# How to parallelize the product of sparse and dense matrices

**URL:** https://discourse.julialang.org/t/how-to-parallelize-the-product-of-sparse-and-dense-matrices/65602
**Category:** Performance
**Tags:** parallel, sparse
**Created:** [July 31, 2021, 2:39pm UTC](https://discourse.julialang.org/t/how-to-parallelize-the-product-of-sparse-and-dense-matrices/65602 "2021-07-31T14:39:21Z")
**Posts on this page:** 1
**Page:** 1

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### Author: ![Gregstrq](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/gregstrq/32/20620_2.png) [@Gregstrq](https://discourse.julialang.org/u/Gregstrq)
#### Post date: [July 31, 2021, 2:39pm UTC](https://discourse.julialang.org/t/how-to-parallelize-the-product-of-sparse-and-dense-matrices/65602/1 "2021-07-31T14:39:21Z")

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This is a general question. Suppose we have a sparse CSR matrix `A` and a dense column majour matrix `B`, and we want to calculate the product.

In principle, one can parallelize either over the rows of matrix `A`, or over the columns of matrix `B`. Given the dimensions of the matrices and may be some additional information (sparsity of `A`, for example), **which way is more efficient?** Does anyone know, how it is done in MKL, for example?