Message-ID: <20180717141955.GA4496@e108498-lin.cambridge.arm.com>
Date:   Tue, 17 Jul 2018 15:19:56 +0100
From:   Quentin Perret <quentin.perret@....com>
To:     Dietmar Eggemann <dietmar.eggemann@....com>
Cc:     peterz@...radead.org, rjw@...ysocki.net,
        linux-kernel@...r.kernel.org, linux-pm@...r.kernel.org,
        gregkh@...uxfoundation.org, mingo@...hat.com,
        morten.rasmussen@....com, chris.redpath@....com,
        patrick.bellasi@....com, valentin.schneider@....com,
        vincent.guittot@...aro.org, thara.gopinath@...aro.org,
        viresh.kumar@...aro.org, tkjos@...gle.com, joel@...lfernandes.org,
        smuckle@...gle.com, adharmap@...cinc.com, skannan@...cinc.com,
        pkondeti@...eaurora.org, juri.lelli@...hat.com,
        edubezval@...il.com, srinivas.pandruvada@...ux.intel.com,
        currojerez@...eup.net, javi.merino@...nel.org
Subject: Re: [RFC PATCH v4 03/12] PM: Introduce an Energy Model management
 framework

Hi Dietmar,

On Tuesday 17 Jul 2018 at 10:57:13 (+0200), Dietmar Eggemann wrote:
> On 07/16/2018 12:29 PM, Quentin Perret wrote:
> I see an impact of 'calculating capacity on the fly' in
> compute_energy()->em_fd_energy(). Running the first energy test case (# task
> equal 10) on the Juno r0 board with function profiling gives me:
> 
> v4:
> 
>   Function              Hit    Time            Avg             s^2
> A53 - cpu [0,3-5]
>   compute_energy      14620    30790.86 us     2.106 us        8.421 us
>   compute_energy        682    1512.960 us     2.218 us        0.154 us
>   compute_energy       1207    2627.820 us     2.177 us        0.172 us
>   compute_energy         93    206.720 us      2.222 us        0.151 us
> A57 - cpu [1-2]
>   compute_energy        153    160.100 us      1.046 us        0.190 us
>   compute_energy        136    130.760 us      0.961 us        0.077 us
> 
> 
> v4 + 'calculating capacity on the fly':
> 
>   Function              Hit    Time            Avg             s^2
> A53 - cpu [0,3-5]
>   compute_energy      11623    26941.12 us     2.317 us        12.203 us
>   compute_energy       5062    11771.48 us     2.325 us        0.819 us
>   compute_energy       4391    10396.78 us     2.367 us        1.753 us
>   compute_energy       2234    5265.640 us     2.357 us        0.955 us
> A57 - cpu [1-2]
>   compute_energy         59    66.020 us       1.118 us        0.112 us
>   compute_energy        229    234.880 us      1.025 us        0.135 us
> 
> The code is not optimized, I just replaced cs->capacity with
> arch_scale_cpu_capacity(NULL, cpu) (max_cap) and 'max_cap * cs->frequency /
> max_freq' respectively.
> There are 3 compute_energy() calls per wake-up on a system with 2 frequency
> domains.

First, thank you very much for looking into this :-)

So, I guess you see this overhead because of the extra division involved
by computing 'cap = max_cap * cs->frequency / max_freq'. However, I
think there is an opportunity to optimize things a bit and avoid that
overhead entirely. My suggestion is to remove the 'capacity' field from
the em_cap_state struct and to add a 'cost' parameter instead:

struct em_cap_state {
	unsigned long frequency;
	unsigned long power;
	unsigned long cost;
};

I define the 'cost' of a capacity state as:

	cost = power * max_freq / freq;

Since 'power', 'max_freq' and 'freq' do not change at run-time (as opposed
to 'capacity'), this coefficient is static and computed when the table is
first created. Then, based on this, you can implement em_fd_energy() as
follows:

static inline unsigned long em_fd_energy(struct em_freq_domain *fd,
				unsigned long max_util, unsigned long sum_util)
{
	unsigned long freq, scale_cpu;
	struct em_cap_state *cs;
	int i, cpu;

