We have one old ASP.Net asmx webservice in our application which receives bulk requests at sometime. Service is taking less than 5 seconds for a single request. But It is taking more than a minute when it receives 20 or more concurrent requests. Following is the way it is implemented,
I have a static class with a static get property, and in this property, I do this:
This is probably a trivial question, but how do I parallelize the following loop in python?
Suppose I have a large in memory numpy array, I have a function func that takes in this giant array as input (together with some other parameters). func with different parameters can be run in parallel. For example:
I am learning how to use the threading and the multiprocessing modules in Python to run certain operations in parallel and speed up my code.
For C++, we can use OpenMP to do parallel programming; however, OpenMP will not work for Python. What should I do if I want to parallel some parts of my python program?
The documentation for the multiprocessing module shows how to pass a queue to a process started with multiprocessing.Process. But how can I share a queue with asynchronous worker processes started with apply_async? I don’t need dynamic joining or anything else, just a way for the workers to (repeatedly) report their results back to base.
I am trying to make 2 functions run at the same time.
I am doing a machine learning project in Python, so I have to do parallel predict function, which I’m using in my program.
I have used rosetta.parallel.pandas_easy to parallelize apply after groupby, for example: