I have several datasets consisting on:
- Number of threads
- Start process time
- Stop process time
- Operations processed
So each line of the dataset mean
n threads processed
x operations in
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?