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Parallel Programming With Python
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Book Synopsis Parallel Programming with Python by : Jan Palach
Download or read book Parallel Programming with Python written by Jan Palach and published by Packt Publishing Ltd. This book was released on 2014-06-25 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Python Parallel Programming Cookbook by : Giancarlo Zaccone
Download or read book Python Parallel Programming Cookbook written by Giancarlo Zaccone and published by Packt Publishing Ltd. This book was released on 2015-08-26 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master efficient parallel programming to build powerful applications using Python About This Book Design and implement efficient parallel software Master new programming techniques to address and solve complex programming problems Explore the world of parallel programming with this book, which is a go-to resource for different kinds of parallel computing tasks in Python, using examples and topics covered in great depth Who This Book Is For Python Parallel Programming Cookbook is intended for software developers who are well versed with Python and want to use parallel programming techniques to write powerful and efficient code. This book will help you master the basics and the advanced of parallel computing. What You Will Learn Synchronize multiple threads and processes to manage parallel tasks Implement message passing communication between processes to build parallel applications Program your own GPU cards to address complex problems Manage computing entities to execute distributed computational tasks Write efficient programs by adopting the event-driven programming model Explore the cloud technology with DJango and Google App Engine Apply parallel programming techniques that can lead to performance improvements In Detail Parallel programming techniques are required for a developer to get the best use of all the computational resources available today and to build efficient software systems. From multi-core to GPU systems up to the distributed architectures, the high computation of programs throughout requires the use of programming tools and software libraries. Because of this, it is becoming increasingly important to know what the parallel programming techniques are. Python is commonly used as even non-experts can easily deal with its concepts. This book will teach you parallel programming techniques using examples in Python and will help you explore the many ways in which you can write code that allows more than one process to happen at once. Starting with introducing you to the world of parallel computing, it moves on to cover the fundamentals in Python. This is followed by exploring the thread-based parallelism model using the Python threading module by synchronizing threads and using locks, mutex, semaphores queues, GIL, and the thread pool. Next you will be taught about process-based parallelism where you will synchronize processes using message passing along with learning about the performance of MPI Python Modules. You will then go on to learn the asynchronous parallel programming model using the Python asyncio module along with handling exceptions. Moving on, you will discover distributed computing with Python, and learn how to install a broker, use Celery Python Module, and create a worker. You will also understand the StarCluster framework, Pycsp, Scoop, and Disco modules in Python. Further on, you will learn GPU programming with Python using the PyCUDA module along with evaluating performance limitations. Next you will get acquainted with the cloud computing concepts in Python, using Google App Engine (GAE), and building your first application with GAE. Lastly, you will learn about grid computing concepts in Python and using PyGlobus toolkit, GFTP and GASS COPY to transfer files, and service monitoring in PyGlobus. Style and approach A step-by-step guide to parallel programming using Python, with recipes accompanied by one or more programming examples. It is a practically oriented book and has all the necessary underlying parallel computing concepts.
Book Synopsis Mastering Concurrency in Python by : Quan Nguyen
Download or read book Mastering Concurrency in Python written by Quan Nguyen and published by Packt Publishing Ltd. This book was released on 2018-11-27 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: Immerse yourself in the world of Python concurrency and tackle the most complex concurrent programming problems Key FeaturesExplore the core syntaxes, language features and modern patterns of concurrency in PythonUnderstand how to use concurrency to keep data consistent and applications responsiveUtilize 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 learnExplore the concepts of concurrency in programmingExplore the core syntax and features that enable concurrency in PythonUnderstand the correct way to implement concurrencyAbstract methods to keep the data consistent in your programAnalyze problems commonly faced in concurrent programmingUse application scaffolding to design highly-scalable programsWho 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.
Book Synopsis High Performance Python by : Micha Gorelick
Download or read book High Performance Python written by Micha Gorelick and published by O'Reilly Media. This book was released on 2020-04-30 with total page 469 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Hands-On GPU Programming with Python and CUDA by : Dr. Brian Tuomanen
Download or read book Hands-On GPU Programming with Python and CUDA written by Dr. Brian Tuomanen and published by Packt Publishing Ltd. This book was released on 2018-11-27 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Topics in Parallel and Distributed Computing by : Sushil K Prasad
Download or read book Topics in Parallel and Distributed Computing written by Sushil K Prasad and published by Morgan Kaufmann. This book was released on 2015-09-16 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: Topics in Parallel and Distributed Computing provides resources and guidance for those learning PDC as well as those teaching students new to the discipline. The pervasiveness of computing devices containing multicore CPUs and GPUs, including home and office PCs, laptops, and mobile devices, is making even common users dependent on parallel processing. Certainly, it is no longer sufficient for even basic programmers to acquire only the traditional sequential programming skills. The preceding trends point to the need for imparting a broad-based skill set in PDC technology. However, the rapid changes in computing hardware platforms and devices, languages, supporting programming environments, and research advances, poses a challenge both for newcomers and seasoned computer scientists. This edited collection has been developed over the past several years in conjunction with the IEEE technical committee on parallel processing (TCPP), which held several workshops and discussions on learning parallel computing and integrating parallel concepts into courses throughout computer science curricula. Contributed and developed by the leading minds in parallel computing research and instruction Provides resources and guidance for those learning PDC as well as those teaching students new to the discipline Succinctly addresses a range of parallel and distributed computing topics Pedagogically designed to ensure understanding by experienced engineers and newcomers Developed over the past several years in conjunction with the IEEE technical committee on parallel processing (TCPP), which held several workshops and discussions on learning parallel computing and integrating parallel concepts
Book Synopsis Parallel and High Performance Programming with Python by : Fabio Nelli
Download or read book Parallel and High Performance Programming with Python written by Fabio Nelli and published by Orange Education Pvt Ltd. This book was released on 2023-04-13 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis The Art of Parallel Programming by : Bruce P. Lester
Download or read book The Art of Parallel Programming written by Bruce P. Lester and published by . This book was released on 1993 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematics of Computing -- Parallelism.
