[LLVMdev] Use perf tool for more accurate time measuring on Linux

[Tobias Grosser]

On 20/05/2014 22:00, Yi Kong wrote:
> On 20 May 2014 17:55, Tobias Grosser <tobias at grosser.es> wrote:
>> On 20/05/2014 18:20, Yi Kong wrote:
>>>
>>> On 20 May 2014 16:40, Tobias Grosser <tobias at grosser.es> wrote:
>>>>
>>>> On 20/05/2014 16:01, Yi Kong wrote:
>>>>>
>>>>>
>>>>> I've set up a public LNT server to show the result of perf stat. There
>>>>> is a huge improvement compared with timeit tool.
>>>>> http://parkas16.inria.fr:8000/
>>>>
>>>>
>>>>
>>>> Hi Yi Kong,
>>>>
>>>> thanks for testing these changes.
>>>>
>>>>
>>>>> Patch is updated to pin the process to a single core, the readings are
>>>>> even more accurate. It's hard coded to run everything on core 0, so
>>>>> don't run parallel testing with it for now. The tool now depends on
>>>>> Linux perf and schedtool.
>>>>
>>>>
>>>>
>>>> I think this sounds like a very good direction.
>>>>
>>>> How did you evaluate the improvements exactly? The following run shows
>>>> e.g
>>>> two execution time changes:
>>>
>>>
>>> I sent a screenshot of original results in the previous mail. We used
>>> to have lots of noise readings, both from small machine background
>>> noise and large noise from the timing tool. Now noise from timing tool
>>> is eliminated and only few machine background noise is left. This
>>> makes manual investigation possible.
>>
>>
>> I think we need to get this down to zero even at the cost of missing
>> regressions. We have many commits and runs per day, having one or two noisy
>> results per run means people will still not look at performance changes.
>>
>>
>>>> http://parkas16.inria.fr:8000/db_default/v4/nts/9
>>>>
>>>> Are they expected? If I change e.g. the aggregation function to median
>>>> they disappear. Similarly the graph for one of them does not suggest an
>>>> actual performance change:
>>>
>>>
>>> Yes, some false positives due to machine noise is expected. Median is
>>> more tolerant to machine noise, therefore they disappear.
>>
>>
>> Right.
>>
>> What I find interesting is that this change filters several results that
>> seem to not be filtered out by our statistical test. Is this right?
>
> Yes. MWU test is nonparametric, it examines the order rather than the
> actual value of the samples. However eliminating with median uses
> actual value(if medians of two samples are close enough, we treat them
> as equal).

I see. So some of the useful eliminations come from the fact that we 
actually run a parametric test? So we _do_ in this case take some 
assumptions about the distribution of the values, right?

>> In the optimal case, we should be able to set the confidence level we
>> require high enough to filter out these results as well. Is this right?
>
> Yes. The lowest confidence we can set is still quite high(90%). We can
> certainly add a lower confidence option, but I can't find any MWU
> table lower than that on the Internet.

Why the lowest confidence? I would be interested in maximal confidence 
to reduce noise.

I found this table:

http://www.stat.purdue.edu/~bogdanm/wwwSTAT503_fall/Tables/Wilcoxon.pdf

I am not sure if those are the right values. Inside it says 
Wilcocan-Mann-Whitney U, but the filename suggests that the tables may 
be for the Wilcoxon signed-rank test.

> Also, we should modify value analysis(based how close the
> medians/minimums are) to vary according to the confidence level as
> well. However this analysis is parametric, we needs to know how data
> is actually distributed for every test. I don't think there is a
> non-parametric test which does the same thing.

What kind of problem could we get in case we assume normal distribution 
and the values are in fact not normal distributed?

Would we just fail to find a significant change? Or would we possibly 
let non-significant changes through?

Under the assumption that there is a non-zero percentage of test cases
where the performance results are normal distributed, it may be OK for a 
special low-noise-configuration to only get results from these test 
cases, but possibly ignore performance changes from the 
non-normal-distributed cases.

Cheers,
Tobias