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Comment by cburdick13 | Hacker News Reader

That's right, we've tested down to pascal, but this should work on Kepler too since CUDA and the underlying libraries support it.

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Will you publish benchmarks for e.g. K80? Or provide a way for users to contribute? It's really handy to know, e.g. comparable to what is Resnet50 inference on a bunch of architectures.

Hi, what specifically are you looking to benchmark on the K80? Users are free to contribute and we've had many external PRs.

Contribution guide is here: https://github.com/NVIDIA/MatX/blob/main/CONTRIBUTING.md

well the root README.md has some "benchmarks" at the very top. maybe there's no existing benchmarks doc with more details? or even a repo of what's in the README? it's like if numpy is 5 sec on CPU, but a K80 is only 100ms, then the K80 cost might be worth not going for the A100 at 3ms. Similar argument for jetson.

We have benchmarks in the benchmarks directory, but these are for things like convolution, matrix multiples, etc. It's not for running a traditional benchmark set like resnet.

Like most benchmarks it really depends on what you want to do, and since it's a general library everyone might care about different things.

I saw those, yes having the code is great but I’m more interested in the actual numbers. E.g. what does this sample do on A100, P100, T4, K80, Jetson nano? The analog is things like Resnet50 get tested and reported and then you know if Resnet50 might work for your budget / hardware.

Versus the advert in the root Readme, which is impressive but gives no data on the pareto.

Hi, if you don't mind opening an issue asking for this we can run these and put in the readme.

I have a spare Titan Z that could be useful if so.

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