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> what do people use to run benchmarks on CI?
Typically, you purchase/rent a server that does nothing but sequentially run queued benchmarks (and the size/performance of this server doesn't really matter, as long as the performance is consistent), then sends the report somewhere for hosting and processing. Of course, this could be triggered by something running in CI, and the CI job could wait for the results, if benchmarking is an important part of your workflow.
But CI and benchmarks really shouldn't be run on the same host.
> What does the rust project use?
It's not clear exactly where the Rust benchmark "perf-runner" is hosted, but here are the specifications of the machine at least: https://github.com/rust-lang/rustc-perf/blob/414230abc695bd7...
> What do other projects use?
Essentially what I described above, a dedicated machine that runs benchmarks. The Rust project seems to do it via GitHub comments (as I understand https://github.com/rust-lang/rustc-perf/tree/master/collecto...), others have API servers that respond to HTTP requests done from CI/chat, others have remote GUIs that triggers the runs. I don't think there is a single solution that everyone/most are using.
Iai^1 uses CacheGrind^2 to count instructions
[1] - https://github.com/bheisler/iai
I've looked into this before and there are very few tools for this. The only vaguely generic one I've found is Codespeed: https://github.com/tobami/codespeed
However it's not very good. Seems like most people just write their own custom performance monitoring tooling.
As for how you actually run it, you can get fairly low noise runtimes by running on a dedicated machine on Linux. You have to do some tricks like pinning your program to dedicated CPU cores and making sure nothing else can run on them. You can get under 1% variance that way, but in general I found you can't really get low enough variance on wall time to be useful in most cases, so instruction count is a better metric.
I think you could do better than instruction count though but it would be a research project - take all the low noise performance metrics you can measure (instruction count, branch misses etc), measure a load of wall times for different programs and different systems (core count, RAM size etc.). Feed it into some kind of ML system and that should give you a decent model to get a low noise wall time estimate.
Good tips here:
https://llvm.org/docs/Benchmarking.html
https://easyperf.net/blog/2019/08/02/Perf-measurement-enviro...