I understand what GP meant, but extraction of values from a sparse matrix is an essential operation of multiplying two sparse matrices. Sparse matmult in turn is an absolutely fundamental operation in everything from weather forecasting to logistics planning to electric grid control to training LLMs. Radix sort on the other hand is very nice but (as far as I know) not nearly used as widely. Matrix multiplication is just super fundamental to the modern world.

I would love to be enlightened about some real-world applications of radix sort I may have missed though, since it's a cool algorithm. Hence my question above.

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littlestymaar 4 days ago | parent [-]

> to training LLMs

LLMs are made from dense matrices, aren't they?

	▲	WJW 4 days ago \| parent \| next [-]
		Not always, or rather not exclusively. For example, some types of distillation benefit from sparse-ifying the dense-ish matrices the original was made of [1]. There's also a lot of benefit to be had from sparsity in finetuning [2]. LLMs were merely one of the examples though, don't focus too much on them. The point was that sparse matmul makes up the bulk of scientific computations and a huge amount of industrial computations too. It's probably second only to the FFT in importance, so it would be wild if radix sort managed to eclipse it somehow. [1] https://developer.nvidia.com/blog/mastering-llm-techniques-i... [2] https://arxiv.org/html/2405.15525v1
	▲	almostgotcaught 4 days ago \| parent \| prev [-]
		Almost all performant kernels employ structured sparsity