ltd
ISBN: 1788995228
Size: 30.33 MB
Format: PDF, Kindle
Category : Computers
Languages : en
Pages : 310
View: 422

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Hands On Gpu Programming With Python And Cuda

by Dr. Brian Tuomanen, Hands On Gpu Programming With Python And Cuda Books available in PDF, EPUB, Mobi Format. Download Hands On Gpu Programming With Python And Cuda books, Build real-world applications by writing effective GPU code, CUDA kernels, and device functions with the latest features of Python 3.7, CUDA 9 and CUDA 10 Key Features Expand your background in GPU programming—PyCUDA, scikit-cuda, and Nsight Effectively use CUDA libraries such as cuBLAS, cuFFT, and cuSolver Apply GPU programming to modern data science applications Book Description Hands-On GPU Programming with Python and CUDA hits the ground running: you’ll start by learning how to apply Amdahl’s Law, use a code profiler to identify bottlenecks in your Python code, and set up an appropriate GPU programming environment. You’ll then see how to “query” the GPU’s features and copy arrays of data to and from the GPU’s own memory. As you make your way through the book, you’ll launch code directly onto the GPU and write full blown GPU kernels and device functions in CUDA C. You’ll get to grips with profiling GPU code effectively and fully test and debug your code using Nsight IDE. Next, you’ll explore some of the more well-known NVIDIA libraries, such as cuFFT and cuBLAS. With a solid background in place, you will now apply your new-found knowledge to develop your very own GPU-based deep neural network from scratch. You’ll then explore advanced topics, such as warp shuffling, dynamic parallelism, and PTX assembly. In the final chapter, you’ll see some topics and applications related to GPU programming that you may wish to pursue, including AI, graphics, and blockchain. By the end of this book, you will be able to apply GPU programming to problems related to data science and high-performance computing. What you will learn Launch GPU code directly from Python Write effective and efficient GPU kernels and device functions Use libraries such as cuFFT, cuBLAS, and cuSolver Debug and profile your code with Nsight and Visual Profiler Apply GPU programming to datascience problems Build a GPU-based deep neuralnetwork from scratch Explore advanced GPU hardware features, such as warp shuffling Who this book is for Hands-On GPU Programming with Python and CUDA is for developers and data scientists who want to learn the basics of effective GPU programming to improve performance using Python code. You should have an understanding of first-year college or university-level engineering mathematics and physics, and have some experience with Python as well as in any C-based programming language such as C, C++, Go, or Java.


Hands On Gpu Computing With Python

Hands On GPU Computing with Python PDF
Author: Avimanyu Bandyopadhyay
Publisher: Packt Publishing Ltd
ISBN: 1789342406
Size: 35.86 MB
Format: PDF, ePub, Mobi
Category : Computers
Languages : en
Pages : 452
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Hands On Gpu Computing With Python

by Avimanyu Bandyopadhyay, Hands On Gpu Computing With Python Books available in PDF, EPUB, Mobi Format. Download Hands On Gpu Computing With Python books, Explore GPU-enabled programmable environment for machine learning, scientific applications, and gaming using PuCUDA, PyOpenGL, and Anaconda Accelerate Key Features Understand effective synchronization strategies for faster processing using GPUs Write parallel processing scripts with PyCuda and PyOpenCL Learn to use the CUDA libraries like CuDNN for deep learning on GPUs 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.


