I have several datasets consisting on:
- Number of threads
n - Start process time
t1 - Stop process time
t2 - Operations processed
x
So each line of the dataset mean n threads processed x operations in t2-t1 time
When more threads are added, the processing time is reduced, because they run on parallel. However, they have locks between them, so the total time is not (t2 -t1)/n, but a bit more.
I would like to infer the time a process will take to do x operations depending on the thread number, some sort of "parallel factor" so t = x * n * factor gives me the estimated time. How could I achieve this?