gnurizen

A lot of times the performance is data dependent, like randomized data can result in poor warp utilization where as sorted data can be much more efficient because you have less warps sitting around idle. We have an upcoming blog post on decompression kernels that show this clearly, stay tuned!

Thanks! I think most performance debugging happens during development, what we're bringing to the table is exposure of system behavior in production which often diverges because of changes in the shape of workloads from dev, which are often simplistic and synthetic. So I'd say its late-stage performance debugging and production observability combined that makes this useful.

Stay tuned for a follow on post where we show how we used this to optimize an FSST decompression kernel for vortex (https://github.com/vortex-data/vortex).

Details of how we integrated USDT support in Parca modeled after libbpf.

CUPTI is kind of a choose your own adventure thing, as you subscribe to more stuff the overhead goes up, this is kind of minimalist profiler that just subscribes to the kernel launches and nothing else. Still to your point depending on kernel launch frequency/granularity it may be higher overhead than some would want in production, we have plans to address that with some probabilistic sampling instead of profiling everything but wanted to get this into folks hands and get some real world feedback first.

This profiler is focused on kernel execution but we do scrape high level metrics (https://www.polarsignals.com/blog/posts/2025/06/04/latest-in... which is based on https://github.com/polarsignals/gpu-metrics-agent). What performance counters in particular were you interested in?

Yeah the quickstart guide covers docker, k8s and "raw" binary options:

https://www.parca.dev/docs/quickstart/

Author here, would be happy to field any questions or feedback!

It should work to profile neovim, does it use the luajit shared library or is it statically linked? You could run parca-agent and set up a filter to discard all non-neovim samples and leave a parca instance running locally and just restart it periodically.

I wrote the code and the blog, happy to answer any questions/comments. Very eager to have folks try it out and give feedback! Like is my meme game strong or very strong? J/K

There's some missing bits around FFI and callbacks (i.e. C calling function pointer that is a luajit generated stub back into the interpreter) and curious if anyone actually uses these things in OpenResty workloads. Deploy and enjoy!

Thanks for the kind words Mike, will correct ASAP! Kinda surprised that’s the only thing I got wrong, thanks for taking the time to read it.

The answer you are looking for is no. No one can translate this into what humans should eat to avoid heart disease, that will require a ton more research. We still haven't even figured out whether eggs are good or bad for you.

I would bet a dollar inhibiting TOR via protein restriction turns out to be one of those things thats good for mice but bad for humans. Whatever aging gains you get are probably over-ridden by earlier onset of sarcopenia. Too many studies show how having muscle and strength is key to aging well, something mice in a cage don't have to worry about. Agree though that 99.9 is probably high.

Anybody else chuckling at the irony here that eBPF is inspired by dtrace which was invented by Cantrill?