Hands On Gpu Accelerated Computer Vision With Opencv And Cuda

Hands On GPU Accelerated Computer Vision with OpenCV and CUDA PDF
Author: Bhaumik Vaidya
Publisher: Packt Publishing Ltd
ISBN: 1789343682
Size: 50.61 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 380
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Hands On Gpu Accelerated Computer Vision With Opencv And Cuda

by Bhaumik Vaidya, Hands On Gpu Accelerated Computer Vision With Opencv And Cuda Books available in PDF, EPUB, Mobi Format. Download Hands On Gpu Accelerated Computer Vision With Opencv And Cuda books, Discover how CUDA allows OpenCV to handle complex and rapidly growing image data processing in computer and machine vision by accessing the power of GPU Key Features Explore examples to leverage the GPU processing power with OpenCV and CUDA Enhance the performance of algorithms on embedded hardware platforms Discover C++ and Python libraries for GPU acceleration Book Description Computer vision has been revolutionizing a wide range of industries, and OpenCV is the most widely chosen tool for computer vision with its ability to work in multiple programming languages. Nowadays, in computer vision, there is a need to process large images in real time, which is difficult to handle for OpenCV on its own. This is where CUDA comes into the picture, allowing OpenCV to leverage powerful NVDIA GPUs. This book provides a detailed overview of integrating OpenCV with CUDA for practical applications. To start with, you’ll understand GPU programming with CUDA, an essential aspect for computer vision developers who have never worked with GPUs. You’ll then move on to exploring OpenCV acceleration with GPUs and CUDA by walking through some practical examples. Once you have got to grips with the core concepts, you’ll familiarize yourself with deploying OpenCV applications on NVIDIA Jetson TX1, which is popular for computer vision and deep learning applications. The last chapters of the book explain PyCUDA, a Python library that leverages the power of CUDA and GPUs for accelerations and can be used by computer vision developers who use OpenCV with Python. By the end of this book, you’ll have enhanced computer vision applications with the help of this book's hands-on approach. What you will learn Understand how to access GPU device properties and capabilities from CUDA programs Learn how to accelerate searching and sorting algorithms Detect shapes such as lines and circles in images Explore object tracking and detection with algorithms Process videos using different video analysis techniques in Jetson TX1 Access GPU device properties from the PyCUDA program Understand how kernel execution works Who this book is for This book is a go-to guide for you if you are a developer working with OpenCV and want to learn how to process more complex image data by exploiting GPU processing. A thorough understanding of computer vision concepts and programming languages such as C++ or Python is expected.


Hands On Embedded Programming With Qt

Hands On Embedded Programming with Qt PDF
Author: John Werner
Publisher: Packt Publishing Ltd
ISBN: 1789953804
Size: 76.54 MB
Format: PDF, ePub, Mobi
Category : Computers
Languages : en
Pages : 416
View: 2569

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Hands On Embedded Programming With Qt

by John Werner, Hands On Embedded Programming With Qt Books available in PDF, EPUB, Mobi Format. Download Hands On Embedded Programming With Qt books, A comprehensive guide that will get you up and running with embedded software development using Qt5 Key Features Learn to create fluid, cross-platform applications for embedded devices Achieve optimum performance in your applications with QT Lite project Explore the implementation of Qt with IoT using QtMqtt, QtKNX, and QtWebSockets Book Description Qt is an open-source toolkit suitable for cross-platform and embedded application development. This book uses inductive teaching to help you learn how to create applications for embedded and Internet of Things (IoT) devices with Qt 5. You’ll start by learning to develop your very first application with Qt. Next, you’ll build on the first application by understanding new concepts through hands-on projects and written text. Each project will introduce new features that will help you transform your basic first project into a connected IoT application running on embedded hardware. In addition to practical experience in developing an embedded Qt project, you will also gain valuable insights into best practices for Qt development, along with exploring advanced techniques for testing, debugging, and monitoring the performance of Qt applications. Through the course of the book, the examples and projects are demonstrated in a way so that they can be run both locally and on an embedded platform. By the end of this book, you will have the skills you need to use Qt 5 to confidently develop modern embedded applications. What you will learn Understand how to develop Qt applications using Qt Creator under Linux Explore various Qt GUI technologies to build resourceful and interactive applications Understand Qt’s threading model to maintain a responsive UI Get to grips with remote target load and debug under Qt Creator Become adept at writing IoT code using Qt Learn a variety of software best practices to ensure that your code is efficient Who this book is for This book is for software and hardware professionals with experience in different domains who are seeking new career opportunities in embedded systems and IoT. Working knowledge of the C++ Linux command line will be useful to get the most out of this book.


