Parallel And High Performance Programming With Python

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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

Parallel Python Programming

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Author : Ethan B Carter
language : en
Publisher: Independently Published
Release Date : 2024-12-30

Parallel Python Programming written by Ethan B Carter and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-30 with Computers categories.

Unlock the full potential of your Python code with Parallel Python Programming, a comprehensive guide to mastering concurrency and multicore processing. This book is designed for developers, data scientists, and engineers looking to accelerate their applications by utilizing modern multi-core processors and parallel execution. Explore the fundamental concepts of parallel computing and how they can be seamlessly integrated into your Python projects. Through practical examples and clear explanations, you'll learn to implement parallel algorithms, optimize performance, and overcome common bottlenecks in CPU-bound tasksFrom leveraging Python's threading and multiprocessing modules to using advanced libraries like Dask and Celery, this book covers the tools, techniques, and best practices for building high-performance, scalable applications. Whether you're working on data analysis, machine learning, or computational simulations, Parallel Python Programming empowers you to speed up your workflows and harness the power of multicore systems with ease. Step into the world of parallelism and unlock the next level of performance in Python programming.

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.

High Performance Computing In Finance

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Author : M. A. H. Dempster
language : en
Publisher: CRC Press
Release Date : 2018-02-21

High Performance Computing In Finance written by M. A. H. Dempster and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-21 with Computers categories.

High-Performance Computing (HPC) delivers higher computational performance to solve problems in science, engineering and finance. There are various HPC resources available for different needs, ranging from cloud computing– that can be used without much expertise and expense – to more tailored hardware, such as Field-Programmable Gate Arrays (FPGAs) or D-Wave’s quantum computer systems. High-Performance Computing in Finance is the first book that provides a state-of-the-art introduction to HPC for finance, capturing both academically and practically relevant problems.

High Performance Computing

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Author : Philippe Navaux
language : en
Publisher: Springer Nature
Release Date : 2022-12-20

High Performance Computing written by Philippe Navaux and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-20 with Computers categories.

This book constitutes the proceedings of the 9th Latin American Conference on High Performance Computing, CARLA 2022, held in Porto Alegre, Brazil, in September 2022. The 16 full papers presented in this volume were carefully reviewed and selected from 56 submissions. CARLA, the Latin American High Performance Computing Conference, is an international academic meeting aimed at providing a forum to foster the growth and strength of the High Performance Computing (HPC) community in Latin America and the Caribbean through the exchange and dissemination of new ideas, techniques, and research in HPC and its application areas.

Parallel And High Performance Computing

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Author : Robert Robey
language : en
Publisher: Simon and Schuster
Release Date : 2021-06-22

Parallel And High Performance Computing written by Robert Robey and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-22 with Computers categories.

Complex calculations, like training deep learning models or running large-scale simulations, can take an extremely long time. Efficient parallel programming can save hours--or even days--of computing time. Parallel and High Performance Computing shows you how to deliver faster run-times, greater scalability, and increased energy efficiency to your programs by mastering parallel techniques for multicore processor and GPU hardware. about the technology Modern computing hardware comes equipped with multicore CPUs and GPUs that can process numerous instruction sets simultaneously. Parallel computing takes advantage of this now-standard computer architecture to execute multiple operations at the same time, offering the potential for applications that run faster, are more energy efficient, and can be scaled to tackle problems that demand large computational capabilities. But to get these benefits, you must change the way you design and write software. Taking advantage of the tools, algorithms, and design patterns created specifically for parallel processing is essential to creating top performing applications. about the book Parallel and High Performance Computing is an irreplaceable guide for anyone who needs to maximize application performance and reduce execution time. Parallel computing experts Robert Robey and Yuliana Zamora take a fundamental approach to parallel programming, providing novice practitioners the skills needed to tackle any high-performance computing project with modern CPU and GPU hardware. Get under the hood of parallel computing architecture and learn to evaluate hardware performance, scale up your resources to tackle larger problem sizes, and deliver a level of energy efficiency that makes high performance possible on hand-held devices. When you''re done, you''ll be able to build parallel programs that are reliable, robust, and require minimal code maintenance. This book is unique in its breadth, with discussions of parallel algorithms, techniques to successfully develop parallel programs, and wide coverage of the most effective languages for the CPU and GPU. The programming paradigms include MPI, OpenMP threading, and vectorization for the CPU. For the GPU, the book covers OpenMP and OpenACC directive-based approaches and the native-based CUDA and OpenCL languages. what''s inside Steps for planning a new parallel project Choosing the right data structures and algorithms Addressing underperforming kernels and loops The differences in CPU and GPU architecture about the reader For experienced programmers with proficiency in a high performance computing language such as C, C++, or Fortran. about the authors Robert Robey has been active in the field of parallel computing for over 30 years. He works at Los Alamos National Laboratory, and has previously worked at the University of New Mexico, where he started up the Albuquerque High Performance Computing Center. Yuliana Zamora has lectured on efficient programming of modern hardware at national conferences, based on her work developing applications running on tens of thousands of processing cores and the latest GPU architectures.

