As for photo and video processing, photoshop does have a lot of legacy cruft that doesn’t exist in more modern applications. And certainly video is unlikely to bound by integer CPU ops in any modern software.
It's not just Photoshop. Final Cut Pro also makes heavy use of CPU's. Indeed, it's precisely for this reason that Apple offers the Afterburner card, to offload some of that computational workload from the CPU's:
https://support.apple.com/en-us/HT210748
Likewise, CPU computation is also important for AVID (a standard within the industry):
http://www.avidblogs.com/how-avid-media-composer-uses-a-computer/
"Playing codecs smoothly in a timeline requires processing, which benefits from more CPU cores. Codecs with large raster sizes also benefit from more cores. Playing a timeline that contains Linked (AMA) clips plus many effects results in a higher stream count, especially with more complex codecs. With more cores there are more opportunities to distribute the processing of those streams effectively."
Synthetic benchmarks are actually quite useful in understanding how real world performance will be, so long as you understand what the synthetic benchmarks measure and how the real world works. That;s why i am curious about the “real” workloads you refer to.
We know the clock rate and the IPC for Apple’s chips, so we know quite a bit. Believe it or not, when we sit down to figure out the architecture for a new x86-64 chip, we rely on synthetic benchmarks to model performance as well. And real world performance seldom differs very much at all from our predictions.
That may be true but, again, you're making claims here without providing the data to support them. In my work I often encounter technical experts that made bold claims that don't hold up when I investigate them critically. It's not because they're unskilled, it's just that even experts get things wrong. For instance, you yourself earlier made a bold claim (the CPU wasn't important for photo and video processing) that, when I investigated it, turned out to be problematic. So it's possible what you believe about ARM performance might also not fully hold. That's why I keep asking for data!
Further, from my work with databases, I know that benchmarks can be notoriously misleading unless they model what you are actually doing. So, since you seem to have internal access to such things, might you be able to, say, tell me what the WolframMark score would be for Mathematica if it were running on a modern ARM processor?
Here's all I have on ARM performance for Mathematica, which only allows for a crude estimate of how it might run on an ARM chip as fast as the iPad Pro's:
Wolfram has spent over 5 years optimizing Mathematica for the Raspberry Pi. The Raspberry Pi 4, which uses a "Broadcom BCM2711 SoC with a 1.5 GHz 64-bit quad-core
ARM Cortex-A72 processor, with 1MB shared L2 cache" (
https://en.wikipedia.org/wiki/Raspberry_Pi#Processor), running the latest version of Mathematica, takes 77.8 seconds to complete the 15 tests in the WolframMark benchmark (
https://blog.wolfram.com/2019/07/11/mathematica-12-available-on-the-new-raspberry-pi-4/).
My 2014 MacBook Pro (also quad-core) takes 6.68 seconds (11.6 x faster). So if other synthetic benchmarks indicate that the per-core speed of the ARM chip in the iPad Pro is ~11–12 times greater than the per-core speed of the ARM chip in the Raspberry Pi, then one could perhaps guess that its per-core speed for Mathematica is comparable to that of my 2014 laptop. Of course, a direct benchmark would be much more preferable.