Are you looking forward to get well versed with Parallel & Concurrent Programming Using Python? Then this is the perfect course for you!
The terms concurrency and parallelism are often used in relation to multithreaded programs. Parallel programming is not a walk in the park and sometimes confuses even some of the most experienced developers.
This comprehensive 2-in-1 course will take you smoothly through this difficult journey of current programming in Python, including common thread programming techniques and approaches to parallel processing. Similarly with parallel programming techniques you explore the ways in which you can write code that allows more than one process to happen at once.
After taking this course you will have gained an in-depth knowledge of using threads and processes with the help of real-world examples along with hands-on in GPU programming with Python using the PyCUDA module and will evaluate performance limitations.
Contents and Overview
This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.
The first course, Python Parallel Programming Solutions will teach you parallel programming techniques using examples in Python and 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, we move 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 and will learn about the performance of MPI Python Modules.
Moving on, you’ll get to grips with the asynchronous parallel programming model using the Python asyncio module, and will see how to handle exceptions. You will discover distributed computing with Python, and learn how to install a broker, use Celery Python Module, and create a worker.
The second course, Concurrent Programming in Python will skill-up with techniques related to various aspects of concurrent programming in Python, including common thread programming techniques and approaches to parallel processing.
Filled with examples, this course will show you all you need to know to start using concurrency in Python. You will learn about the principal approaches to concurrency that Python has to offer, including libraries and tools needed to exploit the performance of your processor. Learn the basic theory and history of parallelism and choose the best approach when it comes to parallel processing.
About the Authors:
Giancarlo Zaccone, a physicist, has been involved in scientific computing projects among firms and research institutions. He currently works in an IT company that designs software systems with high technological content.
BignumWorks Software LLP is an India-based software consultancy that provides consultancy services in the area of software development and technical training. Our domain expertise includes web, mobile, cloud app development, data science projects, in-house software training services, and up-skilling services
