We take a break of developing code and check for performance (non-scientific testing!). In the previous entry a simple file was used as input: 8 DNA sequences of 500 bases each. That’s not enough to test the performance of the Python script against the C++ compiled executable. So, we use a larger file; two larger files to be more exact. First, we use a 555 sequence file with the sequences averaging 19371 nucleotides and another with 3854 sequences averaging 20000 nucleotides in length. Those files were the largest foreground and background clusters used in analysis. Let’s see how the Pyhton and C++ fared (Linux’s time was used in the comparison, for simplicity).

Foreground cluster

Average of 10 runs

C++: 45.66 seconds
Python: 36.4 seconds

Background cluster

Average of 10 runs

C++: 5 minutes and 4 seconds
Python: 2 minutes and 44 seconds

The C++ analysis of the background cluster was done on a cluster’s node, and not on my desktop computer, due to the fact that my desktop could not handle it. C++ defense: the code was developed by me and I am not a computer scientist. Clearly there is room for improvement and performance gain.

But, definitely we can see that by using some advanced techniques in Python we are able to outperform C++ (at least C++ code developed by me) and still have a short and easy to read script. Next time we will change the Python’s script output.