Parallel programming with Python :: develop efficient parallel systems using the robust Python environment /
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 experience...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK ; Mumbai :
Packt Publishing,
2014.
|
| Schriftenreihe: | Community experience distilled.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | 1 online resource (iii, 103 pages) : illustrations |
| ISBN: | 9781783288403 178328840X |
Internformat
MARC
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| 245 | 1 | 0 | |a Parallel programming with Python : |b develop efficient parallel systems using the robust Python environment / |c Jan Palach. |
| 264 | 1 | |a Birmingham, UK ; |a Mumbai : |b Packt Publishing, |c 2014. | |
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| 505 | 0 | |a Cover; Copyright; Credits; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Contextualizing Parallel, Concurrent, and Distributed Programming; Why use parallel programming?; Exploring common forms of parallelization; Communicating in parallel programming; Understanding shared state; Understanding message passing; Identifying parallel programming problems; Deadlock; Starvation; Race conditions; Discovering Python's parallel programming tools; The Python threading module; The Python multiprocessing module | |
| 505 | 8 | |a The parallel Python moduleCelery -- a distributed task queue; Taking care of Python GIL; Summary; Chapter 2: Designing Parallel Algorithms; The divide and conquer technique; Using data decomposition; Decomposing tasks with pipeline; Processing and mapping; Identifying independent tasks; Identifying the tasks that require data exchange; Load balance; Summary; Chapter 3: Identifying a Parallelizable Problem; Obtaining the highest Fibonacci value for multiple inputs; Crawling the Web; Summary; Chapter 4: Using the threading and concurrent.futures Modules; Defining threads | |
| 505 | 8 | |a Advantages and disadvantages of using threadsUnderstanding different kinds of threads; Defining the states of a thread; Choosing between threading and _thread; Using threading to obtain the Fibonacci series term with multiple inputs; Crawling the Web using the concurrent.futures module; Summary; Chapter 5: Using Multiprocessing and ProcessPoolExecutor; Understanding the concept of a process; Understanding the process model; Defining the states of a process; Implementing multiprocessing communication; Using multiprocessing. Pipe; Understanding multiprocessing. Queue | |
| 505 | 8 | |a Using multiprocessing to compute Fibonacci series terms with multiple inputsCrawling the Web using ProcessPoolExecutor; Summary; Chapter 6: Utilizing Parallel Python; Understanding interprocess communication; Exploring named pipes; Using named pipes with Python; Writing in a named pipe; Reading named pipes; Discovering PP; Using PP to calculate the Fibonacci series term on SMP architecture; Using PP to make a distributed Web crawler; Summary; Chapter 7: Distributing Tasks with Celery; Understanding Celery; Why use Celery?; Understanding Celery's architecture; Working with tasks | |
| 505 | 8 | |a Discovering message transport (broker)Understanding workers; Understanding result backends; Setting up the environment; Setting up the client machine; Setting up the server machine; Dispatching a simple task; Using Celery to obtain a Fibonacci series term; Defining queues by task types; Using Celery to make a distributed Web crawler; Summary; Chapter 8: Doing Things Asynchronously; Understanding blocking, nonblocking, and asynchronous operations; Understanding blocking operations; Understanding nonblocking operations; Understanding asynchronous operations; Understanding event loop | |
| 520 | |a 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. | ||
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| 650 | 7 | |a Python (Computer program language) |2 fast | |
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| contents | Cover; Copyright; Credits; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Contextualizing Parallel, Concurrent, and Distributed Programming; Why use parallel programming?; Exploring common forms of parallelization; Communicating in parallel programming; Understanding shared state; Understanding message passing; Identifying parallel programming problems; Deadlock; Starvation; Race conditions; Discovering Python's parallel programming tools; The Python threading module; The Python multiprocessing module The parallel Python moduleCelery -- a distributed task queue; Taking care of Python GIL; Summary; Chapter 2: Designing Parallel Algorithms; The divide and conquer technique; Using data decomposition; Decomposing tasks with pipeline; Processing and mapping; Identifying independent tasks; Identifying the tasks that require data exchange; Load balance; Summary; Chapter 3: Identifying a Parallelizable Problem; Obtaining the highest Fibonacci value for multiple inputs; Crawling the Web; Summary; Chapter 4: Using the threading and concurrent.futures Modules; Defining threads Advantages and disadvantages of using threadsUnderstanding different kinds of threads; Defining the states of a thread; Choosing between threading and _thread; Using threading to obtain the Fibonacci series term with multiple inputs; Crawling the Web using the concurrent.futures module; Summary; Chapter 5: Using Multiprocessing and ProcessPoolExecutor; Understanding the concept of a process; Understanding