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> Once you unroll things like classes, list comprehensions, generators, e.t.c, you end up with basically the same rough structure of code minus memory allocatio
by duped 2y ago
> Once you unroll things like classes, list comprehensions, generators, e.t.c, you end up with basically the same rough structure of code minus memory allocation.
No, you don't, and that's why there are many engineer years invested into designing AoT and JIT compilers for JS and Python.
If you write C like Python you get Python but slower.
> The advantage of doing it through ML is that once you do the initial setup of the training set, and set up the pipeline to train the compiler, to integrate any pattern recognition into the compiler would be very trivial.
Except this has already been done, so what advantage does ML bring? Other than doing it again, but worse, and possibly incorrectly?
- ActorNightly 2y agoAoT/JIT work would literally be the training tool to train the model, with additional optimizations along the way. The issue is that manual removal of all the runtime stuff from the generated code is just cumbersome right now, but with ML, its just a matter of having enough examples. The advantage is that you get native optimized code like someone wrote it in C directly, and ability to automatically generate code to be offloaded to GPU as people start doing expanded training with higher level pattern recognition. The incorectness part I adressed already, stochastic output doesnt matter when your domain is finite.