MKL.jl

Using Julia with Intel's MKL

MKL.jl is a Julia package that allows users to use the Intel MKL library for Julia's underlying BLAS and LAPACK, instead of OpenBLAS, which Julia ships with by default. Julia includes libblastrampoline, which enables picking a BLAS and LAPACK library at runtime. A JuliaCon 2021 talk provides details on this mechanism.

This package requires Julia 1.8+, and only covers the forwarding of BLAS and LAPACK routines in Julia to MKL. Other packages like IntelVectorMath.jl, Pardiso.jl, etc. wrap more of MKL's functionality. The oneAPI.jl package provides support for Intel OneAPI and Intel GPUs.

Usage

If you want to use MKL.jl in your project, make sure it is the first package you load before any other package. It is essential that MKL be loaded before other packages so that it can find the Intel OMP library and avoid issues resulting out of GNU OMP being loaded first.

To Install:

Adding the package will replace the system BLAS and LAPACK with MKL provided ones at runtime. Note that the MKL package has to be loaded in every new Julia process. Upon quitting and restarting, Julia will start with the default OpenBLAS.

julia> using Pkg; Pkg.add("MKL")

Hint: On Windows the installation might fail due to a missing library with the name VCRUNTIME140.dll. To install it, download and execute https://aka.ms/vs/17/release/vc_redist.x64.exe .

To Check Installation:

julia> using LinearAlgebra

julia> BLAS.get_config()
LinearAlgebra.BLAS.LBTConfig
Libraries: 
└ [ILP64] libopenblas64_.0.3.13.dylib

julia> using MKL

julia> BLAS.get_config()
LinearAlgebra.BLAS.LBTConfig
Libraries: 
└ [ILP64] libmkl_rt.1.dylib

Using the 64-bit vs 32-bit version of MKL

We use ILP64 by default on 64-bit systems, and LP64 on 32-bit systems.

Threading control

To set or get the global number of threads used by MKL, use MKL.{set/get}_num_threads. This does not affect specific domains where the number of threads is set with mkl_domain_set_num_threads. Calling LinearAlgebra.BLAS.{set/get}_num_threads is domain-specific and only refers to the BLAS domain (unlike other backends, where the number of threads used by LAPACK is also modified).

NOTE: Using MKL with Distributed

If you are using Distributed for parallelism on a single node, set MKL to single threaded mode to avoid over subcribing the CPUs.

MKL.set_num_threads(1)

NOTE: MKL on Intel Macs

MKL for Intel Macs is discontinued as of MKL 2024. Thus, in order to use MKL on Intel Macs, you will need to install the right version of MKL_jll and possibly also pin it.

julia> Pkg.add(name="MKL_jll", version="2023");
   Resolving package versions...
    Updating `~/.julia/environments/v1.10/Project.toml`
⌃ [856f044c] + MKL_jll v2023.2.0+0
  No Changes to `~/.julia/environments/v1.10/Manifest.toml`

julia> Pkg.pin(name="MKL_jll", version="2023");
   Resolving package versions...
    Updating `~/.julia/environments/v1.10/Project.toml`
⌃ [856f044c] ~ MKL_jll v2023.2.0+0 ⇒ v2023.2.0+0 ⚲
    Updating `~/.julia/environments/v1.10/Manifest.toml`
⌃ [856f044c] ~ MKL_jll v2023.2.0+0 ⇒ v2023.2.0+0 ⚲
        Info Packages marked with ⌃ have new versions available and may be upgradable.

MKL seems to have some issues on Intel Macs when multi-threading is enabled. Threading can be disabled in such cases with:

MKL.set_threading_layer(THREADING_SEQUENTIAL)