Install and Run

Prerequisites

In this guide, it is assumed that readers have a basic knowledge of Linux and its command line operations. For the installation of SALMON, following packages are required.

• Fortran90/C compiler. SALMON assumes users have one of the following compilers:
  • GCC (Gnu Compiler Collection)
  • Intel Compiler
  • Fujitsu Compiler (at FX100 and A64FX)
• One of the following library packages for linear algebra:
  • Netlib BLAS/LAPACK/ScaLAPACK
  • Intel Math Kernel Library (MKL)
  • Fujitsu Scientific Subroutine Library 2 (SSL-II)
• Build tools:
  • CMake

If you use other compilers, you may need to change build scripts (CMake). See Additional options in configure.py script. If no numerical library is installed on your computer system, you may need to install BLAS/LAPACK by yourself. See Troubleshooting of the Installation Process.

For the installation of SALMON, we adopt the CMake tools as the first option. If there were any problems to use CMake tools in your environment, you may use the GNU make tools. See Troubleshooting of the Installation Process.

Download

The newest version of SALMON can be downloaded from download page. To extract files from the downloaded file SALMON-<VERSION>.tar.gz, type the following command in the command-line:

$ tar -zxvf ./salmon-<VERSION>.tar.gz

After the extraction, the following directories will be created:

SALMON
  |- src          Source codes
  |- example      Samples
  |- cmakefiles   CMake related files
  |- gnumakefiles GNU Makefiles for building

Build and Install

To compile SALMON to create executable the binary files, we adopt to use CMake tools as the first option. In case you fail to build SALMON using CMake in your environment, we may use Gnu Make. See Build using GNU Makefile.

Checking CMake availability

First, examine whether CMake is usable in your environment or not. Type the following in Linux command-line:

$ cmake --version

If CMake is not installed in your system, an error message such as cmake: command not found will appear. If CMake is installed on your system, the version number will be shown. To build SALMON, CMake of version 3.14.0 or later is required. If you confirm that CMake of version 3.14.0 or later is installed in your system, proceed to Build using CMake. However, we realize that old versions of CMake are installed in many systems. If CMake is not installed or CMake of older versions is installed in your system, you need to install the new version by yourself. It is a simple procedure and explained below.

Installation of CMake (pre-compiled binary of Linux)

CMake is a cross-platform build tool. The simplest way to make CMake usable in your environment is to get the binary distribution of CMake from the download page. (The file name of the binary distribution will be cmake-<VERSION>-<PLATFORM>.tar.gz). In standard Linux environment, a file for the platform of Linux x86_64 will be appropriate.

To download the file, proceed as follows: We assume that you are in the directory that you extracted files from the downloaded file of SALMON, and that you will use the version 3.16.8. First get the URL of the download link from your browser, and use wget command in your Linux command-line:

$ wget https://cmake.org/files/v3.16/cmake-3.16.8-Linux-x86_64.tar.gz

Next, unpack the archive by:

$ tar -zxvf cmake-3.16.8-Linux-x86_64.tar.gz

and you will have the binary make-3.16.8-Linux-x86_64/bin/cmake in your directory.

To make the cmake command usable in your command-line, you need to modify the environment variable $PATH so that the executable of CMake are settled inside the directory specified in your $PATH. If you use the bash shell, you need to modify the file ~/.bashrc that specifies the $PATH variable. It can be done by typing the following command in your login directory:

$ export PATH=<SALMON_INSTALLATION_DIRECTORY>/cmake-3.16.8-Linux-x86_64/bin:$PATH

and then reload the configuration by typing:

$ source ~/.bashrc

See Installation of CMake describes Other way of the installation.

Build using CMake

Confirming that CMake of version 3.14.0 or later can be usable in your environment, proceed the following steps. We assume that you are in the directory SALMON.

1. Create a new temporary directory build and move to the directory:

   $ mkdir build
   $ cd build
   

2. Execute the python script ''configure.py'' and then make:

   $ python ../configure.py --arch=ARCHITECTURE --prefix=../
   $ make
   $ make install
   

In executing the python script, you need to specify ARCHITECTURE that indicates the architecture of the CPU in your computer system such as intel-avx. The options of the ARCHITECUTRE are as follows:

arch	Detail	Compiler	Numerical Library
intel-knl	Intel Knights Landing	Intel Compiler	Intel MKL
intel-knc	Intel Knights Corner	Intel Compiler	Intel MKL
intel-avx	Intel Processer (Ivy-, Sandy-Bridge)	Intel Compiler	Intel MKL
intel-avx2	Intel Processer (Haswell, Broadwell ..)	Intel Compiler	Intel MKL
intel-avx512	Intel Processer (Skylake-SP)	Intel Compiler	Intel MKL
fujitsu-fx100	FX100 Supercomputer	Fujitsu Compiler	SSL-II
fujitsu-a64fx-ea	A64FX processor (Fugaku, FX1000, FX700)	Fujitsu Compiler	SSL-II

If the build is successful, you will get a file salmon at the top-level build directory.

