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SubjectRe: Linux 2.6.29-rc6

* 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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