Comments (7)
Well. We probably don't need to MKL.jl to be registered to use it for testing.
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But this package already works automatically (as long as you set the env variables with the mklvars
script).
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Yeah. I wasn't precise. I meant without a preinstalled MKL.
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Just a question on this issue.
An RA here is working on fleshing out coverage in MKLSparse. The MKL downloads for Mac on Intel's site are broken, so setting the environment variables is impossible. Instead, we decided to install MKL.jl
, and then nuke the piece of code that manually links to it (i.e., the __init__().
)
Is this correct? (Basically, if you added MKL.jl
as a dep, would that init function be irrelevant?)
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Update here: What should be done is to depend on MKL_jll
and use that (no need to use MKL.jl).
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Hi guys, a question.
I'm new to Julia, MKL, and MKLSparse. I'm having trouble getting MKLSparse to work because it could not load the library "libmkl_rt". I understand that you mentioned to depend the package on MKL or MKL_jll. But how that exactly works? More specifically, how do I depend MKLSparse to one of those?
Many thanks!
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@ywa136 , what you need to do is install the MKL library (from Intel) in your system, and load the env variables before launching Julia (source /opt/intel/mkl/bin/mklvars.sh intel64 ilp64
or somesuch). What these gentlemen are discussing here is not anything the user needs to do, it is a mechanism to allow, in the future, to download the library automatically so the user doesn't have to. Also, please direct your questions to e.g. discourse.julialang.org, so we can keep github focused on development issues.
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Related Issues (20)
- Error tagging new release
- Can't install MKLSparse on Windows HOT 9
- Julia 1.0 Support? HOT 4
- Migrate to new API
- 5-argument mul! has different signature than in stdlib
- Remove type piracy HOT 7
- Add two-stage algorithms HOT 25
- How to control number of threads? HOT 1
- Are sparse matrix-matrix multiplications implemented? HOT 2
- matrix-dense-vector multiplication faster for symmetric/hermitian matrices using adjoint HOT 1
- The speed ratio of matrix multiplication using MKLSparse stop to grow while number of of threads >= 8. HOT 2
- Different type matrices does not use multi-thread HOT 2
- Bug when using MKLSparse following use of MKL HOT 28
- Error on macOS HOT 1
- TagBot trigger issue HOT 20
- Random error with sparse COO matrices
- Update the token for coverage HOT 2
- Tests don't pass HOT 10
- error caused by isreadable(::String) HOT 1
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