Basically I am looking for methodology guidelines for doing my own testing on a bunch of techniques in different papers and seeing what the performance impact is overall. Are there guidelines for doing such things?
On Tue, Oct 16, 2018 at 3:19 AM <[email protected]> wrote: > On Tue, 16 Oct 2018 01:23:45 +0800, Carter Cheng said: > > I am actually looking at some changes that litter the kernel with short > > code snippets and thus according to papers i have read can result in CPU > > hits of around 48% when applied is userspace. > > You're going to need to be more specific. Note that 48% increase in a > micro-benchmark > doesn't necessarily translate to a measurable performance change - for > example, I have a > kernel build running right now with a cold file cache, and it's only using > 6-8% of the CPU in > kernel mode (the rest being gcc in userspace and waiting for the > spinning-oxide disk). If the > entire kernel slowed down by 50% that would only be 3-4% change visible at > the macro level. > > > but I haven't seen any kernel space papers measuring degradations in > overall > > system performance when adding safety checks(perhaps redundant > sometimes) into > > the kernel > > Well.. here's the thing. Papers are usually written by academics and trade > journal pundits, not people who write code for a living. As a result, > they end > up comparing released code versions. As a worked example, see how the > whole > Spectre thing turned out - the *initial* fears were that we'd see a huge > performance drop. But the patches that finally shipped for the Linux > kernel > were after a bunch of clever people had thought about it and come up with > less > intrusive ways to close the security issue. > > (Having said that, the guys at Phoronix do a reasonable job of doing > macro-level benchmarks of each kernel release and pointing out if there's > a big > hit in a subsystem). > > And as I said earlier - sometimes it doesn't matter, because correctness > trumps performance. >
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