# Solving Sparse Linear Systems fast

**URL:** <https://discourse.julialang.org/t/solving-sparse-linear-systems-fast/83071>\
**Category:** Performance\
**Tags:** sparse, linearsolve\
**Created:** [June 20, 2022, 2:11pm UTC](https://discourse.julialang.org/t/solving-sparse-linear-systems-fast/83071 "2022-06-20T14:11:41Z")\
**Posts on this page:** 1\
**Showing post:** 11

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**Author:** ![pushkar\_khandare](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/pushkar_khandare/32/37323_2.png) [@pushkar\_khandare](https://discourse.julialang.org/u/pushkar_khandare)\
**Post date:** [June 23, 2022, 9:04am UTC](https://discourse.julialang.org/t/solving-sparse-linear-systems-fast/83071/11 "2022-06-23T09:04:44Z")

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Hello Chris,

After running through the examples you gave, and following the excellent caching interface of LinearSolve.jl, I found out that KLU factorisation gives me the faster solve times than UMFPACK (though the factorisation times are quite high for KLU, but since its a one-time operation it does not matter in my case).  
I have a key question. When I change the BLAS.set\_num\_threads value I see no difference in solve times. How can I vary the number of threads that these algorithms use, and choose the optimal number of threads? I want to run my code on my institute cluster, and want to use all resources I can to solve Ax=b in the fastest time possible.

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