在 Docker (for Windows) 中编译极致性能的 Quantum ESPRESSO

1. 前言

本文讲述如何通过设置编译参数和依赖库,在 Docker 环境中编译极致性能的 Quantum ESPRESSO 6.3。

使用的相关软件:

所用软件除 ELPA 外均为最新版(QE 6.3 暂不支持更高版本的 ELPA)。

注:本文内容理论上也适用于 Docker for Linux/Mac。

2. 安装步骤

  1. 选择基础镜像,安装 PS XE。

    FROM ubuntu:17.04
    
    MAINTAINER itianda <me#itianda.com>
    
    ARG PS=parallel_studio_xe_2018_update3_cluster_edition
    
    RUN \
        tar -xzf psxe/$PS.tgz && \
        cd $PS && \
        mkdir /opt/intel && \
        cp ../psxe/psxe.lic /opt/intel/licenses && \
        ./install.sh --silent=../psxe/silent.cfg
    
    ARG TOPROOT=/opt/intel
    ARG INTELROOT=$TOPROOT/compilers_and_libraries/linux
    ENV MKLROOT=$INTELROOT/mkl
    ENV TBBROOT=$INTELROOT/tbb
    ENV LD_LIBRARY_PATH=/usr/local/lib:/usr/lib:/lib/x86_64-linux-gnu/:/lib
    ENV LD_LIBRARY_PATH=$INTELROOT/lib/intel64:$MKLROOT/lib/intel64:$TBBROOT/lib/intel64:$LD_LIBRARY_PATH
    ENV PATH=$TOPROOT/bin:$PATH
    

    注:Ubuntu 17.04 是 PS XE 2018 支持的最高版本。

    silent.cfg 的内容:

    ACCEPT_EULA=accept
    CONTINUE_WITH_OPTIONAL_ERROR=yes
    PSET_INSTALL_DIR=/opt/intel
    CONTINUE_WITH_INSTALLDIR_OVERWRITE=yes
    PSET_MODE=install
    ACTIVATION_TYPE=exist_lic
    AMPLIFIER_SAMPLING_DRIVER_INSTALL_TYPE=kit
    AMPLIFIER_DRIVER_ACCESS_GROUP=vtune
    AMPLIFIER_DRIVER_PERMISSIONS=666
    AMPLIFIER_LOAD_DRIVER=no
    AMPLIFIER_C_COMPILER=none
    AMPLIFIER_KERNEL_SRC_DIR=none
    AMPLIFIER_MAKE_COMMAND=none
    AMPLIFIER_INSTALL_BOOT_SCRIPT=no
    AMPLIFIER_DRIVER_PER_USER_MODE=no
    INTEL_SW_IMPROVEMENT_PROGRAM_CONSENT=no
    ARCH_SELECTED=ALL
    COMPONENTS=;intel-comp__x86_64;intel-comp-32bit__x86_64;intel-comp-doc__noarch;intel-comp-l-all-common__noarch;intel-comp-l-all-vars__noarch;intel-comp-nomcu-vars__noarch;intel-comp-ps-32bit__x86_64;intel-comp-ps__x86_64;intel-comp-ps-ss__x86_64;intel-comp-ps-ss-bec__x86_64;intel-comp-ps-ss-bec-32bit__x86_64;intel-openmp__x86_64;intel-openmp-32bit__x86_64;intel-openmp-common__noarch;intel-openmp-common-icc__noarch;intel-openmp-common-ifort__noarch;intel-openmp-ifort__x86_64;intel-openmp-ifort-32bit__x86_64;intel-tbb-libs-32bit__x86_64;intel-tbb-libs__x86_64;intel-idesupport-icc-common-ps__noarch;intel-icc__x86_64;intel-icc-32bit__x86_64;intel-c-comp-common__noarch;intel-icc-common__noarch;intel-icc-common-ps__noarch;intel-icc-common-ps-ss-bec__noarch;intel-icc-doc__noarch;intel-icc-doc-ps__noarch;intel-icc-ps__x86_64;intel-icc-ps-ss__x86_64;intel-icc-ps-ss-bec__x86_64;intel-icc-ps-ss-bec-32bit__x86_64;intel-ifort__x86_64;intel-ifort-32bit__x86_64;intel-ifort-common__noarch;intel-ifort-doc__noarch;intel-mkl-common__noarch;intel-mkl-core-32bit__x86_64;intel-mkl-core__x86_64;intel-mkl-core-rt-32bit__x86_64;intel-mkl-core-rt__x86_64;intel-mkl-doc__noarch;intel-mkl-doc-ps__noarch;intel-mkl-gnu-32bit__x86_64;intel-mkl-gnu__x86_64;intel-mkl-gnu-rt-32bit__x86_64;intel-mkl-gnu-rt__x86_64;intel-mkl-cluster__x86_64;intel-mkl-cluster-common__noarch;intel-mkl-cluster-rt__x86_64;intel-mkl-common-ps__noarch;intel-mkl-core-ps-32bit__x86_64;intel-mkl-core-ps__x86_64;intel-mkl-pgi__x86_64;intel-mkl-pgi-rt__x86_64;intel-mkl-common-c__noarch;intel-mkl-core-c-32bit__x86_64;intel-mkl-core-c__x86_64;intel-mkl-common-c-ps__noarch;intel-mkl-cluster-c__noarch;intel-mkl-tbb-32bit__x86_64;intel-mkl-tbb__x86_64;intel-mkl-tbb-rt-32bit__x86_64;intel-mkl-tbb-rt__x86_64;intel-mkl-pgi-c__x86_64;intel-mkl-gnu-c-32bit__x86_64;intel-mkl-gnu-c__x86_64;intel-mkl-common-f__noarch;intel-mkl-core-f-32bit__x86_64;intel-mkl-core-f__x86_64;intel-mkl-cluster-f__noarch;intel-mkl-pgi-f__x86_64;intel-mkl-gnu-f-rt-32bit__x86_64;intel-mkl-gnu-f-rt__x86_64;intel-mkl-gnu-f__x86_64;intel-mkl-gnu-f-32bit__x86_64;intel-mkl-f95-common__noarch;intel-mkl-f95-32bit__x86_64;intel-mkl-f__x86_64;intel-tbb-devel-32bit__x86_64;intel-tbb-devel__x86_64;intel-tbb-common__noarch;intel-tbb-doc__noarch;intel-ism__noarch;intel-icsxe__noarch;intel-psxe-common__noarch;intel-psxe-doc__noarch;intel-psxe-common-doc__noarch;intel-icsxe-doc__noarch;intel-psxe-licensing__noarch;intel-psxe-licensing-doc__noarch;intel-icsxe-pset
    
