Python Parallel Programming Solutions

This course will teach you the parallel programming techniques using examples in Python and help you explore the many ways in which you can write code that allows for 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 synchronizing threads using locks, mutex, semaphores queues, GIL, and thread pool. Next you will be taught about process-based parallelism, where you will synchronize processes using message passing and learn about performance of MPI Python Modules.

Moving on, you’ll get to grips with the asynchronous parallel programming model using the Python asynchronous module, and will see how to handle exceptions. You will discover distributed computing with Python, and learn how to install a broker, use the Celery Python Module, and create a worker. You will understand the Pycsp, the Scoop framework, and the disk modules in Python. Further, you will get hands-on in GPU programming with Python using the PyCUDA module and will evaluate performance limitations.