	/* Map the utilization value to a frequency */
	cpu = cpumask_first(to_cpumask(fd->cpus));
	scale_cpu = arch_scale_cpu_capacity(NULL, cpu);
	cs = &fd->table[fd->nr_cap_states - 1];
	freq = map_util_freq(max_util, cs->frequency, scale_cpu);

	/* Find the lowest capacity state above this frequency */
	for (i = 0; i < fd->nr_cap_states; i++) {
		cs = &fd->table[i];
		if (cs->frequency >= freq)
			break;
	}

	/*
	 * The capacity of a CPU at a specific performance state is defined as:
	 *
	 *     cap = freq * scale_cpu / max_freq
	 *
	 * The energy consumed by this CPU can be estimated as:
	 *
	 *     nrg = power * util / cap
	 *
	 * because (util / cap) represents the percentage of busy time of the
	 * CPU. Based on those definitions, we have:
	 *
	 *     nrg = power * util * max_freq / (scale_cpu * freq)
	 *
	 * which can be re-arranged as a product of two terms:
	 *
	 *     nrg = (power * max_freq / freq) * (util / scale_cpu)
	 *
	 * The first term is static, and is stored in the em_cap_state struct
	 * as 'cost'. The parameters of the second term change at run-time.
	 */
	return cs->cost * sum_util / scale_cpu;
}

With the above implementation, there is no additional division in
em_fd_energy() compared to v4, so I would expect to see no significant
difference in computation time.

I tried to reproduce your test case and I get the following numbers on
my Juno r0 (using the performance governor):

v4:
***
  Function           Hit    Time            Avg             s^2
A53 - cpu [0,3-5]
  compute_energy    1796    12685.66 us     7.063 us        0.039 us
  compute_energy    4214    28060.02 us     6.658 us        0.919 us
  compute_energy    2743    20167.86 us     7.352 us        0.067 us
  compute_energy   13958    97122.68 us     6.958 us        9.255 us
A57 - cpu [1-2]
  compute_energy      86    448.800 us      5.218 us        0.106 us
  compute_energy     163    847.600 us      5.200 us        0.128 us


'v5' (with 'cost'):
*******************
  Function           Hit    Time            Avg             s^2
A53 - cpu [0,3-5]
  compute_energy    1695    11153.54 us     6.580 us        0.022 us
  compute_energy   16823    113709.5 us     6.759 us        27.109 us
  compute_energy     677    4490.060 us     6.632 us        0.028 us
  compute_energy    1959    13595.66 us     6.940 us        0.029 us
A57 - cpu [1-2]
  compute_energy     211    1089.860 us     5.165 us        0.122 us
  compute_energy      83    420.860 us      5.070 us        0.075 us


So I don't observe any obvious regression with my optimization applied.
The v4 branch I used is the one mentioned in the cover letter:
http://www.linux-arm.org/git?p=linux-qp.git;a=shortlog;h=refs/heads/upstream/eas_v4

And I just pushed the WiP branch I used to compare against:
http://www.linux-arm.org/git?p=linux-qp.git;a=shortlog;h=refs/heads/upstream/eas_v5-WiP-compute_energy_profiling

Is this also fixing the regression on your side ?

> 
> > The second option simplifies the code of the EM framework significantly
> > (no more em_rescale_cpu_capacity()) and shouldn't introduce massive
> > overheads on the scheduler side (the energy calculation already
> > requires one multiplication and one division, so nothing new on that
> > side). At the same time, that would make it a whole lot easier to
> > interface the EM framework with IPA without having to deal with RCU all
> > over the place.
> 
> IMO, em_rescale_cpu_capacity() is just the capacity related example what the
> EM needs if its values can be changed at runtime. There might be other use
> cases in the future like changing power values depending on temperature.
> So maybe it's a good idea to not have this 'EM values can change at runtime'
> feature in the first version of the EM and emphasize on simplicity of the
> code instead (if we can eliminate the extra runtime overhead).

I agree that it would be nice to keep it simple in the beginning. If
there is strong and demonstrated use-case for updating the EM at
run-time later, then we can re-introduce the RCU protection. But until
then, we can avoid the complex implementation at no obvious cost (given
my results above) so that sounds like a good trade-off to me :-)

Thanks,
Quentin

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