  1. 安装相关依赖包。

    RUN \
        apt-get update -y  && \
        apt-get upgrade -y && \
        apt-get install -y cpio wget make gcc g++ python ssh autotools-dev autoconf automake texinfo libtool patch flex
    
  2. 设置环境变量:

    ENV COMPILERVARS_ARCHITECTURE="intel64"
    ENV COMPILERVARS_PLATFORM="linux"
    
  3. 指定编译器选项:

    ARG TARGET="SKYLAKE"
    ARG CCFLAGS="-O3 -no-prec-div -fp-model fast=2 -x${TARGET}"
    ARG FCFLAGS="-O3 -no-prec-div -fp-model fast=2 -x${TARGET} -align array64byte -threads -heap-arrays 4096"
    
  4. 编译 Open MPI:

    RUN \
        cd $OMPI_DIR && \
        . compilervars.sh && \
        ./autogen.pl && \
        ./configure \
            --with-cma="no" \
            CC="icc" \
            CXX="icpc" \
            FC="ifort" \
            CFLAGS="${CCFLAGS}" \
            CXXFLAGS="${CCFLAGS}" \
            FCFLAGS="${FCFLAGS}" \
        && \
        make -j && \
        make install
    
  5. 编译 ELPA:

    RUN \
        cd $ELPA_DIR && \
        . compilervars.sh && \
        autoconf && \
        ./configure \
            --enable-option-checking=fatal \
            --prefix=$ELPAROOT \
            AR="xiar" \
            FC="mpifort" \
            CC="mpicc" \
            CXX="mpicpc" \
            CFLAGS="${CCFLAGS}" \
            CXXFLAGS="${CCFLAGS}" \
            FCFLAGS="${FCFLAGS}" \
            ACLOCAL="aclocal" \
            AUTOCONF='autoconf' \
            AUTOHEADER='autoheader' \
            AUTOMAKE='automake' \
            MAKEINFO="makeinfo" \
            SCALAPACK_LDFLAGS="-L${MKLROOT}/lib/intel64 -lmkl_scalapack_lp64 -lmkl_intel_lp64 -lmkl_sequential -lmkl_core -lmkl_blacs_openmpi_lp64 -Wl,-rpath,${MKLROOT}/lib/intel64" \
            SCALAPACK_FCFLAGS="-L${MKLROOT}/lib/intel64 -lmkl_scalapack_lp64 -lmkl_intel_lp64 -lmkl_sequential -lmkl_core -lmkl_blacs_openmpi_lp64 -I${MKLROOT}/include/intel64/lp64" \
        && \
        make -j && \
        make install
    
  6. 编译 QE:

