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What you would learn in Concurrent and Parallel Programming in Python course?
In this course, you'll learn how to write multi-threaded Asynchronous and multi-process applications in Python so that your applications run much more efficiently.
When applications communicate to other sources, a significant amount of time is spent waiting for data to move from one location to the next. You'll be taught how to use multi-threading and Asynchronous programming to speed up applications that are slowed due to IO operations.
We'll discuss an overview of the possible sources of bottlenecks in speed originating and how we can address these issues in the future. Then, we'll get right into the technical aspects and create an application that multi-threaded collects information online, processes the data, and then saves it to an internal database.
Limitations could impact other programs on CPUs. We'll also discover the process of implementing multiprocessing within Python, the Python library that allows us to utilize multiple CPUs within our Python program. This will allow us to distribute our work across all cores on the machine we're working on.
In addition, we'll integrate both of these elements by looking at ways to use multiprocessing in conjunction with asynchronous programming to reap the most significant benefit by maximizing the use of CPU resources and decreasing time wasted just waiting on IO response.
How do you use parallelism and concurrency Python
How do you write multi-threaded programs?
How do you write multi-processing programs?
How do you write synchronous programs?
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