> Julia is great for explorative programming, while Python et al are better suited for slightly more structured, more productionized programming
In Julia, explorative programming is productionized programming, you don't have to switch to a different or restricted set of tools to make your program fast, it's the same code that you can profile on the fly. With types, you can patch your hotspots by adding a specific method to the generic function, and it will get invoked automatically instead of the more general case algorithm.
In those languages, I can actually take the theoretical practical hardware limits (memory BW, throughput, latency), and relatively easily achieve 99% of their utilization.
When people say “very fast” and “my cores are at 100%” they often mean “my program achieves 1% of the perf the hardware can deliver”.
Is Julia JIT deterministic / reproducible? It doesn't matter if you are writing the code to run yourself on your own machine, which is usually the case in research. But would you recommend Julia for a project has soft realtime requirements on the order of 10 ms and has to run in hundreds of machines scattered across the world, and you have to support personally?
Probably not in default configuration, but we have some people interested in this kind of thing. The compiler candy definitely be configured to make that work, but so far interest hadn't been sufficient to implement that. If you have a particular use case in mind, do let us know to see if we can help you out.
In Julia, explorative programming is productionized programming, you don't have to switch to a different or restricted set of tools to make your program fast, it's the same code that you can profile on the fly. With types, you can patch your hotspots by adding a specific method to the generic function, and it will get invoked automatically instead of the more general case algorithm.