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Comment by kjhrklewiou | Hacker News Reader

In a way this already happened, with GPU programming, which has a more realistic model of what modern hardware is (explicit memory hierarchy for example).

Because the speed advantage is so huge, people went through the pain of learning this new model and redesigning algorithms to better fit it.

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Modern hardware is multiple different things. GPU programming does not have a realistic model of what CPU hardware is like, because despite CPUs having more cores and SIMD, they are also very cache-oriented and branch-prediction-oriented, while GPUs aren't that at all.

And yet the most common language used for GPU programming is a C++ dialect.

And it took 10 years for NVidia to make it match the ISO C++ memory model introduced in C++11, as per NVidia talk at CppCon 2019.

And the C++11 memory model was added so that the language would map to the execution model of the underlying hardware, not viceversa, refuting the parent.

Not really, C++11 initally got its memory model from .NET and Java memory models, two mostly hardware agnostic stacks, and then expanded on top of them.

I'd argue that modern GPUs pipelines are quite different from the API programming models they support, especially tile based renderers do lots of funny things while maintaining the illusion that you are running on the abstract OpenGL pipeline.

So even with GPUs you have a similar situation..

I was talking about stuff like CUDA, not OpenGL.

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