array VS async-profiler

I wrote a library in C++ (I know, probably a non-starter for most reading this) that I think does most of what you want, as well as some other requests in this thread (generalized to more than just multiply-add): https://github.com/dsharlet/array?tab=readme-ov-file#einstei....

A matrix multiply written with this looks like this:

    enum { i = 2, j = 0, k = 1 };

I should have mentioned somewhere, I disabled threading for OpenBLAS, so it is comparing one thread to one thread. Parallelism would be easy to add, but I tend to want the thread parallelism outside code like this anyways.

As for the inner loop not being well optimized... the disassembly looks like the same basic thing as OpenBLAS. There's disassembly in the comments of that file to show what code it generates, I'd love to know what you think is lacking! The only difference between the one I linked and this is prefetching and outer loop ordering: https://github.com/dsharlet/array/blob/master/examples/linea...

If you are looking for something like this in C++, here's my attempt at implementing it: https://github.com/dsharlet/array#einstein-reductions

It doesn't do any automatic optimization of the loops like some of the projects linked in this thread, but, it provides all the tools needed for humans to express the code in a way that a good compiler can turn it into really good code.

Using the Async Profiler we were able to build flamegraphs for the first and second queries to picture the differences in path length of the two transactions execution.

> The other two languages I’ve used mostly in recent decades are Java and Ruby and the profiler situation is for both those languages is kind of shitty. I had to pay real money to get the Java profiler I used at AWS and while it worked, it was klunky, not fun to use.

These days, async profiler (https://github.com/jvm-profiling-tools/async-profiler) is much better than the Go tooling for performance. It is a joy to use and features a top-like view for the hottest methods. It works for locks, allocations and CPU time. It also integrates with JMH.

Also, running it under a profiler (I recommend async-profiler[1]) should give you a good idea of where the slowdown occurs which might help you pin it down further.

[1] https://github.com/jvm-profiling-tools/async-profiler

Profile it. async-profiler is really great. Alternatively you can check out VisualVM/JProfiler/YourKit

async-profiler is really great at analyzing allocations, give it a shot!

JFR to Flame Graph Converter

Async Profiler converter tool does support JFR to Flame Graph, JFR to FlameScope, collapsed stacks to Flame Graph -https://github.com/jvm-profiling-tools/async-profiler#download