Learn Cuda Programming

Learn CUDA Programming PDF
Author: Jaegeun Han
Publisher: Packt Publishing Ltd
ISBN: 178899129X
Size: 39.43 MB
Format: PDF, Mobi
Category : Computers
Languages : en
Pages : 508
View: 1148

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Learn Cuda Programming

by Jaegeun Han, Learn Cuda Programming Books available in PDF, EPUB, Mobi Format. Download Learn Cuda Programming books, Explore different GPU programming methods using libraries and directives, such as OpenACC, with extension to languages such as C, C++, and Python Key Features Learn parallel programming principles and practices and performance analysis in GPU computing Get to grips with distributed multi GPU programming and other approaches to GPU programming Understand how GPU acceleration in deep learning models can improve their performance Book Description Compute Unified Device Architecture (CUDA) is NVIDIA's GPU computing platform and application programming interface. It's designed to work with programming languages such as C, C++, and Python. With CUDA, you can leverage a GPU's parallel computing power for a range of high-performance computing applications in the fields of science, healthcare, and deep learning. Learn CUDA Programming will help you learn GPU parallel programming and understand its modern applications. In this book, you'll discover CUDA programming approaches for modern GPU architectures. You'll not only be guided through GPU features, tools, and APIs, you'll also learn how to analyze performance with sample parallel programming algorithms. This book will help you optimize the performance of your apps by giving insights into CUDA programming platforms with various libraries, compiler directives (OpenACC), and other languages. As you progress, you'll learn how additional computing power can be generated using multiple GPUs in a box or in multiple boxes. Finally, you'll explore how CUDA accelerates deep learning algorithms, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). By the end of this CUDA book, you'll be equipped with the skills you need to integrate the power of GPU computing in your applications. What you will learn Understand general GPU operations and programming patterns in CUDA Uncover the difference between GPU programming and CPU programming Analyze GPU application performance and implement optimization strategies Explore GPU programming, profiling, and debugging tools Grasp parallel programming algorithms and how to implement them Scale GPU-accelerated applications with multi-GPU and multi-nodes Delve into GPU programming platforms with accelerated libraries, Python, and OpenACC Gain insights into deep learning accelerators in CNNs and RNNs using GPUs Who this book is for This beginner-level book is for programmers who want to delve into parallel computing, become part of the high-performance computing community and build modern applications. Basic C and C++ programming experience is assumed. For deep learning enthusiasts, this book covers Python InterOps, DL libraries, and practical examples on performance estimation.


Ipython Interactive Computing And Visualization Cookbook

IPython Interactive Computing and Visualization Cookbook PDF
Author: Cyrille Rossant
Publisher: Packt Publishing Ltd
ISBN: 1785881930
Size: 62.41 MB
Format: PDF, Kindle
Category : Computers
Languages : en
Pages : 548
View: 4939

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Ipython Interactive Computing And Visualization Cookbook

