In Roadmap for a faster time-to-first-plot? , it was suggested by @ChrisRackauckas to circumvent the usage of Dicts since they slow down compile time performance. I would love to get suggestions how to actually do this. In my application I am using TOML files for configuration and when reading TOML …

[image] tobias.knopp: Yes, but that’s the architecture of TOML.jl. It returns nested Dicts. Yes, but the types are not nested. There is nothing inherently slow with Dicts. But code like JuliaLang/Pkg.jl/blob/cf7fc6fe35d6bfb848597614dbbafd8289320efc/src/GraphType.jl#L125-L126 …

That is true. The type is Dict{AbstractString, Any} and it then contains more Dicts of the same type. Not sure, why it is AbstractString and not String.

for reference, I get julia> @time a = TOML.parsefile("myfile.toml") 1.242109 seconds (2.40 M allocations: 120.457 MiB, 11.85% gc time) Dict{AbstractString,Any} with 6 entries: julia> @time a = TOML.parsefile("myfile.toml") 0.001002 seconds (2.15 k allocations: 97.641 KiB) Dict{AbstractString…

I wonder what would happen if we just added a bunch of Dict{Any,Any} or Dict{Symbol,Any} stuff to the sysimage and told people to prefer that (Any,Any should already have some coverage, but I wonder what would happen if we purposely made it pretty complete)

Lets switch to a concrete example. Here are the numbers I get from Winston: julia> @time using Winston 2.744007 seconds (2.26 M allocations: 119.856 MiB, 2.29% gc time) julia> @time plot(1:100) 2.838665 seconds (7.31 M allocations: 376.682 MiB, 4.47% gc time) The last time is not really compl…

Try compiling just the dict portions to the sysimage and see what happens.

If memory serves, the top three inference times for Revise’s startup are all setindex! functions for different Dict types, which is why I chose that for the example in https://github.com/JuliaLang/julia/pull/31466 . And I agree with @ChrisRackauckas that certain programming patterns are unnecessaril…

I want to believe Tim, but since compile time is a non-trivial problem, I have some doubts that there will be a solution soon. This is really not meant as a critique and I want that you prove me wrong. My hope would be that 1.4 (which I expect in summer 2020) contains improvements.

Yes, hoping for improvements in 1.4 (and further improvements in later releases) seems reasonable. But at least for your own personal use, I’d guess if you compile Julia yourself from master you might see some major benefits earlier than summer 2020.

Check out [2018 LLVM Developers’ Meeting: L. Hames & B. Loggins “Updating ORC JIT for Concurrency”] It seems that the road is already paved with the new ORC jit. You get concurrent compilation, object cache jit-dylib, the ability to delete compiled functions. The only problem is the g…

(Compiler) Performance of Dict

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(Compiler) Performance of Dict

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