[Intel-gfx] [RFC] GPU-bound energy efficiency improvements for the intel_pstate driver (v2.99)

Francisco Jerez currojerez at riseup.net
Fri May 15 00:48:34 UTC 2020

Valentin Schneider <valentin.schneider at arm.com> writes:

> (+Lukasz)
> On 11/05/20 22:01, Francisco Jerez wrote:
>>> What I'm missing is an explanation for why this isn't using the
>>> infrastructure that was build for these kinds of things? The thermal
>>> framework, was AFAIU, supposed to help with these things, and the IPA
>>> thing in particular is used by ARM to do exactly this GPU/CPU power
>>> budget thing.
>>> If thermal/IPA is found wanting, why aren't we improving that?
>> The GPU/CPU power budget "thing" is only a positive side effect of this
>> series on some TDP-bound systems.  Its ultimate purpose is improving the
>> energy efficiency of workloads which have a bottleneck on a device other
>> than the CPU, by giving the bottlenecking device driver some influence
>> over the response latency of CPUFREQ governors via a PM QoS interface.
>> This seems to be completely outside the scope of the thermal framework
>> and IPA AFAIU.
> It's been a while since I've stared at IPA, but it does sound vaguely
> familiar.
> When thermally constrained, IPA figures out a budget and splits it between
> actors (cpufreq and devfreq devices) depending on how much juice they are
> asking for; see cpufreq_get_requested_power() and
> devfreq_cooling_get_requested_power(). There's also some weighing involved.

I'm aware of those.  Main problem is that the current mechanism for IPA
to figure out the requested power of each actor is based on a rough
estimate of their past power consumption: If an actor was operating at a
highly energy-inefficient regime it will end up requesting more power
than another actor with the same load but more energy-efficient
behavior.  The IPA power allocator is therefore ineffective at improving
the energy efficiency of an agent beyond its past behavior --
Furthermore it seems to *rely* on individual agents being somewhat
energetically responsible in order for its power allocation result to be
anywhere close to optimal.  But improving the energy efficiency of an
agent seems useful in its own right, whether IPA is used to balance
power across agents or not.  That's precisely the purpose of this

> If you look at the cpufreq cooling side of things, you'll see it also uses
> the PM QoS interface. For instance, should IPA decide to cap the CPUs
> (perhaps because say the GPU is the one drawing most of the juice), it'll
> lead to a maximum frequency capping request.
> So it does sound like that's what you want, only not just when thermally
> constrained.

Capping the CPU frequency from random device drivers is highly
problematic, because the CPU is a shared resource which a number of
different concurrent applications might be using beyond the GPU client.
The GPU driver has no visibility over its impact on the performance of
other applications.  And even in a single-task environment, in order to
behave as effectively as the present series the GPU driver would need to
monitor the utilization of *all* CPUs in the system and place a
frequency constraint on each one of them (since there is the potential
of the task scheduler migrating the process from one CPU to another
without notice).  Furthermore these frequency constraints would need to
be updated at high frequency in order to avoid performance degradation
whenever the balance of load between CPU and IO device fluctuates.

The present series attempts to remove the burden of computing frequency
constraints out of individual device drivers into the CPUFREQ governor.
Instead the device driver provides a response latency constraint when it
encounters a bottleneck, which can be more easily derived from hardware
and protocol characteristics than a CPU frequency.  PM QoS aggregates
the response latency constraints provided by all applications and gives
CPUFREQ a single response latency target compatible with all of them (so
a device driver specifying a high latency target won't lead to
performance degradation in a concurrent application with lower latency
constraints).  The CPUFREQ governor then computes frequency constraints
for each CPU core that minimize energy usage without limiting
throughput, based on the results obtained from CPU performance counters,
while guaranteeing that a discontinuous transition in CPU utilization
leads to a proportional transition in the CPU frequency before the
specified response latency has elapsed.
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