by Cyrille Rossant, Ipython Interactive Computing And Visualization Cookbook Books available in PDF, EPUB, Mobi Format. Download Ipython Interactive Computing And Visualization Cookbook books, Learn to use IPython and Jupyter Notebook for your data analysis and visualization work. Key Features Leverage the Jupyter Notebook for interactive data science and visualization Become an expert in high-performance computing and visualization for data analysis and scientific modeling A comprehensive coverage of scientific computing through many hands-on, example-driven recipes with detailed, step-by-step explanations Book Description Python is one of the leading open source platforms for data science and numerical computing. IPython and the associated Jupyter Notebook offer efficient interfaces to Python for data analysis and interactive visualization, and they constitute an ideal gateway to the platform. IPython Interactive Computing and Visualization Cookbook, Second Edition contains many ready-to-use, focused recipes for high-performance scientific computing and data analysis, from the latest IPython/Jupyter features to the most advanced tricks, to help you write better and faster code. You will apply these state-of-the-art methods to various real-world examples, illustrating topics in applied mathematics, scientific modeling, and machine learning. The first part of the book covers programming techniques: code quality and reproducibility, code optimization, high-performance computing through just-in-time compilation, parallel computing, and graphics card programming. The second part tackles data science, statistics, machine learning, signal and image processing, dynamical systems, and pure and applied mathematics. What you will learn Master all features of the Jupyter Notebook Code better: write high-quality, readable, and well-tested programs; profile and optimize your code; and conduct reproducible interactive computing experiments Visualize data and create interactive plots in the Jupyter Notebook Write blazingly fast Python programs with NumPy, ctypes, Numba, Cython, OpenMP, GPU programming (CUDA), parallel IPython, Dask, and more Analyze data with Bayesian or frequentist statistics (Pandas, PyMC, and R), and learn from actual data through machine learning (scikit-learn) Gain valuable insights into signals, images, and sounds with SciPy, scikit-image, and OpenCV Simulate deterministic and stochastic dynamical systems in Python Familiarize yourself with math in Python using SymPy and Sage: algebra, analysis, logic, graphs, geometry, and probability theory Who this book is for This book is intended for anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, and hobbyists. A basic knowledge of Python/NumPy is recommended. Some skills in mathematics will help you understand the theory behind the computational methods.


                                PDF
Author: ستيفن ولفرام
Publisher: ktab INC.
ISBN: 9772812436
Size: 36.36 MB
Format: PDF, Docs
Category :
Languages : ar
Pages : 126
View: 1669

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by ستيفن ولفرام, Books available in PDF, EPUB, Mobi Format. Download books, منذ ثلاثة قرون حدث تحول درامي في العالم عندما ظهرت فكرة تستند على أن القواعد المبنية على المعادلات الرياضية يمكن أن تستخدم لوصف العالم الطبيعي من حولنا . هدف هذا الكتاب هو بدء تحول آخر ، و وضع مقدمة لعلم جديد مبنى على فكرة القواعد الأكثر شمولاً و عمومية الكامنة في (البرامج الحاسوبية البسيطة) .


Gpu Parallel Computing For Machine Learning In Python

GPU Parallel Computing for Machine Learning in Python PDF
Author: Yoshiyasu Takefuji
Publisher:
ISBN: 9781521524909
Size: 40.39 MB
Format: PDF, ePub, Mobi
Category :
Languages : en
Pages : 51
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Gpu Parallel Computing For Machine Learning In Python

by Yoshiyasu Takefuji, Gpu Parallel Computing For Machine Learning In Python Books available in PDF, EPUB, Mobi Format. Download Gpu Parallel Computing For Machine Learning In Python books, This book illustrates how to build a GPU parallel computer. If you don't want to waste your time for building, you can buy a built-in-GPU desktop/laptop machine. All you need to do is to install GPU-enabled software for parallel computing. Imagine that we are in the midst of a parallel computing era. The GPU parallel computer is suitable for machine learning, deep (neural network) learning. For example, GeForce GTX1080 Ti is a GPU board with 3584 CUDA cores. Using the GeForce GTX1080 Ti, the performance is roughly 20 times faster than that of an INTEL i7 quad-core CPU. We have benchmarked the MNIST hand-written digits recognition problem (60,000 persons: hand-written digits from 0 to 9). The result of MNIST benchmark for machine learning shows that GPU of a single GeForce GTX1080 Ti board takes only less than 48 seconds while the INTEL i7 quad-core CPU requires 15 minutes and 42 seconds. A CUDA core is most commonly referring to the single-precision floating point units in an SM (streaming multiprocessor). A CUDA core can initiate one single precision floating point instruction per clock cycle. CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia. It allows software developers and software engineers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing. The GPU parallel computer is based on SIMD ( single instruction, multiple data) computing.The first GPU for neural networks was used by Kyoung-Su Oh, et al. for image processing published in 2004 (1). A minimum GPU parallel computer is composed of a CPU board and a GPU board. This book contains the important issue on which CPU/GPU board you should buy and also illustrates how to integrate them in a single box by considering the heat problem. The power consumption of GPU is so large that we should take care of the temperature and heat from the GPU board in the single box. Our goal is to have the faster parallel computer with lower power dissipation. Software installation is another critical issue for machine learning in Python. Two operating system examples including Ubuntu16.04 and Windows 10 system will be described. This book shows how to install CUDA and cudnnlib in two operating systems. Three frameworks including pytorch, keras, and chainer for machine learning on CUDA and cudnnlib will be introduced. Matching problems between operating system (Ubuntu, Windows 10), library (CUDA, cudnnlib), and machine learning framework (pytorch, keras, chainer) are discussed. The paper entitled "GPU" and "open source software" play a key role for advancing deep learning was published in Science (eLetter, July 20 2017)http://science.sciencemag.org/content/357/6346/16/tab-e-letters


Opencv Computer Vision Projects With Python

OpenCV  Computer Vision Projects with Python PDF
Author: Joseph Howse
Publisher: Packt Publishing Ltd
ISBN: 1787123847
Size: 60.57 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 558
View: 7231

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Opencv Computer Vision Projects With Python

by Joseph Howse, Opencv Computer Vision Projects With Python Books available in PDF, EPUB, Mobi Format. Download Opencv Computer Vision Projects With Python books, Get savvy with OpenCV and actualize cool computer vision applications About This Book Use OpenCV's Python bindings to capture video, manipulate images, and track objects Learn about the different functions of OpenCV and their actual implementations. Develop a series of intermediate to advanced projects using OpenCV and Python Who This Book Is For This learning path is for someone who has a working knowledge of Python and wants to try out OpenCV. This Learning Path will take you from a beginner to an expert in computer vision applications using OpenCV. OpenCV's application are humongous and this Learning Path is the best resource to get yourself acquainted thoroughly with OpenCV. What You Will Learn Install OpenCV and related software such as Python, NumPy, SciPy, OpenNI, and SensorKinect - all on Windows, Mac or Ubuntu Apply "curves" and other color transformations to simulate the look of old photos, movies, or video games Apply geometric transformations to images, perform image filtering, and convert an image into a cartoon-like image Recognize hand gestures in real time and perform hand-shape analysis based on the output of a Microsoft Kinect sensor Reconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniques Detect and recognize street signs using a cascade classifier and support vector machines (SVMs) Identify emotional expressions in human faces using convolutional neural networks (CNNs) and SVMs Strengthen your OpenCV2 skills and learn how to use new OpenCV3 features In Detail OpenCV is a state-of-art computer vision library that allows a great variety of image and video processing operations. OpenCV for Python enables us to run computer vision algorithms in real time. This learning path proposes to teach the following topics. First, we will learn how to get started with OpenCV and OpenCV3's Python API, and develop a computer vision application that tracks body parts. Then, we will build amazing intermediate-level computer vision applications such as making an object disappear from an image, identifying different shapes, reconstructing a 3D map from images , and building an augmented reality application, Finally, we'll move to more advanced projects such as hand gesture recognition, tracking visually salient objects, as well as recognizing traffic signs and emotions on faces using support vector machines and multi-layer perceptrons respectively. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: OpenCV Computer Vision with Python by Joseph Howse OpenCV with Python By Example by Prateek Joshi OpenCV with Python Blueprints by Michael Beyeler Style and approach This course aims to create a smooth learning path that will teach you how to get started with will learn how to get started with OpenCV and OpenCV 3's Python API, and develop superb computer vision applications. Through this comprehensive course, you'll learn to create computer vision applications from scratch to finish and more!.