# QR decomposition on large (\>1TB) matrix

**URL:** <https://discourse.julialang.org/t/qr-decomposition-on-large-1tb-matrix/50552>\
**Category:** Julia at Scale\
**Tags:** question\
**Created:** [November 21, 2020, 7:06pm UTC](https://discourse.julialang.org/t/qr-decomposition-on-large-1tb-matrix/50552 "2020-11-21T19:06:42Z")\
**Posts on this page:** 1\
**Showing post:** 6

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**Author:** ![abuttari](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/abuttari/32/4165_2.png) [@abuttari](https://discourse.julialang.org/u/abuttari)\
**Post date:** [December 17, 2023, 9:17pm UTC](https://discourse.julialang.org/t/qr-decomposition-on-large-1tb-matrix/50552/6 "2023-12-17T21:17:58Z")

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Hello,  
qr\_mumps is actually developed for solving sparse problems through a multifrontal factorization but also contains some dense linear algebra routines including a parallel QR factorization. The issue, though, is that in order to handle a 1TB matrix you need distributed memory parallelism, which qr\_mumps does not support at the moment. One option is to use ScaLAPACK; there is a related discussion [here](https://discourse.julialang.org/t/how-to-load-scalapack-library-from-intel-mkl-library-inter-dependencies/81239).

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