cargo-trace VS scalene

Are the authors here? Thanks for this! I'm always thrilled to see advances in profiling tools.

I'm curious what they have to say about complexity/necessity of interpreting all of DWARF. cargo-trace (an neat and conceptually similar but abandoned project, I think) [1] says:

> It can be empirically determined that almost all dwarf programs consist of a single instruction and use only three different instructions. rip+offset, rsp+offset or *cfa+offset, where cfa is the rsp value of the previous frame. The result of the unwinding is an array of instruction pointers.

Do you find this to be true? Is more complex interpreting of DWARF necessary?

And in the lkml thread linked from the article, Linus is extremely pessimistic about DWARF unwinding, [2] I'm sure not without justification. He's talking about kernel stacks, and I think the trade-off is different when you're trying to profile existing userspace applications and libraries compiled and implemented however, but nonetheless I'm curious to hear the authors say how applicable they think his points are.

[1] https://github.com/dvc94ch/cargo-trace

[2] https://lkml.org/lkml/2012/2/10/356

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