Yes, except my for loops use the number of times it is looped and the loop step as variables, so it does not repeat the same operation over and over again cause it changes each time it loops by abit. So thats the issue.

What is the requirements for it to be gpu utalised thou? Like from what I know, usually my codes only use gpu if its a AI or machine learning code not normal conventional coding?

Unfortunately my code is not running the same operation over again but with changes at each time it loops for graph plotting of different points. The code also draws from multiple other code files not just an array and so idt it can be converted to be gpu utalised?

The program is running a radar simulation and signal processing algo which runs many loops to generate an ROC graph to check efficiency of radar detection method. The base code of simulation and detection takes 30s to run and there are multiple for loops around it to generate the data points for a graph with axises of probability of detection, probability of false alarm and Signal to noise ratio varying. This means 3 for loops, each within each other, one with 25 repetitions, one with 30 and one with 10. This sums the time to 42 hours.

My problem is just that my code takes 42 hours estimated run time. It is completely conventional python code. I was just wondering if this colab + would be beneficial in this scenario cause idk what the colab + actually does to conventional code.