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Similar to the ridgeline plot, you can see the distribution better without having to interpret derivatives as you do with the CDF plots.
It's little surprising that the author was doing perf work and not already comparing distributions. As the rest of the article outlines, you learn a lot more with more data! It's complicated, but really worth learning how prometheus and grafana heatmaps combine if you want dashboards for real time service data. Multimodal distributions are basically the expected outcome given all the caching done in distributed syste…
By that standard, this guy is doing quite well. It's a good post.
The common method for evaluating MPI performance on a supercomputer is with the OSU microbenchmarks, and with the -z option will print tail latencies. These are far more useful values to predict real-world performance on the big machines.
Second, while means DO say something useful, they are the top part of a profiling hierarchy. Deeper levels are sampling experiments (with minor effects on performance) to tracing (potentially larger effects on the code being measured). Seeing parallel timeline traces of a complex app has helped me and my team debug many performance problems. Look at HPCToolkit, Score-P, rocprof, and other tools.
Lastly, the reason I do computer artwork in the first place was because of a bug in an old fortran77 code which simulated turbulent mixing in 3d. I couldn't find our code mistake at first, but then I visualized the computational elements with a raytracer I had been playing with, and the result was not only shockingly beautiful, but pointed me to the line of code with the bug.
"The “after” curve is higher than the “before” curve to the right of 140ms which means more requests are finishing slower than before." Think they made a typo - "before" curve is higher than the "after" curve to the right of 140ms, which means more requests take longer than before to finish. Or am I the one misunderstanding?
My preference for visualising disributions is violin plots: https://en.wikipedia.org/wiki/Violin_plot Similar to the ridgeline plot, you can see the distribution better without having to interpret derivatives as you do with the CDF plots.
There are so many issues with average stats, not just the fact that people misunderstand it often, but also in collection and graphing, people often end up having final graph that ends up being some type of average of averages which becomes even more useless and completely meaningless.
If your metrics/monitoring system doesn't allow you to collect and graph proper distributions/percentiles, you really should change that.
It's little surprising that the author was doing perf work and not already comparing distributions. As the rest of the article outlines, you learn a lot more with more data! It's complicated, but really worth learning how prometheus and grafana heatmaps combine if you want dashboards for real time service data. Multimodal distributions are basically the expected outcome given all the caching done in distributed syste…
The median developer doing performance work has no background in the subject, they just have a complaint from someone that something is slow, and no real idea how to solve that problem. By that standard, this guy is doing quite well. It's a good post.
It's little surprising that the author was doing perf work and not already comparing distributions. As the rest of the article outlines, you learn a lot more with more data! It's complicated, but really worth learning how prometheus and grafana heatmaps combine if you want dashboards for real time service data. Multimodal distributions are basically the expected outcome given all the caching done in distributed syste…