# Julia Cuda Matrix multiplication
**URL:** https://discourse.julialang.org/t/julia-cuda-matrix-multiplication/21741
**Category:** General Usage
**Tags:** cudanative, cuda
**Created:** [March 11, 2019, 1:57pm UTC](https://discourse.julialang.org/t/julia-cuda-matrix-multiplication/21741 "2019-03-11T13:57:52Z")
**Posts on this page:** 4
**Page:** 1
### Author:  [@Noobie76](https://discourse.julialang.org/u/Noobie76)
#### Post date: [March 11, 2019, 1:57pm UTC](https://discourse.julialang.org/t/julia-cuda-matrix-multiplication/21741/1 "2019-03-11T13:57:52Z")
Hi,
I’m relatively new to Julia and want to implement a numerical method using the CUDA libraries for Julia. I worked myself through the introduction files on GitHub and gained all the basic knowledge to write my own code so far.
The thing is that I want to create at least a not completely inefficient code. Therefore my aim is to avoid unnecessary overhead communication between the GPU and CPU, or their memories. And here is my, maybe stupid, question.
Imagine I have some data
A = rand(ComplexF64, (N,N)) ,
B = rand(ComplexF64, (N,N)) ,
where N is some fixed integer, and upload my data to the Nvidia GPU using
A\_gpu = CuArrays.cu(A),
B\_gpu = CuArrays.cu(B).
And the thing I asking myself is, when I’m performing a simple a simple matrix multiplication
A\_gpu\*B\_gpu
does this calculation take place at the GPU? I mean is this a standard implemented feature of Julia when one is multiplying CuArrays, or need I to write an extra kernel function for a “parallel matrix multiplication” and call it with @cuda…?
I would be great if some expert on Julia can answer this question for me.
Thanks
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### Author:  [@kristoffer.carlsson](https://discourse.julialang.org/u/kristoffer.carlsson)
#### Post date: [March 11, 2019, 2:05pm UTC](https://discourse.julialang.org/t/julia-cuda-matrix-multiplication/21741/2 "2019-03-11T14:05:57Z")
> [@Noobie76](#):
>
> I mean is this a standard implemented feature of Julia when one is multiplying CuArrays,
It will indeed take place on the GPU. This is not really a “standard implemented feature of Julia” it is just that `*` can be overloaded and the guys writing CuArrays overloaded `*` between two `CuArrays` (CuMatrices specifically) to call the CUBLAS version of matrix multiply.
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### Author:  [@maleadt](https://discourse.julialang.org/u/maleadt)
#### Post date: [March 11, 2019, 3:19pm UTC](https://discourse.julialang.org/t/julia-cuda-matrix-multiplication/21741/3 "2019-03-11T15:19:24Z")
> [@kristoffer.carlsson](#):
>
> This is not really a “standard implemented feature of Julia” it is just that `*` can be overloaded and the guys writing CuArrays overloaded `*` between two `CuArrays` (CuMatrices specifically) to call the CUBLAS version of matrix multiply.
Specifically, this is where the CUBLAS-implementations are dispatched to: [https://github.com/JuliaGPU/CuArrays.jl/blob/cee6253edeca2029d8d0522a46e2cdbb638e0a50/src/blas/highlevel.jl#L90-L145](https://github.com/JuliaGPU/CuArrays.jl/blob/cee6253edeca2029d8d0522a46e2cdbb638e0a50/src/blas/highlevel.jl#L90-L145)
And this is the fallback generically-typed implementation (e.g. for use with Dual numbers or other types that are not supported by CUBLAS): [https://github.com/JuliaGPU/CuArrays.jl/blob/cee6253edeca2029d8d0522a46e2cdbb638e0a50/src/matmul.jl#L4-L50](https://github.com/JuliaGPU/CuArrays.jl/blob/cee6253edeca2029d8d0522a46e2cdbb638e0a50/src/matmul.jl#L4-L50)
---
### Author:  [@JosePereiraUA](https://discourse.julialang.org/u/JosePereiraUA)
#### Post date: [February 24, 2021, 6:52pm UTC](https://discourse.julialang.org/t/julia-cuda-matrix-multiplication/21741/4 "2021-02-24T18:52:38Z")
This helps. Can I ask if this is the absolute most efficient way of multiplying two matrices, or is there any “trick” one might employ to speed up calculation even more?