Limitation in Union types with CUDA.jl?

[jling]

it’s the same thing as:

Strange way to recover performance with polymorphic types

This is a followup of dramatic performance change by adding additional unused method. The explanation was the existing limit on the number of overloaded methods the Julia optimizer is looking to decide the actual return type at compile type. I have found a way to avoid the allocation problem and recover the performance. The reproducer code is using BenchmarkTools using StaticArrays const Vector3 = SVector{3,Float64} abstract type AbstractShape end struct Box <: AbstractShape x::Float64;…

Dramatic performance change by adding additional unused method

https://github.com/JuliaLang/julia/blob/96902377bde0b3e84c1319e0d5ad55039610d5ef/base/compiler/types.jl#L69

right?