Messages in this thread | | | Date | Tue, 17 Mar 2009 17:13:22 +0100 | From | Ingo Molnar <> | Subject | Re: Linux 2.6.29-rc6 |
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* Linus Torvalds <torvalds@linux-foundation.org> wrote:
> On Tue, 17 Mar 2009, Ingo Molnar wrote: > > > > Cool. Will you apply it yourself (in the merge window) or should > > we pick it up? > > I'll commit it. I already split it into two commits - one for the > trivial startup problem that John had, one for the "estimate error > and exit when smaller than 500ppm" part.
ok.
> > Incidentally, yesterday i wrote a PIT auto-calibration routine > > (see WIP patch below). > > > > The core idea is to use _all_ thousands of measurement points > > (not just two) to calculate the frequency ratio, with a built-in > > noise detector which drops out of the loop if the observed noise > > goes below ~10 ppm. > > I suspect that reaching 10 ppm is going to take too long in > general. Considering that I found a machine where reaching 500ppm > took 16ms, getting to 10ppm would take almost a second. That's a > long time at bootup, considering that people want the whole boot > to take about that time ;) > > I also do think it's a bit unnecessarily complicated. We really > only care about the end points - obviously we can end up being > unlucky and get a very noisy end-point due to something like SMI > or virtualization, but if that happens, we're really just better > off failing quickly instead, and we'll go on to the slower > calibration routines.
That's the idea of my patch: to use not two endpoints but thousands of measurement points. That way we dont have to worry about the precision of the endpoints - any 'bad' measurement will be counter-acted by thousands of 'good' measurements.
That's the theory at least - practice got in my way ;-)
By measuring more we can get a more precise result, and we also do not assume anything about how much time passes between two measurement points. A single measurement is:
+ /* + * We use the PIO accesses as natural TSC serialization barriers: + */ + pit_lsb = inb(0x42); + tsc = get_cycles(); + pit_msb = inb(0x42); Just like we can prove that there's an exoplanet around a star, just by doing a _ton_ of measurements of a very noisy data source. As long as there's an underlying physical value to be measured (and we are not measuring pure noise) that value is recoverable, with enough measurements.
> On real hardware without SMI or virtualization overhead, the > delays _should_ be very stable. On my main machine, for example, > the PIT read really seems very stable at about 2.5us (which > matches the expectation that one 'inb' should take roughly one > microsecond pretty closely). So that should be the default case, > and the case that the fast calibration is designed for. > > For the other cases, we really can just exit and do something > else. > > > It's WIP because it's not working yet (or at all?): i couldnt > > get the statistical model right - it's too noisy at 1000-2000 > > ppm and the frequency result is off by 5000 ppm. > > I suspect your measurement overhead is getting noticeable. You do > all those divides, but even more so, you do all those traces. > Also, it looks like you do purely local pairwise analysis at > subsequent PIT modelling points, which can't work - you need to > average over a long time to stabilize it.
Actually, it's key to my trick that what happens _between_ the measurement points does not matter _at all_.
My 'delta' algorithm does not assume anything about how much time passes between two measurement points - it calculates the slope and keeps a rolling average of that slope.
That's why i could put the delta analysis there. We are capturing thousands of measurement points, and what matters is the precision of the 'pair' of (PIT,TSC) timestamp measurements.
I got roughly the same end result noise and the same anomalies with tracing enabled and disabled. (and the number of data points was cut in half with tracing enabled)
Ingo
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