Re: [PATCH] perf, tools: Support spark lines in perf stat v2

[Peter Zijlstra]

On Mon, May 12, 2014 at 04:01:26PM -0700, Andi Kleen wrote:
> From: Andi Kleen <ak@xxxxxxxxxxxxxxx>
> 
> perf stat -rX prints the stddev for multiple measurements.
> Just looking at the stddev for judging the quality of the data
> is a bit dangerous The simplest sanity check is to just look
> at a simple plot. This patchs add a sparkline to the end
> of the measurements to make it simple to judge the data.
> 
> The sparkline only uses UTF-8, so should be readable
> in all modern tools and terminals.
> 
> The sparkline is between the minimum and maximum of the data,
> so it's mainly a indicator of variance. To keep the code
> simple and make the output not too wide only the first
> 8 values are printed. If more values are there it adds '..'
> 
> The code is inspired by Zach Holman's spark shell script.
> 
> Example output (view in non-proportial font):
> 
>  Performance counter stats for 'true' (10 runs):
> 
>           0.175672      task-clock (msec)         #    0.555 CPUs utilized            ( +-  1.77% ) ââââââââ..
>                  0      context-switches          #    0.000 K/sec
>                  0      cpu-migrations            #    0.000 K/sec
>                114      page-faults               #    0.647 M/sec                    ( +-  0.14% ) ââââââââ..
>            520,798      cycles                    #    2.965 GHz                      ( +-  1.75% ) ââââââââ..
>            433,525      instructions              #    0.83  insns per cycle          ( +-  0.28% ) ââââââââ..
>             83,012      branches                  #  472.537 M/sec                    ( +-  0.31% ) ââââââââ..
>              3,157      branch-misses             #    3.80% of all branches          ( +-  2.55% ) ââââââââ..
> 
>        0.000316660 seconds time elapsed                                          ( +-  1.78% ) ââââââââ..
> 
> As you can see even in the most simple run there are quite interesting
> patterns. The time sparkline suggests it would be also useful to have an option
> to throw the first measurement away.

Hmm, my first looking at the spark thingies interpreted them as a
histogram. But they're not really.

Would it be possible to make it a histogram of -2sigma,2sigma around the
avg value? That way you can plot all data and get a good idea of the
distribution. I'd suggest using 9 buckets for it.

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