Book Synopsis Python Concurrency with Asyncio by : Matthew Fowler
Download or read book Python Concurrency with Asyncio written by Matthew Fowler and published by Simon and Schuster. This book was released on 2022-03 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: It's easy to overload standard Python and watch your programs slow to a crawl. The asyncio library was built to solve these problems by making it easy to divide and schedule tasks. It seamlessly handles multiple operations concurrently, leading to apps that are lightning fast and scalable. "Python concurrency with asyncio" introduces asynchronous, parallel, and concurrent programming through hands-on Python examples. Hard-to-grok concurrency topics are broken down into simple flowcharts that make it easy to see how your tasks are running. You'll learn how to overcome the limitations of Python using asyncio to speed up slow web servers and microservices. You'll even combine asyncio with traditional multiprocessing techniques for huge improvements to performance.
Book Synopsis Programming Massively Parallel Processors by : David B. Kirk
Download or read book Programming Massively Parallel Processors written by David B. Kirk and published by Newnes. This book was released on 2012-12-31 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis A Tour of Data Science by : Nailong Zhang
Download or read book A Tour of Data Science written by Nailong Zhang and published by CRC Press. This book was released on 2020-11-11 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source. Key features: Allows you to learn R and Python in parallel Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools – data.table and pandas Provides a concise and accessible presentation Includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc. Appealing to data scientists, statisticians, quantitative analysts, and others who want to learn programming with R and Python from a data science perspective.
Book Synopsis Practical Parallel Programming by : Gregory V. Wilson
Download or read book Practical Parallel Programming written by Gregory V. Wilson and published by Cambridge, Mass. : MIT Press. This book was released on 1995-01 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: Parallel computers have become widely available in recent years. Many scientists are now using them to investigate the grand challenges of science, such as modeling global climate change, determining the masses of elementary particles from first principles, or sequencing the human genome. However, software for parallel computers has developed far more slowly than the hardware. Many incompatible programming systems exist, and many useful programming techniques are not widely known. Practical Parallel Programming provides scientists and engineers with a detailed, informative, and often critical introduction to parallel programming techniques. Following a review of the fundamentals of parallel computer theory and architecture, it describes four of the most popular parallel programming models in use today—data parallelism, shared variables, message passing, and Linda—and shows how each can be used to solve various scientific and numerical problems. Examples, coded in various dialects of Fortran, are drawn from such domains as the solution of partial differential equations, solution of linear equations, the simulation of cellular automata, studies of rock fracturing, and image processing. Practical Parallel Programming will be particularly helpful for scientists and engineers who use high-performance computers to solve numerical problems and do physical simulations but who have little experience of networking or concurrency. The book can also be used by advanced undergraduate and graduate students in computer science in conjunction with material covering parallel architectures and algorithms in more detail. Computer science students will gain a critical appraisal of the current state of the art in parallel programming. Scientific and Engineering Computation series
Book Synopsis Parallel Programming Using C++ by : Gregory V. Wilson
Download or read book Parallel Programming Using C++ written by Gregory V. Wilson and published by MIT Press. This book was released on 1996-07-08 with total page 796 pages. Available in PDF, EPUB and Kindle. Book excerpt: Foreword by Bjarne Stroustrup Software is generally acknowledged to be the single greatest obstacle preventing mainstream adoption of massively-parallel computing. While sequential applications are routinely ported to platforms ranging from PCs to mainframes, most parallel programs only ever run on one type of machine. One reason for this is that most parallel programming systems have failed to insulate their users from the architectures of the machines on which they have run. Those that have been platform-independent have usually also had poor performance. Many researchers now believe that object-oriented languages may offer a solution. By hiding the architecture-specific constructs required for high performance inside platform-independent abstractions, parallel object-oriented programming systems may be able to combine the speed of massively-parallel computing with the comfort of sequential programming. Parallel Programming Using C++ describes fifteen parallel programming systems based on C++, the most popular object-oriented language of today. These systems cover the whole spectrum of parallel programming paradigms, from data parallelism through dataflow and distributed shared memory to message-passing control parallelism. For the parallel programming community, a common parallel application is discussed in each chapter, as part of the description of the system itself. By comparing the implementations of the polygon overlay problem in each system, the reader can get a better sense of their expressiveness and functionality for a common problem. For the systems community, the chapters contain a discussion of the implementation of the various compilers and runtime systems. In addition to discussing the performance of polygon overlay, several of the contributors also discuss the performance of other, more substantial, applications. For the research community, the contributors discuss the motivations for and philosophy of their systems. As well, many of the chapters include critiques that complete the research arc by pointing out possible future research directions. Finally, for the object-oriented community, there are many examples of how encapsulation, inheritance, and polymorphism can be used to control the complexity of developing, debugging, and tuning parallel software.
