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    SubjectStatistical methods for latency profiling

    Recently Ingo Molnar asked in one of the voluntary-preempt threads for
    the minimum and average scheduling delay reported by jackd. JACK does
    not currently maintain these statistics.

    I realized that the distribution of maximum latencies reported on each
    process cycle is fairly normally distributed. If this data followed a
    normal distribution, it becomes much easier to make generalizations
    about it - the standard deviation becomes meaningful, we can identify
    whether a change is statistically significant, etc. It would also
    indicate the effectiveness of the Linux scheduler by identifying any
    skew - the distribution in fact should be normal, as the only factor
    that should be influencing this value is the variance in lengths of
    whatever non-preemptible sections the kernel was in when we became

    The first step in being able to meaningfully analyze these results is to
    determine the degree to which distribution is in fact normal. The first
    step in this process is to just to look at a histogram of the data.

    In any jackd engine, the allowable window for scheduler latency is:

    (0, period_usecs/2)

    A latency in the range:

    [period_usecs/2, period_usecs]

    will cause jackd to restart, and of course anything over period_usecs is
    an XRUN.

    Thus we can build a histogram of the observed latencies by creating an
    array with period_usecs/2 elements, and adding 1 to each "bin" whenever
    we see that value. Using 32 frames at 48KHz, we have 333 "bins".

    The overhead is not too bad, because we only look at the highest latency
    for each period (max_usecs), so we only have to update the histogram
    once per period - if the underlying distribution is normal then the
    distribution of the maximimums will be as well.

    The ability to use statistical methods to generalize about this data
    will be increasingly important moving forward, because this is the
    *only* way we can provide hard numbers: confidence intervals, p-values,
    standard deviations, etc. rather than "It feels faster" or "Since $FOO
    change the system is sluggish". Statistical methods would allow us to
    say "$FOO change increases the mean latency from 150 to 200 usecs, but
    the standard deviation is now 20 rather than 75, so it's actually an

    Any change in the kernel that makes a difference in the "feel" of the
    system is now open to analysis. Many developers apparently do not
    consider a user report of "It feels sluggish" to be as much of a concern
    as "X took Y amount of time now it takes Z". Statistical methods can
    close this gap.

    Anyone care to comment?


    PS I did not do very well in my statistics classes, and it's possible
    that many kernel hackers are completely unfamiliar with statistical
    methods. Fortunately the subset that is immediately useful to us is
    pretty easy to understand, and could be covered in one or two lectures,
    or a HOWTO. Depending on the level of interest I can create one.

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