3 ms·

I'm not sure I understand what you're suggesting, but maybe you're thinking that you will run the benchmark for some number of repetitions, and then at some lat

I'm not sure I understand what you're suggesting, but maybe you're thinking that you will run the benchmark for some number of repetitions, and then at some later time run it again, for that number of repetitions, and so forth for some number of times? If nothing changes from one time to another, you might as well combine all the results, and display the overall mean, or a histogram, or whatever. If you think that something may have changed from one time to another (eg, ambient temperature, which affects what clock frequency the processor may have been throttled down to), then you've got a complicated problem, if you're really trying to get to the bottom of the situation. But once again, if you're looking at how the sets of runs at different times differ, confidence intervals do not indicate the spread of the data. Perhaps you're computing quantiles instead, which would be sensible, but then incorrectly referring to them as "confidence intervals", when they're not?

Retric 7y ago
> If nothing changes Something changes, but not necessarily something important for every code path. Test could be to run optimization A x 1,000 then optimization B x 1,000. So now I Run the test, edit optimization B, and just run the full test again. Running A again might seem wasteful, but what if something else where to affect A and B, if I just test B then it’s not obvious something other than my code changed. Perhaps as you say the heat sink is failing and my CPU is now down clocking. The goal is to have a simple test that shows if I am improving things while also isolating that I am measuring what I think I am measuring. That’s not to say I will actually run several long tests repeatedly, but I may re run a test expecting similar results. Anyway, all of the above has nothing to do with why as want a confidence interval. It’s useful for getting a ballpark feeling of the underlying data.

Retric 7y ago

> If nothing changes Something changes, but not necessarily something important for every code path. Test could be to run optimization A x 1,000 then optimization B x 1,000. So now I Run the test, edit optimization B, and just run the full test again. Running A again might seem wasteful, but what if something else where to affect A and B, if I just test B then it’s not obvious something other than my code changed. Perhaps as you say the heat sink is failing and my CPU is now down clocking. The goal is to have a simple test that shows if I am improving things while also isolating that I am measuring what I think I am measuring. That’s not to say I will actually run several long tests repeatedly, but I may re run a test expecting similar results. Anyway, all of the above has nothing to do with why as want a confidence interval. It’s useful for getting a ballpark feeling of the underlying data.