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Remove forgotten lines and add coverage badge
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.github/workflows/nightly.yml

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${{ runner.os }}-
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- uses: julia-actions/julia-buildpkg@latest
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- uses: julia-actions/julia-runtest@latest
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# testAptMKL:
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# name: Julia ${{ matrix.version }} - ${{ matrix.os }} - ${{ matrix.arch }}
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# runs-on: ${{ matrix.os }}
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# strategy:
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# matrix:
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# version:
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# - 'nightly'
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# os:
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# - ubuntu-latest
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# arch:
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# - x64
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# env:
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# LD_LIBRARY_PATH: /opt/intel/compilers_and_libraries_2019.4.243/linux/mkl/lib/intel64_lin/
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# steps:
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# - name: Install MKL via apt
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# run: |
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# curl https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB > GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
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# sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
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# rm GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
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# sudo sh -c 'echo deb https://apt.repos.intel.com/mkl all main > /etc/apt/sources.list.d/intel-mkl.list'
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# sudo apt-get update
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# sudo apt-get install -y intel-mkl-64bit-2019.4-070
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# source /opt/intel/mkl/bin/mklvars.sh intel64
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# - uses: actions/checkout@v2
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# - uses: julia-actions/setup-julia@latest
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# with:
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# version: ${{ matrix.version }}
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# arch: ${{ matrix.arch }}
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# - uses: julia-actions/julia-buildpkg@latest
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# - uses: julia-actions/julia-runtest@latest

README.md

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[pkgeval-url]: https://juliaci.github.io/NanosoldierReports/pkgeval_badges/report.html
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[![PkgEval][pkgeval-img]][pkgeval-url]
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![](https://github.com/JuliaMath/VML.jl/workflows/julia%201.6/badge.svg)
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![](https://github.com/JuliaMath/VML.jl/workflows/julia%201.6+1.8/badge.svg)
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![](https://github.com/JuliaMath/VML.jl/workflows/julia%20nightly/badge.svg)
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[![Coverage Status](https://coveralls.io/repos/github/JuliaMath/IntelVectorMath.jl/badge.svg?branch=master)](https://coveralls.io/github/JuliaMath/IntelVectorMath.jl?branch=master)
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This package provides bindings to the Intel MKL [Vector Mathematics Functions](https://www.intel.com/content/www/us/en/develop/documentation/onemkl-developer-reference-c/top/vector-mathematical-functions.html).
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This is often substantially faster than broadcasting Julia's built-in functions, especially when applying a transcendental function over a large array.

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