Julia versus MATLAB

General Usage Performance
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1

I am trying Julia 1.7.2 on an M1 MacBook Air for bench marking Eigen against MATLAB 2022a

a=randn(100,100)
tic, for x=1:100; eig(a); end; toc
Elapsed time is 0.295766 seconds.

whereas Julia v1.7.2

a=randn(Float64, (100,100))
 @time begin
       for x in 1:100
       eigen(a);
       end
       end
  1.691869 seconds (2.10 k allocations: 41.257 MiB, 1.18% gc time)

Both are in x86 mode, I believe. Is there any reason why Julia should be so slow?
Kumar

2

How many threads is BLAS using? These are both just calling out to BLAS, so with some parameter tuning, they should be identical.

3

BLAS is using 8 threads. I changed it to 16 with no change. In fact, I changed it to 1 thread and the executed time was the same.

4

In my computer MKL’s eigen is 4x as fast as the default, so maybe this is related (I think MATLAB uses MKL by default).

using BLAS:

julia> @time begin
              for x in 1:100
              eigen(a);
              end
              end
  1.968685 seconds (2.10 k allocations: 41.257 MiB, 5.29% gc time)

with MKL:

julia> @time begin
              for x in 1:100
              eigen(a);
              end
              end
  0.519582 seconds (2.10 k allocations: 60.788 MiB, 0.39% gc time)

(second execution in both cases, to discount compilation)

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5

I don’t expect the MacBook Air to benefit from MKL. The processor is ARM-based. But x86 has an emulation mode.
/K

6

See this comment from an earlier discussion:

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