On Tue, May 10, 2016 at 12:31 PM, Tomas Vondra
<tomas.von...@2ndquadrant.com> wrote:
> The following table shows the differences between the disabled and reverted
> cases like this:
>
> sum('reverted' results with N clients)
> ---------------------------------------- - 1.0
> sum('disabled' results with N clients)
>
> for each scale/client count combination. So for example 4.83% means with a
> single client on the smallest data set, the sum of the 5 runs for reverted
> was about 1.0483x than for disabled.
>
> scale 1 16 32 64 128
> 100 4.83% 2.84% 1.21% 1.16% 3.85%
> 3000 1.97% 0.83% 1.78% 0.09% 7.70%
> 10000 -6.94% -5.24% -12.98% -3.02% -8.78%
/me scratches head.
That doesn't seem like noise, but I don't understand the
scale-factor-10000 results either. Reverting the patch makes the code
smaller and removes instructions from critical paths, so it should
speed things up at least nominally. The question is whether it makes
enough difference that anyone cares. However, removing unused code
shouldn't make the system *slower*, but that's what's happening here
at the higher scale factor.
I've seen cases where adding dummy instructions to critical paths
slows things down at 1 client and speeds them up with many clients.
That happens because the percentage of time active processes fighting
over the critical locks goes down, which reduces contention more than
enough to compensate for the cost of executing the dummy instructions.
If your results showed performance lower at 1 client and slightly
higher at many clients, I'd suspect an effect of that sort. But I
can't see why it should depend on the scale factor. That suggests
that, perhaps, it's having some effect on the impact of buffer
eviction, maybe due to a difference in shared memory layout. But I
thought we weren't supposed to have such artifacts any more now that
we start every allocation on a cache line boundary...
--
Robert Haas
EnterpriseDB: http://www.enterprisedb.com
The Enterprise PostgreSQL Company
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