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I think they were trying to say “radix sort is a more important application of prefix sum than extraction of values from a sparse matrix/vector is.”
by otherjason 1y ago
I think they were trying to say “radix sort is a more important application of prefix sum than extraction of values from a sparse matrix/vector is.”
- WJW 1y agoI 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.
- littlestymaar 1y ago> to training LLMs LLMs are made from dense matrices, aren't they?
- WJW 1y agoNot 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-inference-optimization/#sparsity https://developer.nvidia.com/blog/mastering-llm-techniques-i... [2] https://arxiv.org/html/2405.15525v1 https://arxiv.org/html/2405.15525v1
- almostgotcaught 1y agoAlmost all performant kernels employ structured sparsity