Comment by amelius | Hacker News Reader

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Comment by amelius | Hacker News Reader

> languages like Julia are attempting to unseat it

Isn't it CUDA that is taking this role?

CUDA is great for big dense matrices, which is incredibly important. But there is a lot more to scientific computing than big dense matrices.

Also, CUDA is low-level a library by one vendor for one class of hardware. Not a language. Languages serious about scientific computing already have multiple interfaces for CUDA at various level of abstraction.

There is a Fortran version of CUDA: https://developer.nvidia.com/cuda-fortran

There is cuSparse for sparse matrices:

https://docs.nvidia.com/cuda/cusparse/

One of the reasons why CUDA won over OpenCL is because it is polyglot.

What are some example workloads in scientific computing that are not suitable for CUDA?

solving small and medium sized differential equations, most monte-carlo algorithms

Anything that's not linear algebra

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