This course 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. You will understand anche Pycsp, the Scoop framework, and disk modules in Python. Further on, you will get hands-on in GPU programming with Python using the PyCUDA module and will evaluate performance limitations.
A step-by-step guide to parallel programming using Python, with videos that feature one or more programming examples. It is a practically-oriented course and has all the necessary underlying parallel computing concepts.
This course 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. You will understand anche Pycsp, the Scoop framework, and disk modules in Python. Further on, you will get hands-on in GPU programming with Python using the PyCUDA module and will evaluate performance limitations.
A step-by-step guide to parallel programming using Python, with videos that feature one or more programming examples. It is a practically-oriented course and has all the necessary underlying parallel computing concepts.