Learning concurrency in Python :: speed up your Python code with clean, readable, and advanced concurrency techniques /
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...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing,
2017.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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... |
| Beschreibung: | 1 online resource (1 volume) : illustrations |
| ISBN: | 9781787283169 178728316X |
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| 505 | 0 | |a Cover; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Speed It Up!; History of concurrency; Threads and multithreading; What is a thread?; Types of threads; What is multithreading?; Processes; Properties of processes; Multiprocessing; Event-driven programming; Turtle; Breaking it down; Reactive programming; ReactiveX -- RxPy; Breaking it down; GPU programming; PyCUDA; OpenCL; Theano; The limitations of Python; Jython; IronPython; Why should we use Python?; Concurrent image download; Sequential download. | |
| 505 | 8 | |a Breaking it downConcurrent download; Breaking it down; Improving number crunching with multiprocessing; Sequential prime factorization; Breaking it down; Concurrent prime factorization; Breaking it down; Summary; Chapter 2: Parallelize It; Understanding concurrency; Properties of concurrent systems; I/O bottlenecks; Understanding parallelism; CPU-bound bottlenecks; How do they work on a CPU?; Single-core CPUs; Clock rate; Martelli model of scalability; Time-sharing -- the task scheduler; Multi-core processors; System architecture styles; SISD; SIMD; MISD; MIMD. | |
| 505 | 8 | |a Computer memory architecture stylesUMA; NUMA; Summary; Chapter 3: Life of a Thread; Threads in Python; Thread state; State flow chart; Python example of thread state; Breaking it down; Different types of threads; POSIX threads; Windows threads; The ways to start a thread; Starting a thread; Inheriting from the thread class; Breaking it down; Forking; Example; Breaking it down; Daemonizing a thread; Example; Breaking it down; Handling threads in Python; Starting loads of threads; Example; Breaking it down; Slowing down programs using threads; Example; Breaking it down. | |
| 505 | 8 | |a Getting the total number of active threadsExample; Breaking it down; Getting the current thread; Example; Breaking it down; Main thread; Example; Breaking it down; Enumerating all threads; Example; Breaking it down; Identifying threads; Example; Breakdown; Ending a thread; Best practice in stopping threads; Example; Output; Orphan processes; How does the operating system handle threads; Creating processes versus threads; Example; Breaking it down; Multithreading models; One-to-one thread mapping; Many-to-one; Many-to-many; Summary; Chapter 4: Synchronization between Threads. | |
| 505 | 8 | |a Synchronization between threadsThe Dining Philosophers; Example; Output; Race conditions; Process execution sequence; The solution; Critical sections; Filesystem; Life-critical systems; Shared resources and data races; The join method; Breaking it down; Putting it together; Locks; Example; Breaking it down; RLocks; Example; Breaking it down; Output; RLocks versus regular locks; Condition; Definition; Example; Our publisher; Our subscriber; Kicking it off; The results; Semaphores; Class definition; Example; The TicketSeller class; Output; Thread race; Bounded semaphores; Events; Example. | |
| 520 | |a 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... | ||
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| contents | Cover; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Speed It Up!; History of concurrency; Threads and multithreading; What is a thread?; Types of threads; What is multithreading?; Processes; Properties of processes; Multiprocessing; Event-driven programming; Turtle; Breaking it down; Reactive programming; ReactiveX -- RxPy; Breaking it down; GPU programming; PyCUDA; OpenCL; Theano; The limitations of Python; Jython; IronPython; Why should we use Python?; Concurrent image download; Sequential download. Breaking it downConcurrent download; Breaking it down; Improving number crunching with multiprocessing; Sequential prime factorization; Breaking it down; Concurrent prime factorization; Breaking it down; Summary; Chapter 2: Parallelize It; Understanding concurrency; Properties of concurrent systems; I/O bottlenecks; Understanding parallelism; CPU-bound bottlenecks; How do they work on a CPU?; Single-core CPUs; Clock rate; Martelli model of scalability; Time-sharing -- the task scheduler; Multi-core processors; System architecture styles; SISD; SIMD; MISD; MIMD. Computer memory architecture stylesUMA; NUMA; Summary; Chapter 3: Life of a Thread; Threads in Python; Thread state; State flow chart; Python example of thread state; Breaking it down; Different types of threads; POSIX threads; Windows threads; The ways to start a thread; Starting a thread; Inheriting from the thread class; Breaking it down; Forking; Example; Breaking it down; Daemonizing a thread; Example; Breaking it down; Handling threads in Python; Starting loads of threads; Example; Breaking it down; Slowing down programs using threads; Example; Breaking it down. Getting