I’m quite confused by your comment. I think this subthread is about reasons one might see variance on modern hardware in your ‘most control’ end of the continuum.

The start of the thread was about some ways where VM warmup (and benchmarking) may behave differently from how many reasonably experienced people expect.

I claim it’s reasonably different because one cannot tame parts of the VM warmup issues whereas one can tame many of the sources of variability one sees in high-performance systems outside of VMs, eg by cutting out the OS/scheduler, by disabling power saving and properly cooling the chip, by using CAT to limit interference with the L3 cache, and so on.

When we were upgrading to ES6 I was surprised/relieved to find that moving some leaf node code in the call graph to classes from prototypes did help. The common wisdom at the time was that classes were still relatively expensive. But they force strict, which we were using inconsistently, and they flatten the object representation (I discovered these issues in the heap dump, rather than the flame graph). Reducing memory pressure can overcome the cost of otherwise suboptimal code.

OpenTelemetry having not been invented yet, someone implemented server side HAR reports and the data collection on that was a substantial bottleneck. Particularly the original implementation.