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Date: Tue, 8 Jan 2013 15:04:12 +0100 From: Vincent Guittot <vincent.guittot@...aro.org> To: Preeti U Murthy <preeti@...ux.vnet.ibm.com> Cc: Mike Galbraith <bitbucket@...ine.de>, Matthew Garrett <mjg59@...f.ucam.org>, LKML <linux-kernel@...r.kernel.org>, "svaidy@...ux.vnet.ibm.com" <svaidy@...ux.vnet.ibm.com>, "Paul E. McKenney" <paulmck@...ux.vnet.ibm.com>, Peter Zijlstra <a.p.zijlstra@...llo.nl>, Viresh Kumar <viresh.kumar@...aro.org>, Amit Kucheria <amit.kucheria@...aro.org>, Morten Rasmussen <Morten.Rasmussen@....com>, Paul McKenney <paul.mckenney@...aro.org>, Andrew Morton <akpm@...ux-foundation.org>, Arjan van de Ven <arjan@...ux.intel.com>, Ingo Molnar <mingo@...nel.org>, Paul Turner <pjt@...gle.com>, Venki Pallipadi <venki@...gle.com>, Robin Randhawa <robin.randhawa@....com>, Lists linaro-dev <linaro-dev@...ts.linaro.org> Subject: Re: sched: Consequences of integrating the Per Entity Load Tracking Metric into the Load Balancer On 8 January 2013 07:06, Preeti U Murthy <preeti@...ux.vnet.ibm.com> wrote: > On 01/07/2013 09:18 PM, Vincent Guittot wrote: >> On 2 January 2013 05:22, Preeti U Murthy <preeti@...ux.vnet.ibm.com> wrote: >>> Hi everyone, >>> I have been looking at how different workloads react when the per entity >>> load tracking metric is integrated into the load balancer and what are >>> the possible reasons for it. >>> >>> I had posted the integration patch earlier: >>> https://lkml.org/lkml/2012/11/15/391 >>> >>> Essentially what I am doing is: >>> 1.I have disabled CONFIG_FAIR_GROUP_SCHED to make the analysis simple >>> 2.I have replaced cfs_rq->load.weight in weighted_cpuload() with >>> cfs.runnable_load_avg,the active load tracking metric. >>> 3.I have replaced se.load.weight in task_h_load() with >>> se.load.avg.contrib,the per entity load tracking metric. >>> 4.The load balancer will end up using these metrics. >>> >>> After conducting experiments on several workloads I found out that the >>> performance of the workloads with the above integration would neither >>> improve nor deteriorate.And this observation was consistent. >>> >>> Ideally the performance should have improved considering,that the metric >>> does better tracking of load. >>> >>> Let me explain with a simple example as to why we should see a >>> performance improvement ideally:Consider 2 80% tasks and 1 40% task. >>> >>> With integration: >>> ---------------- >>> >>> 40% >>> 80% 40% >>> cpu1 cpu2 >>> >>> The above will be the scenario when the tasks fork initially.And this is >>> a perfectly balanced system,hence no more load balancing.And proper >>> distribution of loads on the cpu. >>> >>> Without integration >>> ------------------- >>> >>> 40% 40% >>> 80% 40% 80% 40% >>> cpu1 cpu2 OR cpu1 cpu2 >>> >>> Because the view is that all the tasks as having the same load.The load >>> balancer could ping pong tasks between these two situations. >>> >>> When I performed this experiment,I did not see an improvement in the >>> performance though in the former case.On further observation I found >>> that the following was actually happening. >>> >>> With integration >>> ---------------- >>> >>> Initially 40% task sleeps 40% task wakes up >>> and select_idle_sibling() >>> decides to wake it up on cpu1 >>> >>> 40% -> -> 40% >>> 80% 40% 80% 40% 80% 40% >>> cpu1 cpu2 cpu1 cpu2 cpu1 cpu2 >>> >>> >>> This makes load balance trigger movement of 40% from cpu1 back to >>> cpu2.Hence the stability that the load balancer was trying to achieve is >>> gone.Hence the culprit boils down to select_idle_sibling.How is it the >>> culprit and how is it hindering performance of the workloads? >>> >>> *What is the way ahead with the per entity load tracking metric in the >>> load balancer then?