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.

 
Topics include:
* Memory organization
* Parallel programming models
* Designing a parallel program and evaluating performance
* Working with threads in Python
* Synchronizing threads and using multithreading
* Spawning a process
* Running a process in the background
* Synchronizing processes
* Using the mpi4py Python module
* Using collective communication
* Reducing operations
* Managing events, tasks, and routines with Asyncio
* Distributing tasks