Parallel Programming With Python

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Parallel Programming With Python

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Author : Jan Palach
language : en
Publisher: Packt Publishing Ltd
Release Date : 2014-06-25

Parallel Programming With Python written by Jan Palach and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-25 with Computers categories.

A fast, easy-to-follow and clear tutorial to help you develop Parallel computing systems using Python. Along with explaining the fundamentals, the book will also introduce you to slightly advanced concepts and will help you in implementing these techniques in the real world. If you are an experienced Python programmer and are willing to utilize the available computing resources by parallelizing applications in a simple way, then this book is for you. You are required to have a basic knowledge of Python development to get the most of this book.

Parallel And High Performance Programming With Python

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Author : Fabio Nelli
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2023-04-13

Parallel And High Performance Programming With Python written by Fabio Nelli and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-13 with Computers categories.

Unleash the capabilities of Python and its libraries for solving high performance computational problems. KEY FEATURES ● Explores parallel programming concepts and techniques for high-performance computing. ● Covers parallel algorithms, multiprocessing, distributed computing, and GPU programming. ● Provides practical use of popular Python libraries/tools like NumPy, Pandas, Dask, and TensorFlow. DESCRIPTION This book will teach you everything about the powerful techniques and applications of parallel computing, from the basics of parallel programming to the cutting-edge innovations shaping the future of computing. The book starts with an introduction to parallel programming and the different types of parallelism, including parallel programming with threads and processes. The book then delves into asynchronous programming, distributed Python, and GPU programming with Python, providing you with the tools you need to optimize your programs for distributed and high-performance computing. The book also covers a wide range of applications for parallel computing, including data science, artificial intelligence, and other complex scientific simulations. You will learn about the challenges and opportunities presented by parallel computing for these applications and how to overcome them. By the end of the book, you will have insights into the future of parallel computing, the latest research and developments in the field, and explore the exciting possibilities that lie ahead. WHAT WILL YOU LEARN ● Build faster, smarter, and more efficient applications for data analysis, machine learning, and scientific computing ● Implement parallel algorithms in Python ● Best practices for designing, implementing, and scaling parallel programs in Python WHO IS THIS BOOK FOR? This book is aimed at software developers who wish to take their careers to the next level by improving their skills and learning about concurrent and parallel programming. It is also intended for Python developers who aspire to write fast and efficient programs, and for students who wish to learn the fundamentals of parallel computing and its practical uses. TABLE OF CONTENTS 1. Introduction to Parallel Programming 2. Building Multithreaded Programs 3. Working with Multiprocessing and mpi4py Library 4. Asynchronous Programming with AsyncIO 5. Realizing Parallelism with Distributed Systems 6. Maximizing Performance with GPU Programming using CUDA 7. Embracing the Parallel Computing Revolution 8. Scaling Your Data Science Applications with Dask 9. Exploring the Potential of AI with Parallel Computing 10. Hands-on Applications of Parallel Computing

Mastering Concurrency In Python

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Author : Quan Nguyen
language : en
Publisher:
Release Date : 2018-11-24

Mastering Concurrency In Python written by Quan Nguyen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-24 with Computers categories.

Immerse yourself in the world of Python concurrency and tackle the most complex concurrent programming problems Key Features Explore the core syntaxes, language features and modern patterns of concurrency in Python Understand how to use concurrency to keep data consistent and applications responsive Utilize application scaffolding to design highly-scalable programs Book Description Python is one of the most popular programming languages, with numerous libraries and frameworks that facilitate high-performance computing. Concurrency and parallelism in Python are essential when it comes to multiprocessing and multithreading; they behave differently, but their common aim is to reduce the execution time. This book serves as a comprehensive introduction to various advanced concepts in concurrent engineering and programming. Mastering Concurrency in Python starts by introducing the concepts and principles in concurrency, right from Amdahl's Law to multithreading programming, followed by elucidating multiprocessing programming, web scraping, and asynchronous I/O, together with common problems that engineers and programmers face in concurrent programming. Next, the book covers a number of advanced concepts in Python concurrency and how they interact with the Python ecosystem, including the Global Interpreter Lock (GIL). Finally, you'll learn how to solve real-world concurrency problems through examples. By the end of the book, you will have gained extensive theoretical knowledge of concurrency and the ways in which concurrency is supported by the Python language What you will learn Explore the concepts of concurrency in programming Explore the core syntax and features that enable concurrency in Python Understand the correct way to implement concurrency Abstract methods to keep the data consistent in your program Analyze problems commonly faced in concurrent programming Use application scaffolding to design highly-scalable programs Who this book is for This book is for developers who wish to build high-performance applications and learn about signle-core, multicore programming or distributed concurrency. Some experience with Python programming language is assumed.

