2ghz does not measure it’s computing power though, only the cycle speed. Two very different things.
An objective measure is a simple benchmark:
Here’s a quad core 1.5ghz RISC-V SoC (noted as VisionFive 2) vs a quad core 1.8ghz ARM chip (noted as Raspberry Pi 400).
It’s not even remotely close to usable for all but the most basic of tasks https://www.phoronix.com/review/visionfive2-riscv-benchmarks/6
If I’m reading this right, yes. Vulkan calls extension, extension translates the call and addresses the GPU directly in PTX.
Makes sense to me, CUDA itself isn’t extremely optimal. It was supposed to be the easy button to get fast parallel code executing on Nvidia GPUs but has mostly failed at both of those objectives.
For optimal you really have to get into the PTX language, which is perhaps a half step above Assembly.
But this really doesn’t help AMD, Intel etc’s position. They still have to do their own extensions and optimization. But would be nice for programmers to be able to consolidate on Vulkan for both ML/Gaming workloads.
It doesn’t. The extensions they’re using are Nvidia specific.
All respect to JetBrains, I’ve loved several of their IDEs… This was a dumb idea from the start. Way too niche and specialized. Honestly the allure of a purpose built language specific IDE is losing it’s lusture as well with modern architectures often blending several languages, configuration frameworks, IaC…