Julia 1.9 precompilation will be a turning point (opens in new tab)

Just to manage expectations: As far as I understand, v1.9 doesn't by itself solve TTFP/precompilation. The tweet seems slightly over-enthusiastic. But it puts tools to solve it in the hands of package developers.

In that sense, one can perhaps say it is a turning point.

Thankfully my GPU programming is shadet related, thus I don't have to deal with all the compute issues, however a good decision of CUDA from early on was to have PTX.

GPGPU needs more languages that make it easier to do compute with feeling like doing Assembly.

Basically the productivity that can be understood by reading stuff about StarLisp and the connection machine.

Julia seems a nice addition to this idea.

The Julia compilation model is fairly complex, due to the Julia user's wish for dynamic capabilities combined with common features in statically typed languages.

Although it may appear so, it was not low hanging fruit - it took a lot of effort building incrementally over several releases by a number of contributors.

To elaborate on the sibling comment: Julia has (1) multimethods / multiple dispatch capabilities, (2) you can dynamically define new types and methods at runtime, (3) everything is aggressively devirtualized/compiled to machine code when possible. That requires the capability of discarding and recompiling code that has already been compiled and cached when a new method is defined at runtime. Worse even, just importing a new library might invalidate some of the compiled cached code. For me, capabilities 1,2,and 3 make the cost of a complicated compilation model worth it. But it also means it takes a lot of time to improve the compiler (this type of better caching was not at all a low hanging fruit).