I think the regressions are a positive, in a sense, since to me it feels like much interesting work is being done, e.g. Also Julia is like Python + NumPy at least if you want to compare, but conversely Python has way more in its standard library for non-numerical (not missing for Julia necessarily, just found in packages), so it’s not easy to compare stats of languages. I barely follow other language such as Python, so I can’t say if Julia has unusually many regressions. I’m very involved in open source now, at least follow very well what’s happening with JuliaLang, and many packages, but Julia is the first and still only language I’m involved with at that level. I’m though not sure if these are many regression, or unusually many for Julia even. ![]() ![]() I wouldn’t worry too much about regressions, this is only a beta, the first one, not even rc1. In total there are 39 regressions since not all are marked on the milestone. It has 16 regressions (41% of the 39 open issues on the milestone, though only 8 also marked performance). It’s on the 1.11 milestone, so it will not be ignored. ┌ 5.0 ms SparseArrays.CHOLMOD._init_() 98.93% compilation timeġ23.1 ms SparseArrays 3.99% compilation time 112 already precompiled.Ħ1.731323 seconds (4.22 M allocations: 263.147 MiB, 0.35% gc time, 1.69% compilation time: 15% of which was recompilation)Ģ5.6 ms SuiteSparse_jll 85.64% compilation time ![]() | | |_| | | | (_| | | Version 1.11.0-alpha1 ()ġ dependency successfully precompiled in 60 seconds. THe compile time is quite variable but the differences are comparable to this.ġ dependency successfully precompiled in 48 seconds.
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