Comment by jpsamaroo | Hacker News Reader

Comment by jpsamaroo | Hacker News Reader

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OpenCL and various other solutions basically require that one writes kernels in C/C++. This is an unfortunate limitation, and can make it hard for less experienced users (researchers especially) to write correct and performant GPU code, since neither language lends itself to writing many mathematical and scientific models in a clean, maintainable manner (in my opinion).

What oneAPI (the runtime), and also AMD's ROCm (specifically the ROCR runtime), do that is new is that they enable packages like oneAPI.jl [1] and AMDGPU.jl [2] to exist (both Julia packages), without having to go through OpenCL or C++ transpilation (which we've tried out before, and it's quite painful). This is a great thing, because now users of an entirely different language can still utilize their GPUs effectively and with near-optimal performance (optimal w.r.t what the device can reasonably attain).

[1] https://github.com/JuliaGPU/oneAPI.jl [2] https://github.com/JuliaGPU/AMDGPU.jl

Which is something that CUDA provided since the very beginning. (with PTX)

No one will take over CUDA's dominance until they realize that one reason why most researchers flocked into it were its polyglot capabilities, and graphical debuggers.

in other words, Nvidia's product execution was spot-on.

Helped by Khronos focus of never supporting anything other than C and letting the community come up with tools.

So for years, before they started having a beating OpenCL was all about a C99 dialect with printf debugging.

SPIR and support for C++ came later as they were already taking a beating, trying to get up.

Apparently that is also the reason why Apple gave up on OpenCL, disagreements on how it should be going, after they gave 1.0 to Khronos.

Just compare Metal, a OOP API for GPUs, with Objective-C/Swift bindings, using a C++ dialect as shading language, a framework for data management, with Vulkan/OpenGL/OpenCL.

well, yeah. All of the opencl implementations were (and probably still are) awful. Other than requiring a lot of boilerplate, I found it adequate, essentially a clone of the the CUDA C driver API.

opencl 2 went into the weeds going full c++, but that all got rolled back with version 3.

Yes, and that's why Julia gained CUDA support first. My point was to respond to "Why would someone use this instead of plain old OpenCL(or CUDA) with C++?", and my answer was, "you can use something other than OpenCL C or C++". I'm not trying to say that CUDA is any lesser of a platform because of this; instead, other vendor's GPUs are now becoming easier to use and program.

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