Deep learning library written in Futhark(github.com)
github.com
Deep learning library written in Futhark
https://github.com/HnimNart/deeplearning
9 comments
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Great to see a futhark lib in the wild, the code seems really concise and readable. I feel the main reason for Futhark's ergonomics is FP combined with its array prelude (e.g. fold/transpose/flatten/rotate/indices).
I've seen some impressive results from Futhark in other areas. Would be very interested to see how it performs when applied to DL. Couldn't find any benchmarks here?
Not much documentation but the example program looks nice https://github.com/HnimNart/deeplearning/blob/master/program...
Wonderful to see this getting attention! I met with Minh since we were both using Futhark for our thesis. It's blazing fast and quite fun to work with.
If anyone's interested, my thesis contains some more examples on deep learning with Futhark: https://github.com/Jegp/thesis/
If anyone's interested, my thesis contains some more examples on deep learning with Futhark: https://github.com/Jegp/thesis/
Every time the Futhark language comes up, I think it should look like this:
ᛈᚱᛖᚾᛏᚨ("ᚺᛖᛚᛟ ᚹᛟᚱᛚᛞ!")
ᛈᚱᛖᚾᛏᚨ("ᚺᛖᛚᛟ ᚹᛟᚱᛚᛞ!")
PRENTF?
Prenta; I don't know ancient Norse (or any free translators) so I went with Icelandic.
In fairness the graphemes ᚨ and ᚠ are both pretty similar to F.
Unfortunately I didn't do that properly with the string too, I should've done something more like "ᚺᛅᛚᛟ ᚺᛖᛁᛗᚢᚱ". My bad.
In fairness the graphemes ᚨ and ᚠ are both pretty similar to F.
Unfortunately I didn't do that properly with the string too, I should've done something more like "ᚺᛅᛚᛟ ᚺᛖᛁᛗᚢᚱ". My bad.
Clouding aftar the greet greet vooel sheft
There's also the thesis linked from https://futhark-lang.org/publications.html - https://futhark-lang.org/student-projects/svend-msc-thesis.p... - and this paper: https://futhark-lang.org/publications/fhpnc19.pdf
Great to see Futhark improving, we need more options on GPGPU ecosystem.
For anyone looking to get started with it, writing a Monte Carlo simulation is a good first exercise to get familiar. Monte Carlo simulations also happen to perfectly showcase its power to exploit parallelism for massive speedups.