Book Synopsis Learning Concurrency in Python by : Elliot Forbes
Download or read book Learning Concurrency in Python written by Elliot Forbes and published by Packt Publishing Ltd. This book was released on 2017-08-16 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practically and deeply understand concurrency in Python to write efficient programs About This Book Build highly efficient, robust, and concurrent applications Work through practical examples that will help you address the challenges of writing concurrent code Improve the overall speed of execution in multiprocessor and multicore systems and keep them highly available Who This Book Is For This book is for Python developers who would like to get started with concurrent programming. Readers are expected to have a working knowledge of the Python language, as this book will build on these fundamentals concepts. What You Will Learn Explore the concept of threading and multiprocessing in Python Understand concurrency with threads Manage exceptions in child threads Handle the hardest part in a concurrent system — shared resources Build concurrent systems with Communicating Sequential Processes (CSP) Maintain all concurrent systems and master them Apply reactive programming to build concurrent systems Use GPU to solve specific problems In Detail Python is a very high level, general purpose language that is utilized heavily in fields such as data science and research, as well as being one of the top choices for general purpose programming for programmers around the world. It features a wide number of powerful, high and low-level libraries and frameworks that complement its delightful syntax and enable Python programmers to create. This book introduces some of the most popular libraries and frameworks and goes in-depth into how you can leverage these libraries for your own high-concurrent, highly-performant Python programs. We'll cover the fundamental concepts of concurrency needed to be able to write your own concurrent and parallel software systems in Python. The book will guide you down the path to mastering Python concurrency, giving you all the necessary hardware and theoretical knowledge. We'll cover concepts such as debugging and exception handling as well as some of the most popular libraries and frameworks that allow you to create event-driven and reactive systems. By the end of the book, you'll have learned the techniques to write incredibly efficient concurrent systems that follow best practices. Style and approach This easy-to-follow guide teaches you new practices and techniques to optimize your code, and then moves toward more advanced ways to effectively write efficient Python code. Small and simple practical examples will help you test the concepts yourself, and you will be able to easily adapt them for any application.
Book Synopsis Is Parallel Programming Hard by : Paul E. McKenney
Download or read book Is Parallel Programming Hard written by Paul E. McKenney and published by . This book was released on 2015-06-13 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis GPU Parallel Program Development Using CUDA by : Tolga Soyata
Download or read book GPU Parallel Program Development Using CUDA written by Tolga Soyata and published by CRC Press. This book was released on 2018-01-19 with total page 671 pages. Available in PDF, EPUB and Kindle. Book excerpt: GPU Parallel Program Development using CUDA teaches GPU programming by showing the differences among different families of GPUs. This approach prepares the reader for the next generation and future generations of GPUs. The book emphasizes concepts that will remain relevant for a long time, rather than concepts that are platform-specific. At the same time, the book also provides platform-dependent explanations that are as valuable as generalized GPU concepts. The book consists of three separate parts; it starts by explaining parallelism using CPU multi-threading in Part I. A few simple programs are used to demonstrate the concept of dividing a large task into multiple parallel sub-tasks and mapping them to CPU threads. Multiple ways of parallelizing the same task are analyzed and their pros/cons are studied in terms of both core and memory operation. Part II of the book introduces GPU massive parallelism. The same programs are parallelized on multiple Nvidia GPU platforms and the same performance analysis is repeated. Because the core and memory structures of CPUs and GPUs are different, the results differ in interesting ways. The end goal is to make programmers aware of all the good ideas, as well as the bad ideas, so readers can apply the good ideas and avoid the bad ideas in their own programs. Part III of the book provides pointer for readers who want to expand their horizons. It provides a brief introduction to popular CUDA libraries (such as cuBLAS, cuFFT, NPP, and Thrust),the OpenCL programming language, an overview of GPU programming using other programming languages and API libraries (such as Python, OpenCV, OpenGL, and Apple’s Swift and Metal,) and the deep learning library cuDNN.
Book Synopsis Parallel Programming with Python by : Jan Palach
Download or read book Parallel Programming with Python written by Jan Palach and published by CreateSpace. This book was released on 2014-12-12 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
