The source code to his data parallel compiler hosted on the GPU is 17 lines of APL!
The co-dfns source I've seen[1] is much longer. Do you know if there's a write-up somewhere of the differences between that and Appendix B from the dissertation?
The co-dfns source I've seen[1] is much longer. Do you know if there's a write-up somewhere of the differences between that and Appendix B from the dissertation?
I like APL and J as a scratchpad where arrays are the basic unit and not scalars. J is functional and it turned me on to that world before I touched Haskell or F#.
Aaron Hsu has a lot of great videos that speak to a lot of the usability and scaling out you mention:
https://www.youtube.com/results?search_query=aaron+hsu
I particularly like this one: https://www.youtube.com/watch?v=z8MVKianh54&t=2857s
I am able to grasp concepts or own them after coding them in APL or J even if the code isn't as fast such as how well APL applies to Convolutional Neural Networks [1,2]. I really understood the mechanics of CNNs better after working through this paper a lot more than books I had read on ANNs in general since the late 80s/early 90s. By contrast, I have coded ANNs in C and Python, and I get lost in the PL, not the concept, if that makes sense. Anyway, I am a polyglot and find people criticize J/APL/k etc. from a brief look without really trying to learn the language. I learned assembler and basic back in 1978 to 1982, and I felt the same way when I first looked at opcodes.
Yes, the takeaway is that with APL or J is that you can see the mechanics in a paragraph of code, and it is not a very trivial example. If the libraries or verbs are created to deal with some of the speed or efficiency issues, it is promising as a way of understanding the concept better.
The dataframes of R and Python (Pandas) were always a thing in APL/J/k/q, so it is their lingua franca or basic unit of computation upon which the languages were built - arrays, not a library.
More importantly, almost along the lines of the emperor has no clothes, is a tack to get away from the black box, minimal domain knowledge, ML or DL that cannot be explained too easily - see newly proposed "Algorithmic Accountability Act" in US legislature. Differentiable Programming and AD (Automatic Differentiation)applied with domain knowledge to create a more easily explainable model, and try to avoid biases that may creep into a model and affect health care and criminal systems in a negative way [1][2].
And then there are those who use DL/ANNs for everything, even things that are easily applied and solved using standard optimization techniques. Forest from the trees kind of phenomenon. I have been guilty of getting swept up with them too. I started programming ANNs in the late 80s to teach myself about this new, cool-sounding thing called "neural networks" back then ;)