AMD always launches impressive hardware specs. But they are way behind in software, which is more important than hardware
> Our goal is to simplify and accelerate ML development by creating more interoperability between various ML frameworks (such as TensorFlow, JAX and PyTorch) and ML compilers (such as XLA and IREE).
From there, their goal would most likely be to work with XLA/OpenXLA teams on XLA[3] and IREE[2] to make RoCM a better backend.
[1] https://github.com/openxla/stablehlo
Not that anyone cares, and everyone keeps using CUDA while simultaneously complaining about Nvidia GPU prices, as if those two things have nothing to do with each other...
(There are some annoying differences in the low-level implementations of OpenCL vs. Vulkan Compute, due to their being based on SPIR-V compute "kernels" vs. "shaders" respectively, that make it hard for them to interop cleanly. So that's why the choice can be significant.)
I did a bit of work in OpenCL almost 10 years ago, and found it decently portable on a range of NVIDIA GPUs as well as Intel iGPUs. On the high end I used something like the Titan X while on the low end it was typical GPUs found in business class laptops.
But my limited exposure to AMD was terrible by comparison. Even though I am away from that work now, I still tend to try to run "clpeak" and one of my simpler image processing scripts on each new system. And while I liked a Ryzen laptop for general use or even games, it seemed like OpenCL was useless there. It seemed my best option was to ignore the GPU and use Intel's x86_64 SIMD OpenCL runtime.
Also my fractal software incl OpenCL multi-GPU / mixed plaftorm rendering: https://chaoticafractals.com/
Both work on [ Nvidia, AMD, Intel, Apple ] x [ CPU, GPU ].
Some of the shared code here: https://github.com/glaretechnologies/glare-core
Don't let anyone tell you OpenCL is dead! Keep writing OpenCL software!!
Only C, C++ and Fortran were never taken seriously enough, other language stacks never considered.
Thus everyone that enjoyed programming in anything not C, with great libraries and graphical debuggers flocked to CUDA, now remains to be seen if SYCL and SPIRV will ever matter enough to regain some of those folks back.
There's relatively few people capable of implementing these frameworks without a solid cuda-like foundation, and those that do exist would need a very strong incentive to do it.