Hi folks - I've put together some recent work to add support for Dtrace and
eBPF profiling of the core R interpreter - it's pretty experimental but it DOES
work. Side project, but hopefully becoming more useful in the long run.
There's a patch here - not terribly big, actually - showing the minimal changes
required to add the pragmas into the core to make it traceable.
patch-diff.githubusercontent.com/raw/wch/r-source/pull/25.patch
My thinking is that this sort of works in concert / cooperation with the
profiling work that Brian Ripley did to the core, 20-some years ago now. ;-)
If you're not terribly familiar with the two technologies involved here -
Dtrace is a technology that came from the Solaris world - essentially you're
measuring the impact and occurrence rate of syscalls as the eval() loop in the
center of the R core runs. It's also supported natively on macOS. eBPF,
similarly, is a technology on the Linux side of the world - a derivative of the
good old Berkeley Packet Filter, it allows for adding custom fragments to a
running kernel and measuring performance (and other...) as a system runs.
Included is a harness to allow you to use lima to test the Linux-y eBPF bits on
a relatively current macOS install, inside of a VM - it's as small as I could
reasonably make it,
The demo here - is to actually run the standard demo() loop, on top of an
instrumented R engine. The included harness script does that, and outputs PDFs
showing you the (minimal) impact of trace probes, as well as how much time
various parts of the system are spending on their tasks.
There is future work to be done, some of which is already in progress - for
example, I've been working on a patch to the Matrix package to allow some of
the same techniques to be applied there, sort of bubbling the same technology
and techniques to places where they can be maximally useful. (Matrix, as a part
of r-recommended, has a TON of other packages that are dependent upon it -
enough to make it a prime target for improvements of any sort.)
I hope this lands nicely for folks doing related work - I was quite surprised
by how straightforward the patch appears, given how much work it's setting up.
best, and thanks for all the previous contributions you've made to R and the
larger performance tooling ecosystem...
--e
[[alternative HTML version deleted]]
______________________________________________
[email protected] mailing list
https://stat.ethz.ch/mailman/listinfo/r-devel
