How can one use multiprocessing to run a run a function faster?

Question

I am calling a function and saving the two outputs to variables but this process is taking time because the outputs are generated by solving an ODE.

Is it possible to use multiple cores to run the function faster so the values are saved sooner? If so, could someone provide a simple example?

Thank you

Answer 1

Simply running the same code on multiple cores will not make it run faster. It really depends on the type of tasks your doing. Here are some questions you need to find answers to before you can decide if the code will benefit from parallel processing:

• Are the steps in your computation sequence dependent? In other words, does one part of the code depend on the calculations done in the previous? Or can some of them be calculated in parallel? Look at Amdahl's law to learn about how much speedup to expect based on how much of your code you can parallelize

• Does your code involve lots of reading/writing to disk and memory? Or is it just lots of computation? If you are doing significant reads and rights to disk, then creating multiple processes to do other work while your threads wait for disk can result in significant speedups. But again, this depend on your answer to the previous point about sequence dependency

• How long does your code currently take to run? And is the overhead of creating multiple processes going to be more than the time it takes to run sequentially? In your question you don't give specific times - if you're talking about speeding up a task that takes a few seconds then the time required to create multiple processes might be significant compared to the time for the total task. But if you're talking about a task that takes minutes, then the overhead won't be as significant

• Have you considered whether your code is is data parallel or task parallel ? If so, you can decide if you want to parallelize using CPU or GPU. For large mathematical operations, look at Numpy for CPU-based and Cupy for GPU-based operations.