Parallel Programming With Python

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Author : Jan Palach
language : en
Publisher: CreateSpace
Release Date : 2014-12-12

Parallel Programming With Python written by Jan Palach and has been published by CreateSpace this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-12 with categories.

Develop efficient parallel systems using the robust Python environment Overview Demonstrates the concepts of Python parallel programming Boosts your Python computing capabilities Contains easy-to-understand explanations and plenty of examples In Detail Starting with the basics of parallel programming, you will proceed to learn about how to build parallel algorithms and their implementation. You will then gain the expertise to evaluate problem domains, identify if a particular problem can be parallelized, and how to use the Threading and Multiprocessor modules in Python. The Python Parallel (PP) module, which is another mechanism for parallel programming, is covered in depth to help you optimize the usage of PP. You will also delve into using Celery to perform distributed tasks efficiently and easily. Furthermore, you will learn about asynchronous I/O using the asyncio module. Finally, by the end of this book you will acquire an in-depth understanding about what the Python language has to offer in terms of built-in and external modules for an effective implementation of Parallel Programming. This is a definitive guide that will teach you everything you need to know to develop and maintain high-performance parallel computing systems using the feature-rich Python. What you will learn from this book Explore techniques to parallelize problems Integrate the Parallel Python module to implement Python code Execute parallel solutions on simple problems Achieve communication between processes using Pipe and Queue Use Celery Distributed Task Queue Implement asynchronous I/O using the Python asyncio module Create thread-safe structures Approach A fast, easy-to-follow and clear tutorial to help you develop Parallel computing systems using Python. Along with explaining the fundamentals, the book will also introduce you to slightly advanced concepts and will help you in implementing these techniques in the real world. Who this book is written for If you are an experienced Python programmer and are willing to utilize the available computing resources by parallelizing applications in a simple way, then this book is for you. You are required to have a basic knowledge of Python development to get the most of this book.

Hands On Gpu Programming With Python And Cuda

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Author : Dr. Brian Tuomanen
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-11-27

Hands On Gpu Programming With Python And Cuda written by Dr. Brian Tuomanen and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-27 with Computers categories.

Build real-world applications with Python 2.7, CUDA 9, and CUDA 10. We suggest the use of Python 2.7 over Python 3.x, since Python 2.7 has stable support across all the libraries we use in this book. Key FeaturesExpand your background in GPU programming—PyCUDA, scikit-cuda, and NsightEffectively use CUDA libraries such as cuBLAS, cuFFT, and cuSolverApply GPU programming to modern data science applicationsBook 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 learnLaunch GPU code directly from PythonWrite effective and efficient GPU kernels and device functionsUse libraries such as cuFFT, cuBLAS, and cuSolverDebug and profile your code with Nsight and Visual ProfilerApply GPU programming to datascience problemsBuild a GPU-based deep neuralnetwork from scratchExplore advanced GPU hardware features, such as warp shufflingWho 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.

Is Parallel Programming Hard

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Author : Paul E. McKenney
language : en
Publisher:
Release Date : 2015-06-13

Is Parallel Programming Hard written by Paul E. McKenney and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-13 with categories.

Parallel And High Performance Programming With Python Unlock Parallel And Concurrent Programming In Python Using Multithreading Cuda Pytorch And Dask

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Author : Fabio Nelli
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2023-04-12

Parallel And High Performance Programming With Python Unlock Parallel And Concurrent Programming In Python Using Multithreading Cuda Pytorch And Dask written by Fabio Nelli and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-12 with Computers categories.

