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I'll have a look but I'm not sure there is anything I can do about this. The only suggestion I can give you is to use cuhre instead of vegas as the former is more accurate and requires fewer evaluations (thus fewer allocations):
julia>using Cuba, BenchmarkTools
julia>@btimecuhre((x, f) -> f[1] = x[1])
25.231 μs (784 allocations:36.89 KiB)
Component:1:0.5±4.291960664289122e-15 (prob.: 0.0)
Integrand evaluations:195
Fail:0
Number of subregions:2
julia>@btimevegas((x, f) -> f[1] = x[1])
21.588 ms (928004 allocations:42.48 MiB)
Component:1:0.4999999506146127±4.969630652405548e-5 (prob.: 2.559630372933608e-5)
Integrand evaluations:232000
Fail:0
Number of subregions:0
Cuba produces a lot of allocations, 4 per iteration in the example below. Is there a way to avoid this?
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