Subject: Re: [RFC PATCH v1 00/11] Create fast idle path for short idle periods
To: Peter Zijlstra <peterz@infradead.org>, Andi Kleen <ak@linux.intel.com>
Cc: Frederic Weisbecker <fweisbec@gmail.com>,
        Christoph Lameter <cl@linux.com>, Aubrey Li <aubrey.li@intel.com>,
        tglx@linutronix.de, len.brown@intel.com, rjw@rjwysocki.net,
        tim.c.chen@linux.intel.com, arjan@linux.intel.com,
        paulmck@linux.vnet.ibm.com, yang.zhang.wz@gmail.com, x86@kernel.org,
        linux-kernel@vger.kernel.org, daniel.lezcano@linaro.org
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From: "Li, Aubrey" <aubrey.li@linux.intel.com>
Message-ID: <34371ef8-b8bc-d2bf-93de-3fccd6beb032@linux.intel.com>
Date: Tue, 18 Jul 2017 11:14:57 +0800
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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, :)

Well, back to the problem, when the scheduler picks up idle thread, it does
not look at the history, nor make the prediction. So it's possible it has
to switch back a task ASAP when it's going into idle(very common under some
workloads).

That is, (idle_entry + idle_exit) > idle. If the system has multiple
hardware idle states, then:

(idle_entry + idle_exit + HW_entry + HW_exit) > HW_sleep

So we eventually want the idle path lighter than what we currently have.
A complex predictor may have high accuracy, but the cost could be high as well.

We need a tradeoff here IMHO. I'll check Daniel's work to understand how/if
it's better than menu governor.

Thanks,
-Aubrey