Re: [PATCH v7 2/2] sched/fair: update scale invariance of PELT

[Patrick Bellasi <patrick.bellasi@arm.com>]

On 29-Nov 11:43, Vincent Guittot wrote:
> On Wed, 28 Nov 2018 at 17:35, Patrick Bellasi <patrick.bellasi@arm.com> wrote:
> > On 28-Nov 16:42, Vincent Guittot wrote:
> > > On Wed, 28 Nov 2018 at 16:21, Patrick Bellasi <patrick.bellasi@arm.com> wrote:
> > > > On 28-Nov 15:55, Vincent Guittot wrote:
> > > > > On Wed, 28 Nov 2018 at 15:40, Patrick Bellasi <patrick.bellasi@arm.com> wrote:
> > > > > > On 28-Nov 14:33, Vincent Guittot wrote:
> > > > > > > On Wed, 28 Nov 2018 at 12:53, Patrick Bellasi <patrick.bellasi@arm.com> wrote:
> > > > > > > > On 28-Nov 11:02, Peter Zijlstra wrote:
> > > > > > > > > On Wed, Nov 28, 2018 at 10:54:13AM +0100, Vincent Guittot wrote:
> > > > > > > > >
> > > > > > > > > > Is there anything else that I should do for these patches ?
> > > > > > > > >
> > > > > > > > > IIRC, Morten mention they break util_est; Patrick was going to explain.
> > > > > > > >
> > > > > > > > I guess the problem is that, once we cross the current capacity,
> > > > > > > > strictly speaking util_avg does not represent anymore a utilization.
> > > > > > > >
> > > > > > > > With the new signal this could happen and we end up storing estimated
> > > > > > > > utilization samples which will overestimate the task requirements.
> > > > > > > >
> > > > > > > > We will have a spike in estimated utilization at next wakeup, since we
> > > > > > > > use MAX(util_avg@dequeue_time, ewma). Potentially we also inflate the EWMA in
> > > > > > > > case we collect multiple samples above the current capacity.
> > > > > > >
> > > > > > > TBH I don't see how it's different from current implementation with a
> > > > > > > task that was scheduled on big core and now wakes up on little core.
> > > > > > > The util_est is overestimated as well.
> > > > > >
> > > > > > While running below the capacity of a CPU, either big or LITTLE, we
> > > > > > can still measure the actual used bandwidth as long as we have idle
> > > > > > time. If the task is then moved into a lower capacity core, I think
> > > > > > it's still safe to assume that, likely, it would need more capacity.
> > > > > >
> > > > > > Why do you say it's the same ?
> > > > >
> > > > > In the example of a task that runs 39ms in period of 80ms that we used
> > > > > during previous version,
> > > > > the utilization on the big core will reach 709 so will util_est too
> > > > > When the task migrates on little core (512), util_est is higher than
> > > > > current cpu capacity
> > > >
> > > > Right, and what's the problem ?
> > >
> > > you worry about an util_est being higher than capacity which is the case there
> >
> > I worry about util_est being higher then the capacity the task WAS
> > running... not the capacity the task IS running... if that value does
> > not correspond to what the task really need... (more on that at the
> > end).
> >
> > > > 1) We know that PELT is calibrated to 32ms period task and in your
> > > >    example, since the runtime is higher then the half-life, it's
> > > >    correct to estimate a utilization higher then 50%.
> > > >
> > > >    PELT utilization is defined _based on the half-life_: thus
> > > >    your task having a 50% duty cycle does not mean we are not correct
> > > >    if report a utilization != 50%.
> > > >    It would be as broken as reporting 10% utilization for a task
> > > >    running 100ms every 1s.
> > > >
> > > > 2) If it was a 70% task on a previous activation, once it's moved into
> > > >    a lower capacity CPU it's still correct to assume that it's likely
> > > >    going to require the same bandwidth and thus will be
> > > >    under-provisioned.
> > > >
> > > > I still don't see where we are wrong in this case :/
> > > >
> > > > To me it looks different then the problem I described.
> > > >
> > > > > > With your new signal instead, once we cross the current capacity,
> > > > > > utilization is just not anymore utilization. Thus, IMHO it make sense
> > > > > > avoid to accumulate a sample for what we call "estimated utilization".
> > >
> > > This is not true. With the example above, the util_est will be exactly the same
> > >  on big and little cores with the new signal
> >
> > ... AFAIU only if we have idle time...
> >
> > > > > > I would also say that, with the current implementation which caps
> > > > > > utilization to the current capacity, we get better estimation in
> > > > > > general. At least we can say with absolute precision:
> > > > > >
> > > > > >    "the task needs _at least_ that amount of capacity".
> > > > > >
> > > > > > Potentially we can also flag the task as being under-provisioned, in
> > > > > > case there was not idle time, and _let a policy_ decide what to do
> > > > > > with it and the granted information we have.
> > > > > >
> > > > > > While, with your new signal, once we are over the current capacity,
> > > > > > the "utilization" is just a sort of "random" number at best useful to
> > > > > > drive some conclusions about how long the task has been delayed.
> > >
> > > see my comment above
> > >
> > > > > >
> > > > > > IOW, I fear that we are embedding a policy within a signal which is
> > > > > > currently representing something very well defined: how much cpu
> > > > > > bandwidth a task used. While, latency/under-provisioning policies
> > > > > > perhaps should be better placed somewhere else.
> > > > > >
> > > > > > Perhaps I've missed it in some of the previous discussions:
> > > > > > have we have considered/discussed this signal-vs-policy aspect ?
> > > >
> > > > What's your opinion on the above instead ?
> > >
> > > It's not a policy but it gives better knowledge about the amount a work done
> > > I have put below discussion on the  subject on previous version
> >
> > Thanks, I think I've skimmed through it, but it's sill useful...
> >
> > > > > With contribution scaling the PELT utilization of a task is a _minimum_
> > > > > utilization. Regardless of where the task is currently/was running (and
> > > > > provided that it doesn't change behaviour) its PELT utilization will
> > > > > approximate its _minimum_ utilization on an idle 1024 capacity CPU.
> > > >
> > > > The main drawback is that the _minimum_ utilization depends on the CPU
> > > > capacity on which the task runs. The two 25% tasks on a 256 capacity
> > > > CPU will have an utilization of 128 as an example
> > > >
> > > > >
> > > > > With time scaling the PELT utilization doesn't really have a meaning on
> > > > > its own. It has to be compared to the capacity of the CPU where it
> > > > > is/was running to know what the its current PELT utilization means. When
> > > >
> > > > I would have said the opposite. The utilization of the task will
> > > > always reflect the same amount of work that has been already done
> > > > whatever the CPU capacity.
> > > > In fact, the new scaling mechanism uses the real amount of work that
> > > > has been already done to compute the utilization signal which is not
> > > > the case currently. This gives more information about the real amount
> > > > of worked that has been computed in the over utilization case.
> > > >
> > > > > the utilization over-shoots the capacity its value is no longer
> > > > > represents utilization, it just means that it has a higher compute
> > > > > demand than is offered on its current CPU and a high value means that it
> > > > > has been suffering longer. It can't be used to predict the actual
> > > > > utilization on an idle 1024 capacity any better than contribution scaled
> > > > > PELT utilization.
> > > >
> > > > I think that it provides earlier detection of over utilization and
> > > > more accurate signal for a longer time duration which can help the
> > > > load balance
> > > > Coming back to 50% task example . I will use a 50ms running time
> > > > during a 100ms period for the example below to make it easier
> > > >
> > > > Starting from 0, the evolution of the utilization is:
> > > >
> > > > With contribution scaling:
> > > >          time  0ms  50ms  100ms  150ms  200ms
> > > > capacity
> > > > 1024           0    666
> > > > 512            0    333   453
> > > > When the CPU start to be over utilized (@100ms), the utilization is
> > > > already too low (453 instead of 666) and scheduler doesn't detect yet
> > > > that we are over utilized
> > > > 256            0    169   226    246    252
> > > > That's even worse with this lower capacity
> > > >




