Learn parallel programming techniques using Python and explore the many ways you can write code that allows more than one task to occur at a time. First, discover how to develop and implement efficient software architecture that is set up to take advantage of thread-based and process-based parallelism. Next, find out how to use Python modules for asynchronous programming. Then, explore GPU programming using PyCUDA, NumbaPro, and PyOpenCL. This course provides extensive coverage of synchronizing processes, streamlining communication, reducing operations, and optimizing code so you can select and implement the right parallel processing solutions for your applications.

• 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