Numerical Solution Of Partial Differential Equations On Parallel Computers

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Author : Are Magnus Bruaset
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-03-05

Numerical Solution Of Partial Differential Equations On Parallel Computers written by Are Magnus Bruaset and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-03-05 with Mathematics categories.

Since the dawn of computing, the quest for a better understanding of Nature has been a driving force for technological development. Groundbreaking achievements by great scientists have paved the way from the abacus to the supercomputing power of today. When trying to replicate Nature in the computer’s silicon test tube, there is need for precise and computable process descriptions. The scienti?c ?elds of Ma- ematics and Physics provide a powerful vehicle for such descriptions in terms of Partial Differential Equations (PDEs). Formulated as such equations, physical laws can become subject to computational and analytical studies. In the computational setting, the equations can be discreti ed for ef?cient solution on a computer, leading to valuable tools for simulation of natural and man-made processes. Numerical so- tion of PDE-based mathematical models has been an important research topic over centuries, and will remain so for centuries to come. In the context of computer-based simulations, the quality of the computed results is directly connected to the model’s complexity and the number of data points used for the computations. Therefore, computational scientists tend to ?ll even the largest and most powerful computers they can get access to, either by increasing the si e of the data sets, or by introducing new model terms that make the simulations more realistic, or a combination of both. Today, many important simulation problems can not be solved by one single computer, but calls for parallel computing.

Advanced Python Programming

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Author : Dr. Gabriele Lanaro
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-02-28

Advanced Python Programming written by Dr. Gabriele Lanaro 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 2019-02-28 with Computers categories.

Create distributed applications with clever design patterns to solve complex problems Key FeaturesSet up and run distributed algorithms on a cluster using Dask and PySparkMaster skills to accurately implement concurrency in your codeGain practical experience of Python design patterns with real-world examplesBook Description This Learning Path shows you how to leverage the power of both native and third-party Python libraries for building robust and responsive applications. You will learn about profilers and reactive programming, concurrency and parallelism, as well as tools for making your apps quick and efficient. You will discover how to write code for parallel architectures using TensorFlow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. With the knowledge of how Python design patterns work, you will be able to clone objects, secure interfaces, dynamically choose algorithms, and accomplish much more in high performance computing. By the end of this Learning Path, you will have the skills and confidence to build engaging models that quickly offer efficient solutions to your problems. This Learning Path includes content from the following Packt products: Python High Performance - Second Edition by Gabriele LanaroMastering Concurrency in Python by Quan NguyenMastering Python Design Patterns by Sakis KasampalisWhat you will learnUse NumPy and pandas to import and manipulate datasetsAchieve native performance with Cython and NumbaWrite asynchronous code using asyncio and RxPyDesign highly scalable programs with application scaffoldingExplore abstract methods to maintain data consistencyClone objects using the prototype patternUse the adapter pattern to make incompatible interfaces compatibleEmploy the strategy pattern to dynamically choose an algorithmWho this book is for This Learning Path is specially designed for Python developers who want to build high-performance applications and learn about single core and multi-core programming, distributed concurrency, and Python design patterns. Some experience with Python programming language will help you get the most out of this Learning Path.

Python High Performance Second Edition

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Author : Gabriele Lanaro
language : en
Publisher:
Release Date : 2017-05-24

Python High Performance Second Edition written by Gabriele Lanaro and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-05-24 with Computers categories.

Learn how to use Python to create efficient applicationsAbout This Book* Identify the bottlenecks in your applications and solve them using the best profiling techniques* Write efficient numerical code in NumPy, Cython, and Pandas* Adapt your programs to run on multiple processors and machines with parallel programmingWho This Book Is ForThe book is aimed at Python developers who want to improve the performance of their application. Basic knowledge of Python is expectedWhat You Will Learn* Write efficient numerical code with the NumPy and Pandas libraries* Use Cython and Numba to achieve native performance* Find bottlenecks in your Python code using profilers* Write asynchronous code using Asyncio and RxPy* Use Tensorflow and Theano for automatic parallelism in Python* Set up and run distributed algorithms on a cluster using Dask and PySparkIn DetailPython is a versatile language that has found applications in many industries. The clean syntax, rich standard library, and vast selection of third-party libraries make Python a wildly popular language.Python High Performance is a practical guide that shows how to leverage the power of both native and third-party Python libraries to build robust applications.The book explains how to use various profilers to find performance bottlenecks and apply the correct algorithm to fix them. The reader will learn how to effectively use NumPy and Cython to speed up numerical code. The book explains concepts of concurrent programming and how to implement robust and responsive applications using Reactive programming. Readers will learn how to write code for parallel architectures using Tensorflow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark.By the end of the book, readers will have learned to achieve performance and scale from their Python applications.Style and approachA step-by-step practical guide filled with real-world use cases and examples