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@KristofferC
Last active May 16, 2017 15:20
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A ratio greater than 1.0 denotes a possible regression (marked with ❌), while a ratio less than 1.0 denotes a possible improvement (marked with ✅). Only significant results - results that indicate possible regressions or improvements - are shown below (thus, an empty table means that all benchmark results remained invariant between builds).

ID time ratio memory ratio
["gradient", "∇S(SymmetricTensor{2, 3, Float32})"] 2.83 (5%) ❌ Inf (1%) ❌
["gradient", "∇S(SymmetricTensor{2, 3, Float64})"] 3.22 (5%) ❌ Inf (1%) ❌
["gradient", "∇S(Tensor{2, 3, Float32})"] 2.83 (5%) ❌ Inf (1%) ❌
["gradient", "∇S(Tensor{2, 3, Float64})"] 3.23 (5%) ❌ Inf (1%) ❌
["gradient", "∇Ψ(SymmetricTensor{2, 3, Float32})"] 5.81 (5%) ❌ Inf (1%) ❌
["gradient", "∇Ψ(SymmetricTensor{2, 3, Float64})"] 7.90 (5%) ❌ Inf (1%) ❌
["gradient", "∇Ψ(Tensor{2, 3, Float32})"] 5.81 (5%) ❌ Inf (1%) ❌
["gradient", "∇Ψ(Tensor{2, 3, Float64})"] 7.79 (5%) ❌ Inf (1%) ❌
["hessian", "ΔΨ(SymmetricTensor{2, 3, Float32})"] 3.59 (5%) ❌ Inf (1%) ❌
["hessian", "ΔΨ(SymmetricTensor{2, 3, Float64})"] 2.90 (5%) ❌ Inf (1%) ❌
["hessian", "ΔΨ(Tensor{2, 3, Float32})"] 3.82 (5%) ❌ Inf (1%) ❌
["hessian", "ΔΨ(Tensor{2, 3, Float64})"] 2.78 (5%) ❌ Inf (1%) ❌
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