Giancarlo Zaccone - Python Parallel Programming Cookbook [2015, PDF/EPUB/MOBI, ENG] +Code
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Files
| Code | |||
| Chapter 1 | |||
| called_Process.py | 93 B | ||
| calling_Process.py | 275 B | ||
| hello_Python_WithThreads.py | 1.06 KB | ||
| Chapter 2 | |||
| Thread_in_a_subclass.py | 1017 B | ||
| __pycache__ | |||
| queue.cpython-33.pyc | 2.14 KB | ||
| condition.py | 1.4 KB | ||
| definining_a_thread.py | 256 B | ||
| determine_the_current_thread.py | 1.08 KB | ||
| evaluating_performances.py | 1.41 KB | ||
| event.py | 1.26 KB | ||
| lock_management.py | 1.54 KB | ||
| queue.py | 1.18 KB | ||
| rlock_management.py | 1.21 KB | ||
| semaphores.py | 1.16 KB | ||
| using_with_statement.py | 1.06 KB | ||
| Chapter 3 | |||
| __pycache__ | |||
| background_process.cpython-33.pyc | 977 B | ||
| naming_a_process.cpython-33.pyc | 913 B | ||
| process_barrier.cpython-33.pyc | 1.76 KB | ||
| process_pool.cpython-33.pyc | 785 B | ||
| run_a_background_process.cpython-33.pyc | 989 B | ||
| spawn_a_process.cpython-33.pyc | 770 B | ||
| subclass_process.cpython-33.pyc | 1.03 KB | ||
| using_a_process_in_a_subclass.cpython-33.pyc | 1.07 KB | ||
| using_queue.cpython-33.pyc | 2.79 KB | ||
| alltoall.py | 394 B | ||
| background_process.py | 699 B | ||
| broadcast.py | 370 B | ||
| deadLockProblems.py | 804 B | ||
| gather.py | 484 B | ||
| helloworldwithMPI.py | 125 B | ||
| kill_a_process.py | 588 B | ||
| naming_a_process.py | 686 B | ||
| pointToPointCommunication.py | 694 B | ||
| process_barrier.py | 1.18 KB | ||
| process_pool.py | 405 B | ||
| processes_barrier.py | 1.19 KB | ||
| reduction.py | 479 B | ||
| scatter.py | 372 B | ||
| spawn_a_process.py | 377 B | ||
| subclass_process.py | 393 B | ||
| synchronize_with_manager.py | 524 B | ||
| using_pipes.py | 1.18 KB | ||
| using_queue.py | 1.48 KB | ||
| virtualTopology.py | 2.02 KB | ||
| Chapter 4 | |||
| Asyncio_coroutine.py | 2.23 KB | ||
| asyncio_Task.py | 1.19 KB | ||
| asyncio_future.py | 1.13 KB | ||
| asyncio_loop.py | 1.24 KB | ||
| pooling_with_concurrent_futures.py | 1.33 KB | ||
| Chapter 5 | |||
| Celery | |||
| addTask.py | 189 B | ||
| addTask_main.py | 146 B | ||
| DISCO | |||
| MapReduce_Count_Words.py | 1.79 KB | ||
| SortAlphabetical.txt | 128 B | ||
| SortNumerical.txt | 128 B | ||
| PyCSP | |||
| example.py | 321 B | ||
| Pyro4 | |||
| First Example | |||
| client.py | 274 B | ||
| server.py | 726 B | ||
| Second Example | |||
| chainTopology.py | 958 B | ||
| client.py | 160 B | ||
| server_1.py | 395 B | ||
| server_2.py | 395 B | ||
| server_3.py | 395 B | ||
| Third Example | |||
| client.py | 1.8 KB | ||
| server.py | 343 B | ||
| shop.py | 1.4 KB | ||
| RPyC | |||
| rpyc_classic.py | 182 B | ||
| SCOOP | |||
| First Example | |||
| pi_calculus_with_montecarlo_method.py | 1.67 KB | ||
| Second Example | |||
| map_reduce.py | 1004 B | ||
| desktop.ini | 84 B | ||
| Chapter 6 | |||
| NUMBAPRO | |||
| blasNumpy.py | 1.03 KB | ||
| matrixMultiplication.py | 729 B | ||
| PyCUDA | |||
| PyCudaElementWise.py | 1.36 KB | ||
| PyCudaGPUArray.py | 333 B | ||
| PyCudaInstallation.py | 383 B | ||
| PyCudaMatrixManipulation.py | 1.45 KB | ||
| PyCudaReductionKernel.py | 699 B | ||
| PyCudaWorkflow.py | 634 B | ||
| PyOPENCL | |||
| PyOpenCLDeviceInfo.py | 1.57 KB | ||
| PyOpenCLDotProduct.py | 1.84 KB | ||
| PyOpenCLDotProduct_2.py | 1.38 KB | ||
| PyOpenCLElementwise.py | 868 B | ||
| PyOpenCLParallellSum.py | 1.33 KB | ||
| PyOpenCLProgramming.py | 1.27 KB | ||
| PyOpenCLTestApplication.py | 2.9 KB | ||
| PyOpenCLTestApplication_2.py | 2.32 KB | ||
| Readme.txt | 42 B | ||
| Software Hardware List.pdf | 98.25 KB | ||
| Giancarlo Zaccone - Python Parallel Programming Cookbook - 2015.epub | 6.52 MB | ||
| Giancarlo Zaccone - Python Parallel Programming Cookbook - 2015.mobi | 10.1 MB | ||
| Giancarlo Zaccone - Python Parallel Programming Cookbook - 2015.pdf | 2.39 MB |
Description
Книги и журналы » Компьютерная литература » Программирование (книги)
Python Parallel Programming Cookbook +Code
Год издания: 2015
Автор: Giancarlo Zaccone
Жанр или тематика: Программирование
Издательство: Packt Publishing
ISBN: 9781785289583
Язык: Английский
Формат: PDF/EPUB/MOBI
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 286
Описание: 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 understand anche Pycsp, the Scoop framework, and disk modules in Python. Further on, you will learnGPU programming withPython using the PyCUDA module along with evaluating performance limitations.
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
[spoiler="Примеры страниц"]
[/spoiler]
[spoiler="Оглавление"]
Table of Contents
1: Getting Started with Parallel Computing and Python
2: Thread-based Parallelism
3: Process-based Parallelism
4: Asynchronous Programming
5: Distributed Python
6: GPU Programming with Python
[/spoiler]
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