NAMD 3.0b6 - SMP(single node)
Webpage
http://www.ks.uiuc.edu/Research/namd/
Version
3.0b6
Build Environment
- GCC 12.1.1 (gcc-toolset-12)
- Intel MKL 2024.0
Files Required
- NAMD_3.0b6_Source.tar.gz
- tcl, tcl-threaded are obtained from http://www.ks.uiuc.edu/Research/namd/libraries
- MKL was used for FFTW
Build Procedure
#!/bin/sh
VERSION=3.0b6
CHARM_VERSION=7.0.0
WORKDIR=/gwork/users/${USER}
SOURCEDIR=/home/users/${USER}/Software/NAMD/${VERSION}
NAME=NAMD_${VERSION}_Source
TARBALL=${SOURCEDIR}/${NAME}.tar.gz
LIBURL=http://www.ks.uiuc.edu/Research/namd/libraries
#FFTW=fftw-linux-x86_64
#FFTW_URL=${LIBURL}/${FFTW}.tar.gz
TCL=tcl8.5.9-linux-x86_64
TCL_URL=${LIBURL}/${TCL}.tar.gz
TCL_THREADED=tcl8.5.9-linux-x86_64-threaded
TCL_THREADED_URL=${LIBURL}/${TCL_THREADED}.tar.gz
#TARBALL_FFTW=${SOURCEDIR}/${FFTW}.tar.gz
TARBALL_TCL=${SOURCEDIR}/${TCL}.tar.gz
TARBALL_TCL_THREADED=${SOURCEDIR}/${TCL_THREADED}.tar.gz
PARALLEL=12
#------------------------------------------------------------------
umask 0022
export LANG=""
export LC_ALL=C
module -s purge
module -s load gcc-toolset/12
module -s load mkl/2024.0
cd ${WORKDIR}
if [ -d ${NAME} ]; then
mv ${NAME} namd_erase
rm -rf namd_erase &
fi
tar zxf ${TARBALL}
cd ${NAME}
tar xf charm-${CHARM_VERSION}.tar
cd charm-v${CHARM_VERSION}
export CC=gcc
export CXX=g++
export F90=gfortran
export F77=gfortran
./build charm++ multicore-linux-x86_64 \
--no-build-shared --with-production -j${PARALLEL}
cd ../
tar zxf ${TARBALL_TCL}
mv ${TCL} tcl
tar zxf ${TARBALL_TCL_THREADED}
mv ${TCL_THREADED} tcl-threaded
./config Linux-x86_64-g++ \
--charm-arch multicore-linux-x86_64 \
--with-mkl \
--with-python
cd Linux-x86_64-g++
make -j${PARALLEL}
make release
# install contents of Linux-x86_64-g++/NAMD_3.0b6_Linux-x86_64-multicore.tar.gz into /apl/namd/3.0b6-smp manually
Notes
- GCC12 binary shows a slightly better performance than Intel oneAPI 2023 Compiler (Classic) one.
- For multi-nodes runs, please use MPI version.
- Acceleration by AVX-512 is not available for EPYC Milan CPUs.
- Please consider to use single node cuda version for higher performance.