* >>> >>> In replies to a post by Paul in https://lkml.org/lkml/2012/12/6/105, >>> he mentions the following: >>> >>> "It is my intuition that the greatest carnage here is actually caused >>> by wake-up load-balancing getting in the way of periodic in >>> establishing a steady state. I suspect more mileage would result from >>> reducing the interference wake-up load-balancing has with steady >>> state." >>> >>> "The whole point of using blocked load is so that you can converge on a >>> steady state where you don't NEED to move tasks. What disrupts this is >>> we naturally prefer idle cpus on wake-up balance to reduce wake-up >>> latency. I think the better answer is making these two processes load >>> balancing() and select_idle_sibling() more co-operative." >>> >>> I had not realised how this would happen until I saw it happening in the >>> above experiment. >>> >>> Based on what Paul explained above let us use the runnable load + the >>> blocked load for calculating the load on a cfs runqueue rather than just >>> the runnable load(which is what i am doing now) and see its consequence. >>> >>> Initially: 40% task sleeps >>> >>> 40% >>> 80% 40% -> 80% 40% >>> cpu1 cpu2 cpu1 cpu2 >>> >>> So initially the load on cpu1 is say 80 and on cpu2 also it is >>> 80.Balanced.Now when 40% task sleeps,the total load on cpu2=runnable >>> load+blocked load.which is still 80. >>> >>> As a consequence,firstly,during periodic load balancing the load is not >>> moved from cpu1 to cpu2 when the 40% task sleeps.(It sees the load on >>> cpu2 as 80 and not as 40). >>> Hence the above scenario remains the same.On wake up,what happens? >>> >>> Here comes the point of making both load balancing and wake up >>> balance(select_idle_sibling) co operative. How about we always schedule >>> the woken up task on the prev_cpu? This seems more sensible considering >>> load balancing considers blocked load as being a part of the load of cpu2. >> >> Hi Preeti, >> >> I'm not sure that we want such steady state at cores level because we >> take advantage of migrating wake up tasks between cores that share >> their cache as Matthew demonstrated. But I agree that reaching such >> steady state at cluster and CPU level is interesting. >> >> IMHO, you're right that taking the blocked load into consideration >> should minimize tasks migration between cluster but it should no >> prevent fast task migration between cores that share their cache > > True Vincent.But I think the one disadvantage even at cpu or cluster > level is that when we consider blocked load, we might prevent any more > tasks from being scheduled on that cpu during periodic load balance if > the blocked load is too much.This is very poor cpu utilization The blocked load of a cluster will be high if the blocked tasks have run recently. The contribution of a blocked task will be divided by 2 each 32ms, so it means that a high blocked load will be made of recent running tasks and the long sleeping tasks will not influence the load balancing. The load balance period is between 1 tick (10ms for idle load balance on ARM) and up to 256 ms (for busy load balance) so a high blocked load should imply some tasks that have run recently otherwise your blocked load will be small and will not have a large influence on your load balance > > Also we can consider steady states if the waking tasks have a specific > waking pattern.I am not sure if we can risk hoping that the blocked task > would wake up soon or would wake up at time 'x' and utilize that cpu. Ok, so you don't consider to use blocked load in load balancing any more ? regards, Vincent > >> >> Vincent > > Regards > Preeti U Murthy > -- To unsubscribe from this list: send the line "unsubscribe linux-kernel" in the body of a message to majordomo@...r.kernel.org More majordomo info at http://vger.kernel.org/majordomo-info.html Please read the FAQ at http://www.tux.org/lkml/
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