On Tue, Jul 18, 2017 at 11:14:57AM +0800, Li, Aubrey wrote: > On 2017/7/18 3:23, Peter Zijlstra wrote: > > On Fri, Jul 14, 2017 at 09:26:19AM -0700, Andi Kleen wrote: > >>> And as said; Daniel has been working on a better predictor -- now he's > >>> probably not used it on the network workload you're looking at, so that > >>> might be something to consider. > >> > >> Deriving a better idle predictor is a bit orthogonal to fast idle. > > > > No. If you want a different C state selected we need to fix the current > > C state selector. We're not going to tinker. > > > > And the predictor is probably the most fundamental part of the whole C > > state selection logic. > > > > Now I think the problem is that the current predictor goes for an > > average idle duration. This means that we, on average, get it wrong 50% > > of the time. For performance that's bad. > > > > If you want to improve the worst case, we need to consider a cumulative > > distribution function, and staying with the Gaussian assumption already > > present, that would mean using: > > > > 1 x - mu > > CDF(x) = - [ 1 + erf(-------------) ] > > 2 sigma sqrt(2) > > > > Where, per the normal convention mu is the average and sigma^2 the > > variance. See also: > > > > https://en.wikipedia.org/wiki/Normal_distribution > > > > We then solve CDF(x) = n% to find the x for which we get it wrong n% of > > the time (IIRC something like: 'mu - 2sigma' ends up being 5% or so). > > > > This conceptually gets us better exit latency for the cases where we got > > it wrong before, and practically pushes down the estimate which gets us > > C1 longer. > > > > Of course, this all assumes a Gaussian distribution to begin with, if we > > get bimodal (or worse) distributions we can still get it wrong. To fix > > that, we'd need to do something better than what we currently have. > > > Maybe you are talking about applying some machine learning algorithm online > to fit a multivariate normal distribution, :)
See here for an implementation of what I spoke about above: https://lkml.kernel.org/r/20170719133940.uytsixvfgpmo3...@hirez.programming.kicks-ass.net Very much statistics 101.