avoid allocating value vectors in get_level #582
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When profiling runs with the default `autodiff=true`, this line was responsible for 35% of the time and almost all allocations: https://github.com/Deltares/Ribasim/blob/b3eb044a722d1655c5465bafe50951b75fe960d6/core/src/solve.jl#L1002 `connectivity.flow` is a sparse matrix, but the DiffCache does not seem to like sparse matrixes. The `dual_du` field was a dense vector of length n x n x cache_size, and the `get_tmp` call led to further allocations trying to restructure the sparse matrix from the vector. Luckily there is the FixedSizeDiffCache that helps here: https://docs.sciml.ai/PreallocationTools/stable/#FixedSizeDiffCache This retains the sparsity in the dual, and returns a `ReinterpretArray` from `get_tmp` during autodiff. To avoid materializing this reinterpretarray I needed to additionally fill the parent array with zeros rather than the array itself. There is another unrelated performance fix here, and that is to concretely type the Parameter struct, by adding type parameters from its fields. Otherwise you have situations like ``` struct A a::Vector end ``` where the compiler doesn't know the element type of the Vector, so it can perform less optimizations. The solution: ``` struct A{T} a::Vector{T} end ``` Finally I consistently added AbstractVector/Matrix argument type annotations to ensure the ReinterpretArray could enter everywhere. And I renamed the functions to formulate flows to `formulate_flow`, to make it easier to separate them from the other `formulate!` methods.
`Dictionary` uses `Indices{I}` as keys, and `Vector{T}` as values. The Parameters contain both, and therefore it was free to construct a `Dictionary` in a frequently called function like `get_level`. However with autodiff, the values could be a ReinterpretArray with Duals instead of just a Vector. This meant that on Dictionary creation it would convert the ReinterpretArray to a Vector, leading to many allocations.
This is on top of #581. After that, this was responsible for 94% of the time spent. With this PR that goes down to about 2%, leading to a nice little speedup.
Perhaps we should make them Indices at some point, if the O(log n) time spent doing searchsorted is much more than the hash table lookup.
Hofer-Julian
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Sep 8, 2023
visr
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Sep 11, 2023
`Dictionary` uses `Indices{I}` as keys, and `Vector{T}` as values. The
Parameters contain both, and therefore it was free to construct a
`Dictionary` in a frequently called function like `get_level`. However
with autodiff, the values could be a ReinterpretArray with Duals instead
of just a Vector. This meant that on Dictionary creation it would
convert the ReinterpretArray to a Vector, leading to many allocations.
This is on top of #581. After
that, this was responsible for 94% of the time spent. With this PR that
goes down to about 2%, leading to a nice little speedup.
---------
Co-authored-by: Hofer-Julian <[email protected]>
visr
added a commit
that referenced
this pull request
Sep 14, 2023
`Dictionary` uses `Indices{I}` as keys, and `Vector{T}` as values. The
Parameters contain both, and therefore it was free to construct a
`Dictionary` in a frequently called function like `get_level`. However
with autodiff, the values could be a ReinterpretArray with Duals instead
of just a Vector. This meant that on Dictionary creation it would
convert the ReinterpretArray to a Vector, leading to many allocations.
This is on top of #581. After
that, this was responsible for 94% of the time spent. With this PR that
goes down to about 2%, leading to a nice little speedup.
---------
Co-authored-by: Hofer-Julian <[email protected]>
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DictionaryusesIndices{I}as keys, andVector{T}as values. The Parameters contain both, and therefore it was free to construct aDictionaryin a frequently called function likeget_level. However with autodiff, the values could be a ReinterpretArray with Duals instead of just a Vector. This meant that on Dictionary creation it would convert the ReinterpretArray to a Vector, leading to many allocations.This is on top of #581. After that, this was responsible for 94% of the time spent. With this PR that goes down to about 2%, leading to a nice little speedup.