the total number of active threadsExample; Breaking it down; Getting the current thread; Example; Breaking it down; Main thread; Example; Breaking it down; Enumerating all threads; Example; Breaking it down; Identifying threads; Example; Breakdown; Ending a thread; Best practice in stopping threads; Example; Output; Orphan processes; How does the operating system handle threads; Creating processes versus threads; Example; Breaking it down; Multithreading models; One-to-one thread mapping; Many-to-one; Many-to-many; Summary; Chapter 4: Synchronization between Threads. Synchronization between threadsThe Dining Philosophers; Example; Output; Race conditions; Process execution sequence; The solution; Critical sections; Filesystem; Life-critical systems; Shared resources and data races; The join method; Breaking it down; Putting it together; Locks; Example; Breaking it down; RLocks; Example; Breaking it down; Output; RLocks versus regular locks; Condition; Definition; Example; Our publisher; Our subscriber; Kicking it off; The results; Semaphores; Class definition; Example; The TicketSeller class; Output; Thread race; Bounded semaphores; Events; Example. |
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| discipline | Informatik |
| format | Electronic eBook |
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| publisher | Packt Publishing, |
| record_format | marc |
| spelling | Forbes, Elliot, author. Learning concurrency in Python : speed up your Python code with clean, readable, and advanced concurrency techniques / Elliot Forbes. Birmingham, UK : Packt Publishing, 2017. 1 online resource (1 volume) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Online resource; title from title page (Safari, viewed September 19, 2017). Cover; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Speed It Up!; History of concurrency; Threads and multithreading; What is a thread?; Types of threads; What is multithreading?; Processes; Properties of processes; Multiprocessing; Event-driven programming; Turtle; Breaking it down; Reactive programming; ReactiveX -- RxPy; Breaking it down; GPU programming; PyCUDA; OpenCL; Theano; The limitations of Python; Jython; IronPython; Why should we use Python?; Concurrent image download; Sequential download. Breaking it downConcurrent download; Breaking it down; Improving number crunching with multiprocessing; Sequential prime factorization; Breaking it down; Concurrent prime factorization; Breaking it down; Summary; Chapter 2: Parallelize It; Understanding concurrency; Properties of concurrent systems; I/O bottlenecks; Understanding parallelism; CPU-bound bottlenecks; How do they work on a CPU?; Single-core CPUs; Clock rate; Martelli model of scalability; Time-sharing -- the task scheduler; Multi-core processors; System architecture styles; SISD; SIMD; MISD; MIMD. Computer memory architecture stylesUMA; NUMA; Summary; Chapter 3: Life of a Thread; Threads in Python; Thread state; State flow chart; Python example of thread state; Breaking it down; Different types of threads; POSIX threads; Windows threads; The ways to start a thread; Starting a thread; Inheriting from the thread class; Breaking it down; Forking; Example; Breaking it down; Daemonizing a thread; Example; Breaking it down; Handling threads in Python; Starting loads of threads; Example; Breaking it down; Slowing down programs using threads; Example; Breaking it down. Getting the total number of active threadsExample; Breaking it down; Getting the current thread; Example; Breaking it down; Main thread; Example; Breaking it down; Enumerating all threads; Example; Breaking it down; Identifying threads; Example; Breakdown; Ending a thread; Best practice in stopping threads; Example; Output; Orphan processes; How does the operating system handle threads; Creating processes versus threads; Example; Breaking it down; Multithreading models; One-to-one thread mapping; Many-to-one; Many-to-many; Summary; Chapter 4: Synchronization between Threads. Synchronization between threadsThe Dining Philosophers; Example; Output; Race conditions; Process execution sequence; The solution; Critical sections; Filesystem; Life-critical systems; Shared resources and data races; The join method; Breaking it down; Putting it together; Locks; Example; Breaking it down; RLocks; Example; Breaking it down; Output; RLocks versus regular locks; Condition; Definition; Example; Our publisher; Our subscriber; Kicking it off; The results; Semaphores; Class definition; Example; The TicketSeller class; Output; Thread race; Bounded semaphores; Events; Example. 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... Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Python (Langage de programmation) Logiciels d'application Développement. COMPUTERS Programming Languages Python. bisacsh Application software Development fast Python (Computer program language) fast has work: Learning Concurrency in Python (Text) https://id.oclc.org/worldcat/entity/E39PD3FQgkRKQ9XCVdK6cChM4C https://id.oclc.org/worldcat/ontology/hasWork Print version: Forbes, Elliot. Learning Concurrency in Python. Birmingham : Packt Publishing, ©2017 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1577485 Volltext |
| spellingShingle | Forbes, Elliot Learning concurrency in Python : speed up your Python code with clean, readable, and advanced concurrency techniques / Cover; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: Speed It Up!; History of concurrency; Threads and multithreading; What is a thread?; Types of threads; What is multithreading?; Processes; Properties of processes; Multiprocessing; Event-driven programming; Turtle; Breaking it down; Reactive programming; ReactiveX -- RxPy; Breaking it down; GPU programming; PyCUDA; OpenCL; Theano; The limitations of Python; Jython; IronPython; Why should we use Python?; Concurrent image download; Sequential download. Breaking it downConcurrent download; Breaking it down; Improving number crunching with multiprocessing; Sequential prime factorization; Breaking it down; Concurrent prime factorization; Breaking it down; Summary; Chapter 2: Parallelize It; Understanding concurrency; Properties of concurrent systems; I/O bottlenecks; Understanding parallelism; CPU-bound bottlenecks; How do they work on a CPU?; Single-core CPUs; Clock rate; Martelli model of scalability; Time-sharing -- the task scheduler; Multi-core processors; System architecture styles; SISD; SIMD; MISD; MIMD. Computer memory architecture stylesUMA; NUMA; Summary; Chapter 3: Life of a Thread; Threads in Python; Thread state; State flow chart; Python example of thread state; Breaking it down; Different types of threads; POSIX threads; Windows threads; The ways to start a thread; Starting a thread; Inheriting from the thread class; Breaking it down; Forking; Example; Breaking it down; Daemonizing a thread; Example; Breaking it down; Handling threads in Python; Starting loads of threads; Example; Breaking it down; Slowing down programs using threads; Example; Breaking it down. Getting the total number of active threadsExample; Breaking it down; Getting the current thread; Example; Breaking it down; Main thread; Example; Breaking it down; Enumerating all threads; Example; Breaking it down; Identifying threads; Example; Breakdown; Ending a thread; Best practice in stopping threads; Example; Output; Orphan processes; How does the operating system handle threads; Creating processes versus threads; Example; Breaking it down; Multithreading models; One-to-one thread mapping; Many-to-one; Many-to-many; Summary; Chapter 4: Synchronization between Threads. Synchronization between threadsThe Dining Philosophers; Example; Output; Race conditions; Process execution sequence; The solution; Critical sections; Filesystem; Life-critical systems; Shared resources and data races; The join method; Breaking it down; Putting it together; Locks; Example; Breaking it down; RLocks; Example; Breaking it down; Output; RLocks versus regular locks; Condition; Definition; Example; Our publisher; Our subscriber; Kicking it off; The results; Semaphores; Class definition; Example; The TicketSeller class; Output; Thread race; Bounded semaphores; Events; Example. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Python (Langage de programmation) Logiciels d'application Développement. COMPUTERS Programming Languages Python. bisacsh Application software Development fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh96008834 http://id.loc.gov/authorities/subjects/sh95009362 |
| title | Learning concurrency in Python : speed up your Python code with clean, readable, and advanced concurrency techniques / |
| title_auth | Learning concurrency in Python : speed up your Python code with clean, readable, and advanced concurrency techniques / |
| title_exact_search | Learning concurrency in Python : speed up your Python code with clean, readable, and advanced concurrency techniques / |
| title_full | Learning concurrency in Python : speed up your Python code with clean, readable, and advanced concurrency techniques / Elliot Forbes. |
| title_fullStr | Learning concurrency in Python : speed up your Python code with clean, readable, and advanced concurrency techniques / Elliot Forbes. |
| title_full_unstemmed | Learning concurrency in Python : speed up your Python code with clean, readable, and advanced concurrency techniques / Elliot Forbes. |
| title_short | Learning concurrency in Python : |
| title_sort | learning concurrency in python speed up your python code with clean readable and advanced concurrency techniques |
| title_sub | speed up your Python code with clean, readable, and advanced concurrency techniques / |
| topic | Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Application software Development. http://id.loc.gov/authorities/subjects/sh95009362 Python (Langage de programmation) Logiciels d'application Développement. COMPUTERS Programming Languages Python. bisacsh Application software Development fast Python (Computer program language) fast |
| topic_facet | Python (Computer program language) Application software Development. Python (Langage de programmation) Logiciels d'application Développement. COMPUTERS Programming Languages Python. Application software Development |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1577485 |
| work_keys_str_mv | AT forbeselliot learningconcurrencyinpythonspeedupyourpythoncodewithcleanreadableandadvancedconcurrencytechniques |