MKLSparse.jl
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JuliaSparse
Make available to Julia the sparse functionality in MKL
This PR * simplifies a bit the wrapper generation logic * some unnecessary "type piracy" was removed (`A*B` calls `mul!(C,A,B)` already) * streamlines testing, so that all 4 numeric types...
Probably naive question, but I installed this package and got a nice speed boost for sparse matrix * dense vector multiplication, but I still only observe 8 of 64 cores...
Motivated in part by https://discourse.julialang.org/t/wraping-a-mkl-handle-in-julia/48241 I looked in more detail at the two-stage algorithms for recent MKL versions, They are of interest to me, specifically some of the multiplication and...
As Julia is for now exclusively using CSC storage format, the matrix-dense-vector multiplication is faster for symmetric/hermitian matrix `A` if one computes `A' * v` instead of `A * v`....
The matrix multiplication here should likely be implemented with an `MKLSparse.matmul` or something instead of type piracying the Base matrix multiplication with more specific versions.
See https://github.com/JuliaSparse/MKLSparse.jl/issues/15#issuecomment-426310273
I cannot use `MKLSparse` on macOS, I receive the error below: ``` ERROR: LoadError: Failed to precompile MKLSparse [0c723cd3-b8cd-5d40-b370-ba682dde9aae] to "/Users/eschnett/.julia/compiled/v1.11/MKLSparse/jl_WvNVir". Stacktrace: [1] error(s::String) @ Base ./error.jl:35 [2] compilecache(pkg::Base.PkgId, path::String,...
This is a subset of #30 excluding the `Matrix * SparseMatrix`: * adds support for MKL 2023.x * simplifies the MKLSparse wrappers by introducing `mkl_call(funcname, T, args...)` generated function that...
When I `use MKLSparse` after a previous `use MKL`, a bug manifests when multiplying a real-valued `SparseMatrixCSC{ComplexF64, Int64}` matrix times a `ComplexF64` or `Float64` vector. The following output exhibits the...
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Make available to Julia the sparse functionality in MKL