the process model; Defining the states of a process; Implementing multiprocessing communication; Using multiprocessing. Pipe; Understanding multiprocessing. Queue Using multiprocessing to compute Fibonacci series terms with multiple inputsCrawling the Web using ProcessPoolExecutor; Summary; Chapter 6: Utilizing Parallel Python; Understanding interprocess communication; Exploring named pipes; Using named pipes with Python; Writing in a named pipe; Reading named pipes; Discovering PP; Using PP to calculate the Fibonacci series term on SMP architecture; Using PP to make a distributed Web crawler; Summary; Chapter 7: Distributing Tasks with Celery; Understanding Celery; Why use Celery?; Understanding Celery's architecture; Working with tasks Discovering message transport (broker)Understanding workers; Understanding result backends; Setting up the environment; Setting up the client machine; Setting up the server machine; Dispatching a simple task; Using Celery to obtain a Fibonacci series term; Defining queues by task types; Using Celery to make a distributed Web crawler; Summary; Chapter 8: Doing Things Asynchronously; Understanding blocking, nonblocking, and asynchronous operations; Understanding blocking operations; Understanding nonblocking operations; Understanding asynchronous operations; Understanding event loop |
| ctrlnum | (OCoLC)883303744 |
| dewey-full | 005.275 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
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| dewey-sort | 15.275 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| series | Community experience distilled. |
| series2 | Community experience distilled |
| spelling | Palach, Jan, author. Parallel programming with Python : develop efficient parallel systems using the robust Python environment / Jan Palach. Birmingham, UK ; Mumbai : Packt Publishing, 2014. ©2014 1 online resource (iii, 103 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Community experience distilled Online resource; title from PDF title page (EBL, viewed October 6, 2014). Cover; Copyright; Credits; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Contextualizing Parallel, Concurrent, and Distributed Programming; Why use parallel programming?; Exploring common forms of parallelization; Communicating in parallel programming; Understanding shared state; Understanding message passing; Identifying parallel programming problems; Deadlock; Starvation; Race conditions; Discovering Python's parallel programming tools; The Python threading module; The Python multiprocessing module The parallel Python moduleCelery -- a distributed task queue; Taking care of Python GIL; Summary; Chapter 2: Designing Parallel Algorithms; The divide and conquer technique; Using data decomposition; Decomposing tasks with pipeline; Processing and mapping; Identifying independent tasks; Identifying the tasks that require data exchange; Load balance; Summary; Chapter 3: Identifying a Parallelizable Problem; Obtaining the highest Fibonacci value for multiple inputs; Crawling the Web; Summary; Chapter 4: Using the threading and concurrent.futures Modules; Defining threads Advantages and disadvantages of using threadsUnderstanding different kinds of threads; Defining the states of a thread; Choosing between threading and _thread; Using threading to obtain the Fibonacci series term with multiple inputs; Crawling the Web using the concurrent.futures module; Summary; Chapter 5: Using Multiprocessing and ProcessPoolExecutor; Understanding the concept of a process; Understanding the process model; Defining the states of a process; Implementing multiprocessing communication; Using multiprocessing. Pipe; Understanding multiprocessing. Queue Using multiprocessing to compute Fibonacci series terms with multiple inputsCrawling the Web using ProcessPoolExecutor; Summary; Chapter 6: Utilizing Parallel Python; Understanding interprocess communication; Exploring named pipes; Using named pipes with Python; Writing in a named pipe; Reading named pipes; Discovering PP; Using PP to calculate the Fibonacci series term on SMP architecture; Using PP to make a distributed Web crawler; Summary; Chapter 7: Distributing Tasks with Celery; Understanding Celery; Why use Celery?; Understanding Celery's architecture; Working with tasks Discovering message transport (broker)Understanding workers; Understanding result backends; Setting up the environment; Setting up the client machine; Setting up the server machine; Dispatching a simple task; Using Celery to obtain a Fibonacci series term; Defining queues by task types; Using Celery to make a distributed Web crawler; Summary; Chapter 8: Doing Things Asynchronously; Understanding blocking, nonblocking, and asynchronous operations; Understanding blocking operations; Understanding nonblocking operations; Understanding asynchronous operations; Understanding event loop 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 programming (Computer science) http://id.loc.gov/authorities/subjects/sh85097827 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Programmation parallèle (Informatique) Python (Langage de programmation) COMPUTERS Programming Parallel. bisacsh COMPUTERS Programming Languages Python. bisacsh Parallel programming (Computer science) fast Python (Computer program language) fast Print version: Palach, Jan. Parallel programming with python. Birmingham, UK : Packt Publishing Limited, 2014 1783288396 (OCoLC)881807285 Community experience distilled. http://id.loc.gov/authorities/names/no2011030603 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=805415 Volltext |
| spellingShingle | Palach, Jan Parallel programming with Python : develop efficient parallel systems using the robust Python environment / Community experience distilled. Cover; Copyright; Credits; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Contextualizing Parallel, Concurrent, and Distributed Programming; Why use parallel programming?; Exploring common forms of parallelization; Communicating in parallel programming; Understanding shared state; Understanding message passing; Identifying parallel programming problems; Deadlock; Starvation; Race conditions; Discovering Python's parallel programming tools; The Python threading module; The Python multiprocessing module The parallel Python moduleCelery -- a distributed task queue; Taking care of Python GIL; Summary; Chapter 2: Designing Parallel Algorithms; The divide and conquer technique; Using data decomposition; Decomposing tasks with pipeline; Processing and mapping; Identifying independent tasks; Identifying the tasks that require data exchange; Load balance; Summary; Chapter 3: Identifying a Parallelizable Problem; Obtaining the highest Fibonacci value for multiple inputs; Crawling the Web; Summary; Chapter 4: Using the threading and concurrent.futures Modules; Defining threads Advantages and disadvantages of using threadsUnderstanding different kinds of threads; Defining the states of a thread; Choosing between threading and _thread; Using threading to obtain the Fibonacci series term with multiple inputs; Crawling the Web using the concurrent.futures module; Summary; Chapter 5: Using Multiprocessing and ProcessPoolExecutor; Understanding the concept of a process; Understanding the process model; Defining the states of a process; Implementing multiprocessing communication; Using multiprocessing. Pipe; Understanding multiprocessing. Queue Using multiprocessing to compute Fibonacci series terms with multiple inputsCrawling the Web using ProcessPoolExecutor; Summary; Chapter 6: Utilizing Parallel Python; Understanding interprocess communication; Exploring named pipes; Using named pipes with Python; Writing in a named pipe; Reading named pipes; Discovering PP; Using PP to calculate the Fibonacci series term on SMP architecture; Using PP to make a distributed Web crawler; Summary; Chapter 7: Distributing Tasks with Celery; Understanding Celery; Why use Celery?; Understanding Celery's architecture; Working with tasks Discovering message transport (broker)Understanding workers; Understanding result backends; Setting up the environment; Setting up the client machine; Setting up the server machine; Dispatching a simple task; Using Celery to obtain a Fibonacci series term; Defining queues by task types; Using Celery to make a distributed Web crawler; Summary; Chapter 8: Doing Things Asynchronously; Understanding blocking, nonblocking, and asynchronous operations; Understanding blocking operations; Understanding nonblocking operations; Understanding asynchronous operations; Understanding event loop Parallel programming (Computer science) http://id.loc.gov/authorities/subjects/sh85097827 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Programmation parallèle (Informatique) Python (Langage de programmation) COMPUTERS Programming Parallel. bisacsh COMPUTERS Programming Languages Python. bisacsh Parallel programming (Computer science) fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85097827 http://id.loc.gov/authorities/subjects/sh96008834 |
| title | Parallel programming with Python : develop efficient parallel systems using the robust Python environment / |
| title_auth | Parallel programming with Python : develop efficient parallel systems using the robust Python environment / |
| title_exact_search | Parallel programming with Python : develop efficient parallel systems using the robust Python environment / |
| title_full | Parallel programming with Python : develop efficient parallel systems using the robust Python environment / Jan Palach. |
| title_fullStr | Parallel programming with Python : develop efficient parallel systems using the robust Python environment / Jan Palach. |
| title_full_unstemmed | Parallel programming with Python : develop efficient parallel systems using the robust Python environment / Jan Palach. |
| title_short | Parallel programming with Python : |
| title_sort | parallel programming with python develop efficient parallel systems using the robust python environment |
| title_sub | develop efficient parallel systems using the robust Python environment / |
| topic | Parallel programming (Computer science) http://id.loc.gov/authorities/subjects/sh85097827 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Programmation parallèle (Informatique) Python (Langage de programmation) COMPUTERS Programming Parallel. bisacsh COMPUTERS Programming Languages Python. bisacsh Parallel programming (Computer science) fast Python (Computer program language) fast |
| topic_facet | Parallel programming (Computer science) Python (Computer program language) Programmation parallèle (Informatique) Python (Langage de programmation) COMPUTERS Programming Parallel. COMPUTERS Programming Languages Python. |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=805415 |
| work_keys_str_mv | AT palachjan parallelprogrammingwithpythondevelopefficientparallelsystemsusingtherobustpythonenvironment |