Is there a specified/stable Typed Racket intermediate representation
that has all the type info resolved, and which separate projects could
build upon, for other target backends or analysis?
Reason for asking...
I was idly thinking of various ways to do GPU/TPU "supercomputing" from
a normal Racket program, or to specify GPU bits in a Racket-ish source
language.
One general way involves compiling a normal Racket/-ish program to both
Racket VM and (at least some of the procedures or closures, or parts of
same) to a language/IR like for OpenCL (or one of the other
existing/emerging ones). From there, even a simple implementation might
be able to do things like automatically run a chunk/extent of algorithm
on the GPU, when a static/dynamic heuristic suggests that the overhead
of going to GPU is worthwhile.
Having very little time for this weekend side project, and not wanting
to spend it reimplementing type inferencing or annotation... Is there a
specified/stable IR of Typed Racket that would be easy to work with for
this? (Or would it be easier to do something simple from scratch in
Racket (like a syntax transformation-heavy `#lang`), or to try to adapt
the Pre-Scheme C target for this purpose?)
(I'm aware we could write numerical bits in, say, a C-like OpenCL
language, and then do Racket FFI of the OpenCL API to run the GPU bits
from our program otherwise coded in Racket. I'm more interested in the
problem of ways of compiling a Racket-ish language to run on the GPU.)
(Motivation: I have a new GPU computer toy,
"https://www.neilvandyke.org/machine-learning/";, and, while some of my
old Racket packages are still great for scraping/importing data for
what's now called "data science", I then have to switch over to various
growing stacks of software tools in other languages. Top-down, I could
write wrappers to use some of those other tools from Racket. But I
wonder whether there are opportunities being missed, from a potential
Racket bottom-up, while most people are busy with the big ML/stats
toolkits and the algorithmic languages that are currently popular in
data science. Also, there is only so much Jupyter Notebook in a Web
browser that a person can take, before they want to also understand more
about the new metal. :)
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