State of Julia: the future looks modular, generic, and fast
July came and went, and with it JuliaCon, the annual get together of Julia users. During the three-day event, co-creators Stefan Karpinski and Jeff Benzanos once again took to the virtual stage to inform developers about the state of the programming language. Helped by Viral B. Shah, another father to the project, and Julia Computing co-founder Keno Fischer, the duo reflected upon current goings-on and gave a brief glimpse at what users can expect for the upcoming 12 months.
One of the notable developments during the last year is the number of registered Julia packages hitting 1.0 — turning almost a quarter of the ecosystem stable. Part of this seems to be down to a lot of the data analysis capabilities maturing and closing up to the mathematical packages, Karpinski found. He also noted a significant increase of speed in fundamental packages such as CSV.jl and DataFrames.jl, which maybe also put a bit of pressure on the core Julia team to keep up.
The compiler team at least, on whose behalf Jeff Benzanos presented, was quite successful in that regard. It can list faster method insertion leading to a speedup of ‘using’ statements, quicker subtyping and intersection, and fewer recompilations under recent successes. Inference improvements such as interprocedural condition propagation and constant propagation with invoke were also implemented, besides putting lots of work into CI stability to make sure everything stays current.
