scalene VS parca-agent

I collected a list of profilers (also memory profilers, also specifically for Python) here: https://github.com/albertz/wiki/blob/master/profiling.md

Currently I actually need a Python memory profiler, because I want to figure out whether there is some memory leak in my application (PyTorch based training script), and where exactly (in this case, it's not a problem of GPU memory, but CPU memory).

I tried Scalene (https://github.com/plasma-umass/scalene), which seems to be powerful, but somehow the output it gives me is not useful at all? It doesn't really give me a flamegraph, or a list of the top lines with memory allocations, but instead it gives me a listing of all source code lines, and prints some (very sparse) information on each line. So I need to search through that listing now by hand to find the spots? Maybe I just don't know how to use it properly.

I tried Memray, but first ran into an issue (https://github.com/bloomberg/memray/issues/212), but after using some workaround, it worked now. I get a flamegraph out, but it doesn't really seem accurate? After a while, there don't seem to be any new memory allocations at all anymore, and I don't quite trust that this is correct.

There is also Austin (https://github.com/P403n1x87/austin), which I also wanted to try (have not yet).

Somehow this experience so far was very disappointing.

(Side node, I debugged some very strange memory allocation behavior of Python before, where all local variables were kept around after an exception, even though I made sure there is no reference anymore to the exception object, to the traceback, etc, and I even called frame.clear() for all frames to really clear it. It turns out, frame.f_locals will create another copy of all the local variables, and the exception object and all the locals in the other frame still stay alive until you access frame.f_locals again. At that point, it will sync the f_locals again with the real (fast) locals, and then it can finally free everything. It was quite annoying to find the source of this problem and to find workarounds for it. https://github.com/python/cpython/issues/113939)

Use this my fren: https://github.com/plasma-umass/scalene

You should take a look at Scalene - it's even better.

https://github.com/plasma-umass/scalene

I like seeing another Python profiler. The one I've been playing with is Scalene (GitHub). It does some fun things related to letting you see how much things are moving across the system Python memory boundary.

Ai vazut "Python Performance Matters" by Emery Berger (Strange Loop 2022)? E in principiu o prezentare si demo cu Scalene.

I tried out some features of machine learning models suggesting optimisations on code profiled by scalene and pretty much all of them would make the code less efficient, both time and memory wise. I am not worried. The devil is in the details and ML will not replace all of us anytime soon

If that's true, you should probably update the docs. Everything I could find implied dotnet, jvm, python were still unsupported. For example, the roadmap section of the readme mentions most of these but nothing mentions dotnet. However I did find your tickets and a demo being merged in which makes it seem maybe supported?

Ticket: https://github.com/parca-dev/parca-agent/issues/161

Demo: https://github.com/parca-dev/parca-demo/pull/18

Couldn't see any advantages to this over https://github.com/parca-dev/parca-agent. Which uses eBPF so it can be used with non-instrumented apps and code paths.

The pervasive lack of frame pointers is the reason why we've developed a custom format derived from DWARF unwind information thanks to some insights: DWARF unwind information is incredible flexible, it supports many arches and allows restoring any arbitrary register. But we only need 3: the frame pointer, the stack pointer, and in non-x86 the return address.

In addition, this encoding doesn't use that many bytes, but unfortunately reading and parsing that information is quite expensive.

For that reason I've developed a new unwinder that uses custom unwind information derived from DWARF (https://www.polarsignals.com/blog/posts/2022/11/29/profiling..., previously discussed in https://news.ycombinator.com/item?id=33788794) that runs in BPF. This new compact representation can be binary searched easily and each unwind row has a size of 16 bytes. I are currently working on reducing it down to ~10 bytes.

All the code is fully OSS (Apache 2.0 for userspace and GPL for BPF), and part of the Parca project (https://github.com/parca-dev/parca-agent).

We've also given some talks in FOSDEM going deeper into how we made it scale for many big processes.

I find this surprising! Was this for off the shelf applications or some custom binaries?

As mentioned above, we see DWARF expressions such as `DW_CFA_def_cfa_expression` on the regular. See the "Test Plan" section and commit messages of the PR that introduced support for this particular opcode [0]

[0]: https://github.com/parca-dev/parca-agent/pull/1058

Let's see an example perf map file for NodeJS. The runtimes out there output this file with more or less the same format, more or less!

The good news is we got you covered. If you are using Parca Agent, we already do the heavy lifting for you to symbolize captured stack traces. And we keep extending our support for the different languages and runtimes.