Advanced Python for Data Science Assignment 3

All assigments beginning with Assignment 3 are to be submitted via GitHub. Create a new repository for each assignment using your NetID, an underscore ‘_’, the word ‘assignment’ and the assignment number, all in lower case. For example, if your NetID is aaa11 then the repository for this assignment would be aaa11_assignment3. Clone this repository to your local computer. Commit any files that you are required to create as part of the assignment, then push the changes back to GitHub. Whatever is in the repository at the due date will be graded.

1. An astrophysicist colleague was recently complaining about how long it was taking to run an N-body simulation. “It’s really just a simple calculation, and I’m only simulating four planets, but it takes nearly a minute and a half to run one simulation. I really need it done in under 30 seconds.” You kindly offer to take a look at code to see if it is possible to speed it up. Your colleague provides you with a link to the source.

   Although your colleague said the code was simple, it is still fairly complex, so you decide to tackle the problem in stages. A first scan of the code reveals a number of potential areas that could be improved. These include:

   • Reducing function call overhead
   • Using alternatives to membership testing of lists
   • Using local rather than global variables
   • Using data aggregation to reduce loop overheads

   As you’re a cautious programmer, you decide to address each of these in turn. This will ensure that it is possible to check the program is still working correctly after each change, and to assess the performance improvement that the change achieved. You are also aware that the program has to be maintained by others in the future, so you want to make sure that the changes do not make this more difficult, especially if the performance improvement is only minor.

   For each of these areas, create a new version of nbody.py (call them nbody_1.py, nbody_2.py, etc.) and commit them to the repository. You may also add a file with any other optimizations that you find. At the beginning of each file, put a comment indicating if the change made the most improvement, second most, etc. Finally, create another file called nbody_opt.py that contains all of the optimizations you made. Put a comment at the top indicating the relative speedup of the optimized version compared to the original version. Calculate the relative speedup (R) as follows:

   

   where

   

   

   Are you able to get it to run in under 30 seconds?