    RUN \
        ln -s q-e-$QE_DIR $QE_DIR && \
        cd $QE_DIR && \
        . compilervars.sh && \
        ./configure \
            AR="xiar" \
            MPIF90="mpifort" \
            CC="mpicc" \
            CFLAGS="${CCFLAGS}" \
            FFLAGS="${FCFLAGS} -I${MKLROOT}/include -I${MKLROOT}/include/fftw" \
            LDFLAGS="-Wl,--start-group \
                ${MKLROOT}/lib/intel64/libmkl_intel_lp64.a \
                ${MKLROOT}/lib/intel64/libmkl_core.a \
                ${MKLROOT}/lib/intel64/libmkl_sequential.a \
                ${MKLROOT}/lib/intel64/libmkl_blacs_openmpi_lp64.a \
                ${MKLROOT}/lib/intel64/libmkl_scalapack_lp64.a \
                -Wl,--end-group" \
            --with-elpa-include="${ELPAROOT}/include/${ELPA_DIR}/modules" \
            --with-elpa-lib="${ELPAROOT}/lib/libelpa.a" \
            --with-elpa-version=2016 && \
        make all
    

3. 性能对比

为了对比性能,默认编译版本采用 GCC 编译,同样使用 Open MPI 实现并行计算,但不使用 ELPA 和 Intel MKL。

分别使用默认编译版本和优化编译版本进行简单自洽计算,计算时间分别为2m 5.57s55.82s,优化编译版本足足快了一倍!

默认编译版本输出结果:

     highest occupied level (ev):    21.1832

!    total energy              =   -1259.25185017 Ry
     Harris-Foulkes estimate   =   -1259.25185020 Ry
     estimated scf accuracy    <       0.00000003 Ry

     The total energy is the sum of the following terms:

     one-electron contribution =    -125.55179190 Ry
     hartree contribution      =     164.79277944 Ry
     xc contribution           =    -169.12506962 Ry
     ewald contribution        =   -1129.36776808 Ry

     convergence has been achieved in  12 iterations

     Writing output data file scf.save/

     init_run     :      3.27s CPU      3.49s WALL (       1 calls)
     electrons    :    121.94s CPU    129.38s WALL (       1 calls)

     Called by init_run:
     wfcinit      :      2.06s CPU      2.24s WALL (       1 calls)
     potinit      :      0.35s CPU      0.37s WALL (       1 calls)
     hinit0       :      0.74s CPU      0.78s WALL (       1 calls)

     Called by electrons:
     c_bands      :    111.74s CPU    118.15s WALL (      12 calls)
     sum_band     :      8.83s CPU      9.62s WALL (      12 calls)
     v_of_rho     :      0.83s CPU      0.87s WALL (      13 calls)
     newd         :      0.48s CPU      0.53s WALL (      13 calls)
     mix_rho      :      0.09s CPU      0.10s WALL (      12 calls)

     Called by c_bands:
     init_us_2    :      0.67s CPU      0.59s WALL (     375 calls)
     cegterg      :    106.89s CPU    112.99s WALL (     180 calls)

     Called by sum_band:
     sum_band:bec :      0.01s CPU      0.01s WALL (     180 calls)
     addusdens    :      0.65s CPU      0.67s WALL (      12 calls)

     Called by *egterg:
     h_psi        :     46.89s CPU     51.33s WALL (    1227 calls)
     s_psi        :      8.79s CPU      9.16s WALL (    1227 calls)
     g_psi        :      0.14s CPU      0.14s WALL (    1032 calls)
     cdiaghg      :     24.53s CPU     25.18s WALL (    1212 calls)

     Called by h_psi:
     h_psi:pot    :     46.75s CPU     51.07s WALL (    1227 calls)
     h_psi:calbec :      9.90s CPU     10.38s WALL (    1227 calls)
     vloc_psi     :     27.52s CPU     31.41s WALL (    1227 calls)
     add_vuspsi   :      9.31s CPU      9.27s WALL (    1227 calls)

     General routines
     calbec       :     12.87s CPU     13.52s WALL (    1407 calls)
     fft          :      0.39s CPU      0.47s WALL (     168 calls)
     ffts         :      0.03s CPU      0.01s WALL (      25 calls)
     fftw         :     28.64s CPU     32.93s WALL (   97364 calls)
     interpolate  :      0.06s CPU      0.08s WALL (      13 calls)

     Parallel routines
     fft_scatt_xy :      3.42s CPU      3.78s WALL (   97557 calls)
     fft_scatt_yz :      8.70s CPU     10.91s WALL (   97557 calls)

     PWSCF        :  2m 5.57s CPU     2m14.60s WALL

优化编译版本输出结果:

     highest occupied level (ev):    21.1832

!    total energy              =   -1259.25185017 Ry
     Harris-Foulkes estimate   =   -1259.25185020 Ry
     estimated scf accuracy    <       0.00000003 Ry