Files necessary to run SALMON

To run SALMON, at least two kinds of files are required for any calculations. One is an input file with the filename extension *.inp that should be read from the standard input stdin. This file should be prepared in the Fortran90 namelist format. Pseudopotential files of relevant elements are also required. Depending on your purpose, some other files may also be necessary. For example, coordinates of atomic positions of the target material may be either written in the input file or prepared as a separate file.

Pseudopotentials

SALMON utilizes norm-conserving pseudpotentials. You may find pseudopotentials of some elements in the samples prepared in Exercises. In SALMON, several formats of pseudopotentials may be usable. Pseudopotentials with an extension .fhi can be obtained from the website listed below. (This is a part of previous atomic data files for the ABINIT code.)

Pseudopotential	Website
Pseudopotentials for the ABINIT code	https://www.abinit.org/sites/default/files/PrevAtomicData/psp-links/psp-links/lda_fhi

Filenames of the pseudopotentials should be written in the input file.

input file

Input files are composed of several blocks of namelists:

&namelist1
  variable1 = int_value
  variable2 = 'char_value'
/
&namelist2
  variable1 = real8_value
  variable2 = int_value1, int_value2, int_value3
/

A block of namelists starts with &namelist line and ends with / line. The blocks may appear in any order.

Between two lines of &namelist and /, descriptions of variables and their values appear. Note that many variables have their default values so that it is not necessary to give values for all variables. Descriptions of the variables may appear at any position if they are between &namelist and /.

SALMON describes electron dynamics in systems with both isolated and periodic boundary conditions. The boundary condition is specified by the variable iperiodic in the namelist &system.

Calculations are usually achieved in two steps; first, the ground state calculation is carried out and then electron dynamics calculations in real time is carried out. A choice of the calculation mode or theory in the calculation is specified by the variable theory in the namelist &calculation. In the typical way, the ground state calculation based on DFT is first carried out specifying theory = 'dft'. Then the real-time electron dynamics calculation based on TDDFT is carried out specifying theory = 'tddft_pulse'.

In Exercises, we prepare six exercises that cover typical calculations feasible by SALMON. We prepare explanations of the input files of the exercises that will help to prepare input files of your own interests.

There are more than 20 groups of namelists. A complete list of namelist variables is given in the file SALMON/manual/input_variables.md. Namelist variables that are used in our exercises are explained at Input Keywords for Exercises.

Run SALMON

Before running SALMON, the following preparations are required as described above: The executable file of salmon should be built from the source file of SALMON. An input file inputfile.inp and pseudopotential files should also be prepared.

The execution of the calculation can be done as follows: In single process environment, type the following command:

$ salmon < inputfile.inp > fileout.out

In multiprocess environment in which the command to execute parallel calculations using MPI is mpiexec, type the following command:

$ mpiexec -n NPROC salmon < inputfile.inp > fileout.out

where NPROC is the number of MPI processes that you will use.

The execution command and the job submission procedure depends much on local environment. We summarize general conditions to execute SALMON:

• SALMON runs in both single-process and multi-process environments using MPI.
• executable files are prepared as salmon in the standard build procedure.
• to start calculations, inputfile.inp should be read through stdin.

Tips for large-scale calculation

We explain below some tips that will be useful to improve performance when you carry out large scale simulations using world top-level supercomputers. Therefore, the following contents will only be useful only for limited users.

MPI process distribution

SALMON provides three variables to determine the process distribution/allocation.

• nproc_k
• nproc_ob
• nproc_rgrid(3)

In SALMON, the process distribution is determined automatically as default. However, in many situations, an explicit assignment of the process distribution will provide a better performance than the default setting.

We recommend to distribute the processes as follows,

If you use k-points ( the number of k-points is greater than 1) and the number of the real-space grid (num_rgrid) is not very large (about 16^3):

• First, assign many processes to nproc_k.
• Then, assign the remaining processes to nproc_ob.
• Not dividing the spatial grid, nproc_rgrid = 1, 1, 1.

Else:

• First, assign the processes to nproc_ob.

• Then, assign the remaining processes to nproc_rgrid.

  • If real-space grid size (num_rgrid(1:3) = al(1:3) / dl(1:3)) is equal to or larger than about 64^3,

  you should find a balanced distribution between nproc_rgrid and nproc_ob.

Improve the performance of the eigenvalues solver

In DFT calculations of large systems, subspace diagonalization becomes the performance bottleneck in the entire calculation. Therefore, it is important to use a parallel eigenvalues solver. In SALMON, a LAPACK routine without parallelization is used for the diagonalization as default. As parallelized solvers, ScaLAPACK and EigenExa are usable. To use them, it is necessary to rebuild SALMON enabling ScaLAPACK/EigenExa. You can find the instruction in Install and Run.

To execute SALMON using ScaLAPACK/EigenExa, either yn_scalapack = 'y' or yn_eigenexa = 'y' should be included in the inputfile:

&parallel
  yn_scalapack = 'y'         ! use ScaLAPACK for diagonalization
  !yn_eigenexa  = 'y'        ! use EigenExa
  yn_scalapack_red_mem = 'y' ! to reduce the memory consumption
/

ScaLAPACK/EigenExa solves the eigenvalue problem with nproc_ob process distribution. If nproc_ob = 1, ScaLAPACK/EigenExa will perform in the same way as the LAPACK library.

Improve the performance of Hartree solver

For periodic systems, a Fourier transformation is used to solve the Poisson equation (to calculate the Hartree potential). In SALMON, a simple Fourier transformation without Fast Fourier Transformation (FFT) is used as default. In SALMON, a parallelized FFT foutine, FFTE, is usable and works efficiently for large systems. In using FFTE, the following conditions should be satisfied:

num_rgrid(1) mod nproc_rgrid(2) = 0
num_rgrid(2) mod nproc_rgrid(2) = 0
num_rgrid(2) mod nproc_rgrid(3) = 0
num_rgrid(3) mod nproc_rgrid(3) = 0

In addition, the prime factors for the number of real-space grid of each direction (num_rgrid(1:3)) must be a combination of 2, 3 or 5.

To use FFTE, yn_ffte = 'y' should be included in the input file:

&parallel
  yn_ffte = 'y'
/

Improve IO performance (write/read wavefunction)

Almost all supercomputer systems provide distributed filesystems such as Lustre. Distributed filesystems are equipped with a meta-data server (MDS) and an object-storage server (OST). The OST stores real user data files, and the MDS stores the address of the user date files in the OST. When accessing to the data files in the OST, the process send a query about the OST address to MDS. Then, a network contention may occur in the query process.

In most implementations of the filesystem, the MDS that replies to the query is determined by the directory structure. For a calculation in which k-point is not used, method_wf_distributor and nblock_wf_distribute are prepared to reduce the network contention:

&control
  method_wf_distributor = 'slice' ! every orbital function is stored as a single file.
  nblock_wf_distribute  = 32      ! files of 32 orbital functions are stored in one directory.
/

Improve the communication performance for mesh-torus network system

Large-scale supercomputers often adopt a mesh-torus network system such as Cray dragon-fly and Fujitsu Tofu to achieve high scalability with relatively low cost. In SALMON, a special MPI process distribution (communicator creation rule) is prepared to improve the performance in large-scale mesh-torus network systems.

Currently, we provide the communicator creation rule for "Supercomputer Fugaku", which is developed by RIKEN R-CCS and Fujitsu limited. Fugaku is equipped with a 6-D mesh-torus network which is called "Tofu-D". Users may control it as a 3-D logical network. SALMON utilizes 5-D array (wavefunction(x, y, z, orbital, k-point)) as a domain for parallelization. We create a map that connects the 3-D network to the 5-D array distribution.

We introduce the following variables and conditons to assign the 3-D mesh-torus network to the 5-D array distribution:

PW           = nproc_ob * nproc_k
(PX, PY, PZ) = nproc_rgrid
PPN          = '# of process per node' (we recommend the value 4 in Fugaku)

Requested process shape: (PX, PY, PZ, PW)
Tofu-D network    shape: (TX, TY, TZ)
Actual process    shape: (TX * PPN, TY, TZ)

if (process_allocation == 'grid_sequential'):
  PW  = PW1 * PW2 * PW3
  PW1 = (TX * PPN) / PX
  PW2 = TY         / PY
  PW3 = TZ         / PZ
  TX  = (PX * PW1) / PPN
  TY  = PY * PW2
  TZ  = PZ * PW3

else if (process_allocation == 'orbital_sequential'):
  PX  = PX1 * PX2 * PX3
  PX1 = (TX * PPN) / PW
  PX2 = TY         / PY
  PX3 = TZ         / PZ
  TX  = (PW * PX1) / PPN
  TY  = PY * PX2
  TZ  = PZ * PX3

From these conditions, you can determine the suitable process distribution and the Tofu-D network shape (compute node shape). process_allocation input variable controls the order of the process distribution. It indicates which communications should be executed in closer processes.

