Master Parallel & Concurrent Programming Using Python:2 in 1

Dive into Parallel & Concurrent Programming with Python and unlock the power of multi-tasking! This comprehensive 2-in-1 course is designed for developers looking to master advanced Python programming techniques. You'll navigate the complexities of concurrency and parallelism, even if you're an experienced developer.

What you'll learn:

• Parallel Programming in Python: Explore techniques to execute multiple processes simultaneously, significantly boosting performance.
• Concurrent Programming in Python: Gain expertise in managing multiple tasks that appear to run at the same time.
• Threading with Python: Learn to utilize the Python threading module for thread-based parallelism. Cover topics like thread synchronization using locks, mutexes, semaphores, queues, the Global Interpreter Lock (GIL), and thread pools.
• Process-Based Parallelism: Discover how to synchronize processes using message passing and understand the performance implications of using MPI Python modules.
• Asynchronous Programming with asyncio: Master asynchronous programming using the Python asyncio module, including exception handling.
• Distributed Computing: Explore distributed computing concepts with Python, including setting up a broker, utilizing the Celery Python module, and creating worker processes.
• GPU Programming with Python (PyCUDA): Get hands-on experience with GPU programming using the PyCUDA module, evaluate performance limitations, and leverage parallel processing for computationally intensive tasks.
• Performance Optimization: Understand the fundamental theory and history of parallelism to choose the optimal approach for parallel processing and maximize your processor's capabilities.

Course Overview:

This training program combines two in-depth courses for complete skill development:

Course 1: Python Parallel Programming Solutions

• Introduction to parallel computing concepts.
• Fundamentals of parallel programming in Python.
• Thread-based parallelism using the threading module.
• Process-based parallelism with message passing and MPI.
• Asynchronous parallel programming with asyncio.
• Distributed computing with Celery.

Course 2: Concurrent Programming in Python

• Core concepts of concurrent programming.
• Essential libraries and tools for exploiting processor performance.
• Practical examples to immediately apply your knowledge.

About the Authors:

This course is led by Giancarlo Zaccone, a physicist with extensive experience in scientific computing and software development. The course is brought to you by BignumWorks Software LLP, an India-based software consultancy specializing in software development, technical training, and upskilling services. Our expertise spans web, mobile, cloud app development, data science, and in-house software training.