cpplinks VS hyperfine

linking and loading: https://github.com/MattPD/cpplinks/blob/master/executables.linking_loading.md - in addition to the aforementioned books and blog posts, there also some pretty good talks: https://github.com/MattPD/cpplinks/blob/master/executables.linking_loading.md#talks

For more compilers correctness* resources see https://github.com/MattPD/cpplinks/blob/master/compilers.correctness.md

As for linkers and loaders, see https://github.com/MattPD/cpplinks/blob/master/executables.linking_loading.md

More: https://github.com/MattPD/cpplinks/blob/master/atomics.lockfree.memory_model.md

Take the red pill and see how deep the rabbit hole goes... https://github.com/MattPD/cpplinks/ https://github.com/shafik/cpp_learning

x86-64 tutorials here should be a good starting point: https://github.com/MattPD/cpplinks/blob/master/assembly.x86.md#tutorials

See also static analysis resources (more C++-oriented, although some of the readings are general): https://github.com/MattPD/cpplinks/blob/master/analysis.static.md#readings-books and https://gist.github.com/MattPD/71b63a3e1600c2b52e1db80fa2834e60#correctness-in-practice (formal methods and program analysis in industry).

Further readings: Book recommendations in https://github.com/MattPD/cpplinks/blob/master/compilers.md#books as well as program analysis resources (in particular lattice theory, type systems and programming languages theory, related notation): https://gist.github.com/MattPD/00573ee14bf85ccac6bed3c0678ddbef#program-analysis-resources

Microarchitectural performance analysis research is a pretty active and interesting area--see also: https://github.com/MattPD/cpplinks/blob/master/performance.tools.md#microarchitecture

For more on GDB see: https://github.com/MattPD/cpplinks/blob/master/debugging.md#gdb

Check out the official hyperfine Github repo

And then I used hyperfine to run the benchmarks on my MacBook Pro 14 M2 Max, and here are the results:

Search "benchmarking tools for linux" and decide that hyperfine is good for what I'm doing. Run Jennifer's new python script against my refactored perl and find that the python is 1.26 times faster for k=3 and 1.47 times faster for k=4. For the Covid-19 sequence, these are both on the order of hundreds of milliseconds.

> It is very possible to write sub 100ms procedures in TS, […]

I will not disagree with this statement because I don’t have a way to test inshellisense right now. Could you (or anyone with a working Node + NPM installation) please install inshellisense and post the actual numbers? Perhaps using a tool like hyperfine (https://github.com/sharkdp/hyperfine).

Yeah, while it's not as thorough as these tools, the method is at least reproducible and sane, and with ~10 or so samples, you get an interval with a nice confidence.

Another through method will be hyperfine[0], yet I wanted to provide a method which requires no installation and can be done in a whim, without jumps and hoops, with the tools already at hand.

[0]: https://github.com/sharkdp/hyperfine

That is native and inbuild but I would suggest below options instead 1. Using lazy's Profile tab instead https://github.com/folke/lazy.nvim 2. Using a dedicated plugin to do this https://github.com/dstein64/vim-startuptime. 3. Using an external program hyperfine is one that I use https://github.com/sharkdp/hyperfine

Fair enough, although might I recommend using hyperfine for your testing? ;p