Unleash the capabilities of Python and its libraries for solving high performance computational problems. Key Features ● Explores parallel programming concepts and techniques for high-performance computing. ● Covers parallel algorithms, multiprocessing, distributed computing, and GPU programming. ● Provides practical use of popular Python libraries/tools like NumPy, Pandas, Dask, and TensorFlow. Book Description This book will teach you everything about the powerful techniques and applications of parallel computing, from the basics of parallel programming to the cutting-edge innovations shaping the future of computing. The book starts with an introduction to parallel programming and the different types of parallelism, including parallel programming with threads and processes. The book then delves into asynchronous programming, distributed Python, and GPU programming with Python, providing you with the tools you need to optimize your programs for distributed and high-performance computing. The book also covers a wide range of applications for parallel computing, including data science, artificial intelligence, and other complex scientific simulations. You will learn about the challenges and opportunities presented by parallel computing for these applications and how to overcome them. By the end of the book, you will have insights into the future of parallel computing, the latest research and developments in the field, and explore the exciting possibilities that lie ahead. What you will learn ● Build faster, smarter, and more efficient applications for data analysis, machine learning, and scientific computing ● Implement parallel algorithms in Python ● Best practices for designing, implementing, and scaling parallel programs in Python Who is this book for? This book is aimed at software developers who wish to take their careers to the next level by improving their skills and learning about concurrent and parallel programming. It is also intended for Python developers who aspire to write fast and efficient programs, and for students who wish to learn the fundamentals of parallel computing and its practical uses. Table of Contents 1. Introduction to Parallel Programming 2. Building Multithreaded Programs 3. Working with Multiprocessing and mpi4py Library 4. Asynchronous Programming with AsyncIO 5. Realizing Parallelism with Distributed Systems 6. Maximizing Performance with GPU Programming using CUDA 7. Embracing the Parallel Computing Revolution 8. Scaling Your Data Science Applications with Dask 9. Exploring the Potential of AI with Parallel Computing 10. Hands-on Applications of Parallel Computing

Programming Massively Parallel Processors

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Author : David B. Kirk
language : en
Publisher: Newnes
Release Date : 2012-12-31

Programming Massively Parallel Processors written by David B. Kirk and has been published by Newnes this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-31 with Computers categories.

Programming Massively Parallel Processors: A Hands-on Approach, Second Edition, teaches students how to program massively parallel processors. It offers a detailed discussion of various techniques for constructing parallel programs. Case studies are used to demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. This guide shows both student and professional alike the basic concepts of parallel programming and GPU architecture. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in depth. This revised edition contains more parallel programming examples, commonly-used libraries such as Thrust, and explanations of the latest tools. It also provides new coverage of CUDA 5.0, improved performance, enhanced development tools, increased hardware support, and more; increased coverage of related technology, OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism; and two new case studies (on MRI reconstruction and molecular visualization) that explore the latest applications of CUDA and GPUs for scientific research and high-performance computing. This book should be a valuable resource for advanced students, software engineers, programmers, and hardware engineers. - New coverage of CUDA 5.0, improved performance, enhanced development tools, increased hardware support, and more - Increased coverage of related technology, OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism - Two new case studies (on MRI reconstruction and molecular visualization) explore the latest applications of CUDA and GPUs for scientific research and high-performance computing

High Performance Python

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Author : Micha Gorelick
language : en
Publisher: O'Reilly Media
Release Date : 2020-04-30

High Performance Python written by Micha Gorelick and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-30 with Computers categories.

Your Python code may run correctly, but you need it to run faster. Updated for Python 3, this expanded edition shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By exploring the fundamental theory behind design choices, High Performance Python helps you gain a deeper understanding of Python’s implementation. How do you take advantage of multicore architectures or clusters? Or build a system that scales up and down without losing reliability? Experienced Python programmers will learn concrete solutions to many issues, along with war stories from companies that use high-performance Python for social media analytics, productionized machine learning, and more. Get a better grasp of NumPy, Cython, and profilers Learn how Python abstracts the underlying computer architecture Use profiling to find bottlenecks in CPU time and memory usage Write efficient programs by choosing appropriate data structures Speed up matrix and vector computations Use tools to compile Python down to machine code Manage multiple I/O and computational operations concurrently Convert multiprocessing code to run on local or remote clusters Deploy code faster using tools like Docker