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====== Receta de compilación de VASP ======

Disclaimer: Es necesario tener una licencia de VASP para poder acceder a los archivos fuentes. 

===== Dependencias =====

==== Intel OneAPI ====

Viene incluído en el cluster. Se accede mediante el módulo ''intel''. La versión default es la ''2023.2.1''.

==== Intel MPI ====

Viene inluído en el cluster. Luego de cargar el módulo ''intel'', se debe cargar el módulo ''mpi''.


==== Intel MKL ====

Viene incluído en el cluster. Se accede mediante el módulo ''intel''. La versión default es la ''2023.2.1''.

==== Wannier90 ====

Se encuentra en el directorio  ''/data/shared/apps/wannier90''.


==== LIBXC ====

[[https://libxc.gitlab.io/|Web oficial de libxc]].

Descargar de [[https://libxc.gitlab.io/download/|aquí]] la versión de libxc deseada.

Dentro de un nodo de cómputo:
<code>srun --pty -N 1 -n 1 -c 64 --cpu-bind=none bash</code>

<code>module purge
module load intel autotools
autoreconf -i
./configure --prefix=/data/shared/apps/libxc/7.0.0/ CC=icc CF=ifort --disable-fhc
make -j 64
make install
</code>

Al utilizar ''--disable-fhc'' los tests de ''make check'' fallan. Supuestamente se pueden parchear los tests. [[https://gitlab.com/libxc/libxc/-/issues/489| Ver issue en gitlab de libxc]].

Luego de compilar, libxc se encuentra en el directorio ''/data/shared/apps/libxc/7.0.0/''.

==== HDF5 ====

Viene incluído en el cluster. Se accede mediante el módulo ''hdf5''.

===== Compilando VASP =====

==== makefile.include ====
<code># Default precompiler options
CPP_OPTIONS = -DHOST=\"LinuxIFC\" \
              -DMPI -DMPI_BLOCK=8000 -Duse_collective \
              -DCACHE_SIZE=4000 \
              -Davoidalloc \
              -Dvasp6 \
              -Dtbdyn \
              -Dfock_dblbuf \
              -D_OPENMP

CPP         = fpp -f_com=no -free -w0  $*$(FUFFIX) $*$(SUFFIX) $(CPP_OPTIONS)

FC          = mpiifort -qopenmp
FCL         = mpiifort

FREE        = -free -names lowercase

FFLAGS      = -assume byterecl -w

OFLAG       = -O2
OFLAG_IN    = $(OFLAG)
DEBUG       = -O0

# For what used to be vasp.5.lib
CPP_LIB     = $(CPP)
FC_LIB      = $(FC)
CC_LIB      = icc
CFLAGS_LIB  = -O
FFLAGS_LIB  = -O1
FREE_LIB    = $(FREE)

OBJECTS_LIB = linpack_double.o

# For the parser library
CXX_PARS    = icpc
LLIBS       = -lstdc++

##
## Customize as of this point! Of course you may change the preceding
## part of this file as well if you like, but it should rarely be
## necessary ...

# When compiling on the target machine itself, change this to the
# relevant target when cross-compiling for another architecture
VASP_TARGET_CPU ?= -xHOST
FFLAGS     += $(VASP_TARGET_CPU)

# Intel MKL (FFTW, BLAS, LAPACK, and scaLAPACK)
# (Note: for Intel Parallel Studio's MKL use -mkl instead of -qmkl)
FCL        += -qmkl
MKLROOT    ?= /opt/intel/oneapi/mkl/2023.2.0
LLIBS      += -L$(MKLROOT)/lib/intel64 -lmkl_scalapack_lp64 -lmkl_blacs_intelmpi_lp64
INCS       += -I$(MKLROOT)/include/fftw

# HDF5-support (optional but strongly recommended, and mandatory for some features)
CPP_OPTIONS += -DVASP_HDF5
HDF5_ROOT   ?= /opt/ohpc/pub/libs/intel/hdf5/1.10.8
LLIBS       += -L$(HDF5_ROOT)/lib -lhdf5_fortran
INCS        += -I$(HDF5_ROOT)/include

# For the VASP-2-Wannier90 interface (optional)
CPP_OPTIONS    += -DVASP2WANNIER90
WANNIER90_ROOT ?= /data/shared/apps/wannier90/3.1.0
LLIBS          += -L$(WANNIER90_ROOT)/lib -lwannier

# For the fftlib library (hardly any benefit in combination with MKL's FFTs)
#FCL         = mpiifort fftlib.o -qmkl
#CXX_FFTLIB  = icpc -qopenmp -std=c++11 -DFFTLIB_USE_MKL -DFFTLIB_THREADSAFE
#INCS_FFTLIB = -I./include -I$(MKLROOT)/include/fftw
#LIBS       += fftlib
# For libxc

CPP_OPTIONS += -DUSELIBXC
LIBXC_ROOT  ?= /data/shared/apps/libxc/6.2.2
LLIBS       += -L$(LIBXC_ROOT)/lib -lxcf03 -lxc
INCS        += -I$(LIBXC_ROOT)/include

# For machine learning library vaspml (experimental)
#CPP_OPTIONS += -Dlibvaspml
#CPP_OPTIONS += -DVASPML_USE_CBLAS
#CPP_OPTIONS += -DVASPML_USE_MKL
#CPP_OPTIONS += -DVASPML_DEBUG_LEVEL=3
#CXX_ML      = mpiicpc -cxx=icpx -qopenmp
#CXXFLAGS_ML = -O3 -std=c++17 -Wall
#INCLUDE_ML  =
</code>

En un nodo de cómputo:

<code>srun --pty -N 1 -n 1 -c 64 --cpu-bind=none bash</code>

cargar módulos y compilar:
<code>module purge
module load intel mpi hdf5
make DEPS=1 -j 64 all
</code>