Hi Amit, On Tue, Aug 19, 2014 at 11:11 AM, Amit Kucheria <amit.kuche...@linaro.org> wrote: > > We’re soliciting early feedback from community on the direction of idlestat
Nice :) > Idlestat Details > ---------------- > Idlestat uses FTRACE to capture traces related to C-state and P-state > transitions of the CPU and wakeups (IRQ, IPI) on the system and then > post-processes the data to print statistics. It is designed to be used > non-interactively. Idlestat can deduce the idle time for a cluster as an > intersection between the idle times of all the cpus belonging to the same > cluster. This data is useful to analyse and optimise scheduling behaviour. > The tool will also list how many times the menu governor mis-predicts > target residency in a C-state. We discussed this in the energy aware scheduling workshop this week @ the Kernel Summit. A few notes: 1. We need to really understand the co-relation of this tool w.r.t actual hardware states. It is usually likely that the software "thinks" it is in a low power state, but the actual hardware might not be. What is the coverage for these kind of cases here. 2. I understand that C/P states are a direct metric of how well the workload behaved w.r.t power; but I am not sure that relates to a direct measure of how the scheduler performed. The C/P states could be maintained whilst giving away performance or power at the expense of additional components on the SoC and platform like DDR IOs, fabric states etc. Quick Summary of what I discussed with Daniel @ the workshop about idlestat: 1. There might be usually platform specific tools to get residencies for P/C states. PowerTop & Turbostat are two that first come to mind. Any specific item apart from prediction logic that idlestat differs from these two? 2. To me debugging performance or power, C/P states provide the direction that something is wrong. But they still dont tell me "what" is wrong "if" the issue is somehow in the kernel as opposed to a more easily fixable software code (traceable at hardware/software level for best optimizations). How do I conclude that my scheduler is the culprit apart from the points where it took a decision to select the right idle states based on predicted sleep times? In my opinion, that would boil down to if the scheduler was invoking too much load balancing calls, moving my threads across cores too much, data being thrashed across caches, cores too much etc. I think a tool for scheduler metrics must be based on more inner details like the above, finally culminating into C/P states. as opposed to C/P states being the metric to be relied. Let me know your thoughts. Cheers! -- these are my personal thoughts and do not represent my employers' -- To unsubscribe from this list: send the line "unsubscribe linux-kernel" in the body of a message to majord...@vger.kernel.org More majordomo info at http://vger.kernel.org/majordomo-info.html Please read the FAQ at http://www.tux.org/lkml/