• process_allocation = 'grid_sequential'
  • (PX, PY, PZ, PW), nproc_rgrid major ordering
  • improves nproc_rgrid related communication performance
  • communicator: s_parallel_info::icomm_r, icomm_x, icomm_y, icomm_z, icomm_xy
  • suitable theory: 'dft' and 'dft_md'
• process_allocation = 'orbital_sequential'
  • (PW, PY, PZ, PX), nproc_ob major ordering
  • improves nproc_ob related communication performance
  • communicator: s_parallel_info::icomm_o and icomm_ko
  • suitable theory: 'tddft_response', 'tddft_pulse', 'single_scale_maxwell_tddft' and 'multi_scale_maxwell_tddft'

Appendix

Additional options in configure.py script

Manual specifications of compiler and environment variables

In executing configure.py, you may manually specify compiler and environment variables instead of specifying the architecture, for example:

$ python ../configure.py FC=mpiifort CC=mpiicc FFLAGS="-xAVX" CFLAGS="-restrict -xAVX"

The major options of configure.py are as follows:

Commandline switch	Detail
-a ARCH, --arch=ARCH	Target architecture
--enable-mpi, --disable-mpi	enable/disable MPI parallelization
--enable-scalapack, --disable-scalapack	enable/disable computations with ScaLAPACK library
--enable-eigenexa, --disable-eigenexa	enable/disable computations with RIKEN R-CCS EigenExa library
--enable-libxc, --disable-libxc	enable/disable computations with Libxc library
--with-lapack	specified LAPACK/ScaLAPACK installed directory
--with-libxc	specified Libxc installed directory
--debug	enable debug build
--release	enable release build
FC, FFLAGS	User-defined Fortran Compiler, and the compiler options
CC, CFLAGS	User-defined C Compiler, and the compiler options

In the build procedure by CMake, they search the following libraries. If the libraries don't found in the path that is specified by environment variables, they will build the required libraries automatically.

• Netlib LAPACK (includes BLAS), and ScaLAPACK

  • We will download and build the Netlib libraries as the typical implementation.
  • http://www.netlib.org/lapack/
  • http://www.netlib.org/scalapack/

• Libxc

  • https://www.tddft.org/programs/libxc/

EigenExa will download and build automatically even if the library is installed to your machine.

Build for single process calculations

When using the --arch option, MPI parallelization is enabled as default in almost case. If you use a single processor machine, explicitly specify --disable-mpi in executing the python script:

$ python ../configure.py --arch=<ARCHITECTURE> --disable-mpi

Build by user-specified compiler

If you want that specify the compiler, set the FC and CC flags in executing the python script:

$ python ../configure.py FC=gfortran CC=gcc

When not using the --arch option, MPI parallelization is disabled as default.

Build using GNU Makefile

If CMake build fails in your environment, we recommend you to try to use Gnu Make for the build process. First, enter the directory gnumakefiles:

$ cd SALMON/gnumakefiles

In the directory, Makefile files are prepared for several architectures:

• gnu-mpi
• intel-mpi
• gnu-without-mpi
• intel-without-mpi

Makefile files with *-without-mpi indicate that they are for single processor environment. Choose Makefile appropriate for your environment, and execute the make command:

$ make -f Makefile.PLATFORM

If the make proceeds successful, a binary file is created in the directory SALMON/bin/.

Troubleshooting of the Installation Process

Installation of CMake

The CMake is a cross-platform build tool. In order to build the SALMON from the source code, the CMake of version 3.14.0 or later is required. You may install it following one of the three instructions below.

Installation by package manager

If your system has a built-in package manager, you may conveniently install the CMake tools as below:

Debian/Ubuntu Linux

sudo apt-get install cmake

Fedora Linux/CentOS

sudo yum install cmake

openSUSE Linux

sudo zypper install cmake

Installation from source code

You can get the source code distribution from the download page. In this time, we will use the cmake version 3.16.8 as an example. Download the archive by wget comamnd and unpack it as below:

wget https://cmake.org/files/v3.16/cmake-3.16.8.tar.gz
tar -zxvf cmake-3.16.8.tar.gz

And, move to the unpacked directory and build.

cd cmake-3.16.8
./configure --prefix=INSTALLATION_DIRECTORY
make
make install

(replace INSTALLATION_DIRECTORY to your installation directory.)

Next, to utilize the cmake command, it is required that the executable are settled inside the directory specified in your $PATH. If you use the bash shell, edit ~/.bashrc and append the line:

export PATH=INSTALLATION_DIRECTORY/bin:$PATH

and reload the configuration:

source ~/.bashrc