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I am not aware of any historical performance data stored somewhere for the OpenCV buildbots. And I doubt performance data on this CI instances would be meaningful. Dedicated hardware (but costly, time consuming for maintenance, ...) would be needed, that is not the job of the buildbot CI. And OpenCV human resources are already limited.

So all code changes to OpenCV rely on the individual developer always checking for performance regressions.

Yes it is. See the optimization label for some examples/discussions.

I would like also add that OpenCV relies on external libraries for "performance critical" functions like Intel IPP for lot of image processing functions, or cuDNN/OpenVINO/Tengine for deep learning.

I think there are few "micro optimizations" for contributed new code, or in OpenCV code base.

I mean, a large part of the pull requests for performance optimization are "obvious optimizations": e.g. add SIMD code path for a specific function, parallelize an algorithm, add OpenCL code path, ...

Another topic is conversion of old code from native intrinsics to universal intrinsics / wide-universal intrinsics (more information). For a library of this size, it is unmaintainable to have native intrinsics for all the supported architecture (x86, ARM, PowerPC, RISC-V, WebAssembly, ...).

In summary, and in my opinion:

• performance of OpenCV code are in general really good
• but for critical performance, OpenCV relies on external libraries, e.g. for the deep learning hot topic see OpenVINO, cuDNN, Tengine
• or the user should rely on external libraries (e.g. Intel MKL, OpenBLAS for BLAS operations)
• contributed new SIMD code must rely on universal intrinsics / wide-universal intrinsics (WUI) framework
• for conversion of native intrinsics to WUI, requirement is no perf regression (most of the time it is conversion from x86 (or ARM NEON) intrinsics to universal intrinsics where you can safely expect similar results since it is more or less a mapping of intrinsics, so no need to have rigorous regression benchmarks on different CPU models, ...)
• contributed new performance code should bring noticeable performance gain, most of the time it is obvious since you add SIMD code path, and you quantify the perf gain
• OpenCV remains a general computer vision library, I am sure you can easily outperform OpenCV performance if you are dead serious and if you are written code targeting a specific architecture, specific memory cache size, etc.
• for instance it is likely this Simd Library outperforms OpenCV for image processing functions it targets, but if you look at the source code it is a nightmare for maintenance in my opinion

My intention is to see the performance effect of a code fix and compare it historically across all the platforms the pipeline/builtbot makes.

Maybe you can ad more information about which function you are targeting, what is the optimization?

Is the expected performance gain significative?

Note:

• I am not affiliated with the OpenCV organisation
• neither my job involves HPC