Update benchmark results #633
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This PR is mostly a FYI regarding JuliaArrays/StaticArrays.jl#913 Depending on whether or not that issue is closed, you may want to switch to explicitly calling `generic_norm2` on static vectors to circumvent the accuracy issue. The issue arises when combining static arrays with ForwardDiff, in our case it occured in `exp(::SkewSymmetric)`. I did some benchmarking, and `generic_norm2` is faster than `norm` for all standard arrays up to at least length 9. For static arrays, it appears to do okay as well, while avoiding the accuracy issue. ```julia julia> a 3-element SVector{3, Int64} with indices SOneTo(3): 1 2 3 julia> @Btime norm($(Ref(a))[]) # standard norm of static array 5.135 ns (0 allocations: 0 bytes) 3.74166 julia> @Btime norm($(Ref(Vector(a)))[]) # standard norm of array 8.545 ns (0 allocations: 0 bytes) 3.74166 julia> @Btime LinearAlgebra.generic_norm2($(Ref((a)))[]) # generic_norm2 of static array 4.490 ns (0 allocations: 0 bytes) 3.74166 julia> @Btime LinearAlgebra.generic_norm2($(Ref(Vector(a)))[]) # generic_norm2 of array 7.671 ns (0 allocations: 0 bytes) 3.74166 ```
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Closing in favour of #646. |
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Benchmarks of RBD.jl 2.3.2 (b9ef1d6) using Julia 1.8.5 on an Apple MacBook Air (M1, 2020).
I've removed
BLAS.set_num_threads(1)fromperf/runbenchmarks.jlas it no longer seems relevant for Julia versions >=1.8. See my comments and plots in #500 for more details.