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Using optimized BLAS/LAPACK libaries through stdlib for dense linear algebra is now very easy. Sparse matrices do not count with such a level of API standardization. stdlib counts now with a minimal support for sparse matrices but it would be useful to extended in several ways one of such would be to enable using MKL as an optimized backend.
A first step would be to enable switching between the internal spmv and mkl spmv kernels. This would require additional specifications at the level of the preprocessor macros because the sparse matrix API of MKL has a specific signature and the current STDLIB_EXTERNAL_BLAS/LAPACK macros will not be enough.
Prior Art
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The text was updated successfully, but these errors were encountered:
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Feb 8, 2025
Motivation
Using optimized BLAS/LAPACK libaries through stdlib for dense linear algebra is now very easy. Sparse matrices do not count with such a level of API standardization. stdlib counts now with a minimal support for sparse matrices but it would be useful to extended in several ways one of such would be to enable using MKL as an optimized backend.
A first step would be to enable switching between the internal spmv and mkl spmv kernels. This would require additional specifications at the level of the preprocessor macros because the sparse matrix API of MKL has a specific signature and the current
STDLIB_EXTERNAL_BLAS/LAPACK
macros will not be enough.Prior Art
No response
Additional Information
No response
The text was updated successfully, but these errors were encountered: