# Performance of \`exp(A)\` for 9x9 anti-Hermitian matrix: Julia vs. PyTorch vs. MATLAB (CPU & GPU)

**URL:** <https://discourse.julialang.org/t/performance-of-exp-a-for-9x9-anti-hermitian-matrix-julia-vs-pytorch-vs-matlab-cpu-gpu/131696>\
**Category:** Performance\
**Tags:** question, performance\
**Created:** [August 19, 2025, 3:57am UTC](https://discourse.julialang.org/t/performance-of-exp-a-for-9x9-anti-hermitian-matrix-julia-vs-pytorch-vs-matlab-cpu-gpu/131696 "2025-08-19T03:57:33Z")\
**Posts on this page:** 1\
**Showing post:** 14

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**Author:** ![yolhan\_mannes](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/yolhan_mannes/32/220485_2.png) [@yolhan\_mannes](https://discourse.julialang.org/u/yolhan_mannes)\
**Post date:** [August 19, 2025, 6:48am UTC](https://discourse.julialang.org/t/performance-of-exp-a-for-9x9-anti-hermitian-matrix-julia-vs-pytorch-vs-matlab-cpu-gpu/131696/14 "2025-08-19T06:48:59Z")

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As for julia, I think we really miss a lot of batched solvers, we have everything to write them ie, CUBLAS and CUSOLVER however I think they are not right now, it’s just tricky to get right but if you know the alg enough don’t hesitate to try implementing it ( for your case (non-hermitian) it is the Padé + scaling & squaring batched alg)

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