mpx | enhanced cross-end applet framework | Chat library

You're not setting the video output, and the QMediaPlayer in VideoWindow is completely useless (so you can remove that), while you should use the one created for QueueWin and set the video output with the QVideoWidget of the other window.

The "basic" solution would be the following:

There's too much for me to put this in a comment, so I will summarize here. Maybe it will help you, or someone else. I should also mention this is for RHEL EC2 instances behind a corporate proxy (not Arch Linux), but I still feel like it may help. I had to do the following to get puppeteer working. This is straight from my docs, but I had to hand-jam the contents because my docs are on an intranet.

I had to install all of these libraries manually. I also don't know what the Arch Linux equivalents are. Some are duplicates from your question, but I don't think they all are:
pango libXcomposite libXcursor libXdamage libXext libXi libXtst cups-libs libXScrnSaver libXrandr GConf2 alsa-lib atk gtk3 ipa-gothic-fonts xorg-x11-fonts-100dpi xorg-x11-fonts-75dpi xorg-x11-utils xorg-x11-fonts-cyrillic xorg-x11-fonts-Type1 xorg-x11-fonts-misc liberation-mono-fonts liberation-narrow-fonts liberation-narrow-fonts liberation-sans-fonts liberation-serif-fonts glib2

If Arch Linux uses SELinux, you may also have to run this:
setsebool -P unconfirmed_chrome_sandbox_transition 0

It is also worth adding dumpio: true to your options to debug. Should give you a more detailed output from puppeteer, instead of the generic error. As I mentioned in my comment. I have this option ignoreDefaultArgs: ['--disable-extensions']. I can't tell you why because I don't remember. I think it is related to this issue, but also could be related to my corporate proxy.

-march native means -march $MY_HARDWARE. We have no idea what hardware you have. For you, that would be -march=skylake-avx512 (SkyLake SP) The results could be reproduced by specifying your hardware architecture explicitly.

It's quite possible that the errors will decrease with more modern instructions, specifically Fused-Multiply-and-Add (FMA). This is the operation a*b+c, but rounded once instead of twice. That saves one rounding error.

A self semi join of your results should do the trick:

Please notice, that cv2.matchTemplate has a mask parameter, where you can set up a mask for the template, which would be the alpha channel of your template (small image). Further, keep in mind to feed image (large image) and template (small image) in that order to cv2.matchTemplate:

On "mainstream" CPUs like Skylake-X and IceLake, it's only worth using 512-bit vectors at all if you use them consistently for a lot of your program's run-time, not just for an occasional memcpy. See SIMD instructions lowering CPU frequency for the details: you don't want occasional calls to memcpy to hold your CPU frequency down to a lower max turbo.

Using AVX-512 features with 256-bit vectors (AVX-512VL) can be worth it for some things, e.g. if masking is nice, or if you use YMM16..31 to avoid VZEROUPPER.

I'd guess that glibc would only resolve memcpy to __memcpy_avx512_no_vzeroupper on systems like Knight's Landing (KNL) Xeon Phi, where the CPU is designed around AVX-512, and there's no downside to using 512-bit ZMM vectors. There's no need for vzeroupper even after using ymm0..15 on KNL. In fact vzeroupper is very slow on KNL, and definitely something to avoid, hence putting no_vzeroupper in the function name.

https://code.woboq.org/userspace/glibc/sysdeps/x86_64/multiarch/memmove-avx512-no-vzeroupper.S.html is the source for that version. It uses ZMM vectors, including ZMM0..15, so if used on a Skylake/IceLake CPU it should use vzeroupper. This version looks designed for KNL.

There would be some tiny benefit to having an AVX-512VL version that used ymm16..31 to avoid vzeroupper (to speed 32 .. 64 byte copies), without ever using ZMM registers.

And it would make sense for __memcpy_avx512_no_vzeroupper to only use ZMM16..31 so avoiding vzeroupper isn't a problem on mainstream CPUs; then it would be a usable option in code that already made heavy use of AVX-512 (and thus was already paying the CPU-frequency cost.)

There are plenty of reasons why a code can be slower with -march=native, although this is quite exceptional.

That being said, in your specific context, one possible scenario is the use of slower SIMD instructions, or more precisely different SIMD instructions finally making the program slower. Indeed, GCC vectorize loops with -O3 using the SSE instruction set on x86 processors such as yours (for backward compatibility). With -march=native, GCC will likely vectorize loops using the more advanced and more recent AVX instruction set (supported by your processor but not on many old x86 processors). While the use of AVX instruction should speed your program up, it is not always the case in few pathological cases (less efficient code generated by compiler heuristics, loops are too small to leverage AVX instructions, missing/slower AVX instructions available in SSE, alignment, transition penality, energy/frequency impact, etc.).

My guess is your program is memory bound and thus AVX instructions do not make your program faster.

You can confirm this hypothesis by enabling AVX manually using -mavx -mavx2 rather than -march=native and look if your performance issue is still there. I advise you to carefully benchmark your application using a tool like perf.

Every time you access a shared memory component like multiprocessing's Array, you necessarily engage some inter-process communication. This is slower than normal memory access, of course, and to make matters worse, it tends to lock the array, meaning your workers are mostly locking on each other, and not doing real work.

If instead of sending one pixel at a time, if you batch up the results, you can speed things up. For instance, if you change to a model of listing what pixels need to be updated, and have your main worker do the actual work of updating the target array:

You can check out below workarounds without using NuGet credential plugin.

1, Use dotnet cli to add credentials to the nuget source in your docker file. And then pass the $PAT in the build arguments (ie. --build-arg PAT=$PAT ):