     The total energy is the sum of the following terms:

     one-electron contribution =    -125.55179190 Ry
     hartree contribution      =     164.79277944 Ry
     xc contribution           =    -169.12506962 Ry
     ewald contribution        =   -1129.36776808 Ry

     convergence has been achieved in  12 iterations

     Writing output data file scf.save/

     init_run     :      1.14s CPU      1.31s WALL (       1 calls)
     electrons    :     54.36s CPU     57.23s WALL (       1 calls)

     Called by init_run:
     wfcinit      :      0.86s CPU      0.96s WALL (       1 calls)
     potinit      :      0.09s CPU      0.11s WALL (       1 calls)
     hinit0       :      0.15s CPU      0.18s WALL (       1 calls)

     Called by electrons:
     c_bands      :     48.91s CPU     51.33s WALL (      12 calls)
     sum_band     :      4.63s CPU      5.00s WALL (      12 calls)
     v_of_rho     :      0.50s CPU      0.50s WALL (      13 calls)
     newd         :      0.23s CPU      0.24s WALL (      13 calls)
     mix_rho      :      0.11s CPU      0.10s WALL (      12 calls)

     Called by c_bands:
     init_us_2    :      0.59s CPU      0.52s WALL (     375 calls)
     cegterg      :     46.47s CPU     48.67s WALL (     180 calls)

     Called by sum_band:
     sum_band:bec :      0.00s CPU      0.01s WALL (     180 calls)
     addusdens    :      0.24s CPU      0.24s WALL (      12 calls)

     Called by *egterg:
     h_psi        :     23.69s CPU     25.21s WALL (    1227 calls)
     s_psi        :      2.16s CPU      2.48s WALL (    1227 calls)
     g_psi        :      0.03s CPU      0.10s WALL (    1032 calls)
     cdiaghg      :     13.07s CPU     13.43s WALL (    1212 calls)

     Called by h_psi:
     h_psi:pot    :     23.42s CPU     24.96s WALL (    1227 calls)
     h_psi:calbec :      2.54s CPU      2.73s WALL (    1227 calls)
     vloc_psi     :     18.39s CPU     19.73s WALL (    1227 calls)
     add_vuspsi   :      2.48s CPU      2.49s WALL (    1227 calls)

     General routines
     calbec       :      3.16s CPU      3.47s WALL (    1407 calls)
     fft          :      0.40s CPU      0.37s WALL (     168 calls)
     ffts         :      0.01s CPU      0.01s WALL (      25 calls)
     fftw         :     19.18s CPU     20.54s WALL (   97364 calls)
     interpolate  :      0.03s CPU      0.04s WALL (      13 calls)

     Parallel routines
     fft_scatt_xy :      2.45s CPU      2.63s WALL (   97557 calls)
     fft_scatt_yz :      5.49s CPU      5.84s WALL (   97557 calls)

     PWSCF        :    55.82s CPU     1m 0.23s WALL

4. 注意事项

  1. 镜像默认的 LD_LIBRARY_PATH 未设置,需手动指定。

  2. 有时会因系统资源不足而出现编译器内部错误。此时可以增加内存,或取消并行编译(取消 make -j 选项)。

  3. QE 不能完全并行编译,但是单独的模块可以:make -j pw

  4. 编译 Open MPI 时需要设置 ./configure --with-cma="no",不然会一直输出:

    Read -1, expected ###, errno =1
    Read -1, expected ###, errno =1
    Read -1, expected ###, errno =1
    ...
    
  5. Open MPI 禁止 root 用户直接调用,这会导致 QE 测试运行失败。建议创建新用户。


2018.08.28 修正配置 QE 时的参数FCFLAGS=...FFLAGS=...

Build Node.js native addons without installing Visual Studio

最近用Electron写程序,间接地需要编译某个native addon(node-nslog)。

在Windows上编译native模块需要VC++编译器,然而并不需要安装整个Visual Studio,微软良心的提供了多个版本的VC++编译器

  1. 首先下载Microsoft Visual C++ Build Tools 2015,一定要同时安装Win10的SDK。

  2. 设置环境变量GYP_MSVS_VERSION=2015或执行npm config set msvs_version 2015 --global,再或者npm install nslog --msvs_version=2015也行。

  3. 打开%LOCALAPPDATA%\Microsoft\MSBuild\v4.0\Microsoft.Cpp.x64.user.props这个文件,加入以下内容(这是64位编译的配置方法,32位同理):

    <PropertyGroup>
        <IncludePath>C:\Program Files (x86)\Windows Kits\10\Include\10.0.10150.0\ucrt;$(IncludePath)</IncludePath>
        <LibraryPath>C:\Program Files (x86)\Windows Kits\10\Lib\10.0.10150.0\ucrt\x64;$(LibraryPath)</LibraryPath>
    </PropertyGroup>
    

Enjoy it!


2016.11.28 修复链接。