Concurrent Numpy In Python
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Concurrent NumPy in Python
Concurrency in NumPy is not an afterthought * Discover matrix multiplication that is 2.7x faster. * Discover array initialization that is up to 3.2x faster. * Discover sharing copied arrays that is up to 516.91x faster. NumPy is how we represent arrays of numbers in Python. An entire ecosystem of third-party libraries has been developed around NumPy arrays, from machine learning and deep learning to image and computer vision and more. Given the wide use of NumPy, it is essential we know how to get the most out of our system when using it. We cannot afford to have CPU cores sit idle when performing mathematical operations on arrays. Therefore we must know how to correctly harness concurrency in NumPy, such as: * NumPy has multithreaded algorithms and functions built-in (using BLAS). * NumPy will release the infamous GIL so Python threads can run in parallel. * NumPy arrays can be shared efficiently between Python processes using shared memory. The problem is, no one is talking about how. Introducing: "Concurrent NumPy in Python". A new book designed to teach you how to bring concurrency to your NumPy programs in Python, super fast! You will get fast-paced tutorials showing you how to bring concurrency to the most common NumPy tasks. Including: * Parallel array multiplication, common math functions, matrix solvers, and decompositions. * Parallel array filling and parallel creation of arrays of random numbers. * Parallel element-wise array arithmetic and common array math functions * Parallel programs for working with many NumPy arrays with thread and process pools. * Efficiently share arrays directly, and copies of arrays between Python processes. Don't worry if you are new to NumPy programming or concurrency, you will also get primers on the background required to get the most out of this book, including: * The importance of concurrency when using NumPy and the cost of approaching it naively. * How to perform common NumPy operations and math functions. * How to install, query, and configure BLAS libraries for built-in multithreaded NumPy functions. * How to use Python concurrency APIs including threading, multiprocessing, and pools of workers. Each tutorial is carefully designed to teach one critical aspect of how to bring concurrency to your NumPy projects. Learn Python concurrency correctly, step-by-step.
Python Concurrency with asyncio
Learn how to speed up slow Python code with concurrent programming and the cutting-edge asyncio library. Use coroutines and tasks alongside async/await syntax to run code concurrently Build web APIs and make concurrency web requests with aiohttp Run thousands of SQL queries concurrently Create a map-reduce job that can process gigabytes of data concurrently Use threading with asyncio to mix blocking code with asyncio code Python is flexible, versatile, and easy to learn. It can also be very slow compared to lower-level languages. Python Concurrency with asyncio teaches you how to boost Python's performance by applying a variety of concurrency techniques. You'll learn how the complex-but-powerful asyncio library can achieve concurrency with just a single thread and use asyncio's APIs to run multiple web requests and database queries simultaneously. The book covers using asyncio with the entire Python concurrency landscape, including multiprocessing and multithreading. About the technology It’s easy to overload standard Python and watch your programs slow to a crawl. Th e 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. About the book 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. What's inside Build web APIs and make concurrency web requests with aiohttp Run thousands of SQL queries concurrently Create a map-reduce job that can process gigabytes of data concurrently Use threading with asyncio to mix blocking code with asyncio code About the reader For intermediate Python programmers. No previous experience of concurrency required. About the author Matthew Fowler has over 15 years of software engineering experience in roles from architect to engineering director. Table of Contents 1 Getting to know asyncio 2 asyncio basics 3 A first asyncio application 4 Concurrent web requests 5 Non-blocking database drivers 6 Handling CPU-bound work 7 Handling blocking work with threads 8 Streams 9 Web applications 10 Microservices 11 Synchronization 12 Asynchronous queues 13 Managing subprocesses 14 Advanced asyncio
Mastering Concurrency in Python
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.