> > > > With time scaling,
> > > >          time  0ms  50ms  100ms  150ms  200ms
> > > > capacity
> > > > 1024           0    666
> > > > 512            0    428   677
> > > > 256            0    234   468    564    677

[...]

> > I like the idea that we ramp up faster and always get to the same
> > value. I like also the idea that we always reach the same value on
> > both LITTLE and big.
> >
> > As long as there is idle time this is working fine, in these cases we
> > should probably also collect util_est samples.
> >
> > But what happens when we don't have idle time ?
> 
> As shown above, the utilization stays correct for a longer time frame
> even after the over utilization point and provides better over
> utilization detection
> 
> >
> > Let say we have 2 15% tasks, co-scheduled on a cpu with <300 capacity.
> >
> > Are not these two tasks being reported as 50% tasks (after a while) ?
> 
> Yes they will but similarly to above they will stay correct for longer
> time even when they become higher than current cpu capacity

Seems we agree that, when there is no idle time:
- the two 15% tasks will be overestimated
- their utilization will reach 50% after a while

If I'm not wrong, we will have:
- 30% CPU util in  ~16ms @1024 capacity
                   ~64ms  @256 capacity

Thus, the tasks will be certainly over-estimated after ~64ms.
Is that correct ?

> > If that's the case, these are samples we should not store...

Now, we can argue that 64ms is a pretty long time and thus it's quite
unlucky we will have no idle for such a long time.

Still, I'm wondering if we should keep collecting those samples or
better find a way to detect that and skip the sampling.

-- 
#include <best/regards.h>

Patrick Bellasi