[pizlonator]

Ok but my data disagrees with you. Specifically: when apps get complex enough, the differences between CPUs and schedulers wash out in the chaos.

I’ve tested this over the course of a decade on multiple Linuxes, multiple Darwins, multiple x86 flavors (Intel, amd, and lots of core counts and topologies), POWER, and various arms, and on many large benchmarks in two very different languages (Java and C/C++). In Java I tested in in two very different VMs (JikesRVM and FijiVM). I think the key is that a typical benchmark for me is >million lines of code with very heterogenous and chaotic locking behavior stemming from the fact that there are hundreds (at least) of different hot critical sections of varying lengths and subtle relationships between them. So you get a law of large numbers or maybe wisdom of the masses kind of “averaging” of differences between CPUs and schedulers.

I’d love to see some contradictory data on similarly big stuff. But if you’re just saying that some benchmark that had a very homogenous lock behavior (like ~one hot critical section in the code that always runs for a predictable amount of time and never blocks on weird OS stuff) experiences wild differences between CPUs and schedulers then sure. But that just means there are no ideal answers for that scenario, not that there aren’t ideal answers for anyone.