Messages in this thread | | | From | Vincent Guittot <> | Date | Thu, 29 Nov 2018 17:19:51 +0100 | Subject | Re: [PATCH v7 2/2] sched/fair: update scale invariance of PELT |
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On Thu, 29 Nov 2018 at 16:00, Patrick Bellasi <patrick.bellasi@arm.com> wrote: > > 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 ?
From a pure util_avg pov it's correct But i'd like to weight that a bit with the example below
> > > > 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.
The problem is that you can have util_avg above capacity even with idle time In the 1st example of this thread, the 39ms/80ms task will reach 709 which is the value saved by util_est on a big core But on core with half capacity, there is still idle time so 709 is a correct value although above 512
In fact, max will be always above the linear ratio because it's based on geometric series
And this is true even with 15.6ms/32ms (same ratio as above) task although the impact is smaller (max value, which should be saved by util est, becomes 587 in this case).
> > -- > #include <best/regards.h> > > Patrick Bellasi
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