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How to compile the CP2K code

1. Acquire the code

For users, the preferred method is to download a release (use the versioned tarballs, cp2k-X.Y.tar.bz2). For developers, the preferred method is to download from Git.

For more details on downloading CP2K, see https://www.cp2k.org/download.

2. Install prerequisites

The easiest way to build CP2K with all its dependencies is as a Docker container.

Alternatively, the toolchain script can also be run directly.

For a complete introduction to the toolchain script, see the README.

The basic steps are:

  • Read toolchain installation options:
cd tools/toolchain/
./install_cp2k_toolchain.sh --help
  • Launch toolchain script (example option choice)
./install_cp2k_toolchain.sh --with-libxsmm=install --with-openblas=system \
     --with-fftw=system --with-reflapack=no  --enable-cuda
  • Once the script has completed successfully, follow the instructions given at the end of its output. Note that the pre-built arch files provided by the toolchain are for the GNU compiler, users must adapt them for other compilers. It is possible to use the provided arch files as guidance.

There are arch files for a few specific platforms (e.g. Linux-gnu-x86_64, Linux-intel-x86_64) which include a toolchain build. Sourcing such an arch file in the cp2k folder launches a toolchain build, e.g.

source ./arch/Linux-gnu-x86_64.psmp

After a successful toolchain build, run one of the suggested make commands

make -j ARCH=Linux-gnu-x86_64 VERSION=psmp

Check also the corresponding HowTos for Apple M1 (macOS) and Cray XC40/50 (Piz Daint, CSCS).

Sub-points here discuss prerequisites needed to build CP2K. Copies of the recommended versions of 3rd party software can be downloaded from https://www.cp2k.org/static/downloads/.

Generally, CP2K supports only one version for each of its dependencies. These are defined by the toolchain scripts. Other versions might work too, but we don't test them. So, your mileage may vary.

2a. GNU make (required, build system)

GNU make should be on your system (gmake or make on linux) and used for the build, go to https://www.gnu.org/software/make/make.html download from https://ftp.gnu.org/pub/gnu/make/.

2b. Python (required, build system)

Python 3.5+ is needed to run the dependency generator. On most system Python is already installed. For more information visit: https://www.python.org

2c. Fortran and C Compiler (required, build system)

A Fortran 2008 compiler and matching C99 compiler should be installed on your system. We have good experience with gcc/gfortran (gcc >=4.6 works, later version recommended). Be aware that some compilers have bugs that might cause them to fail (internal compiler errors, segfaults) or, worse, yield a mis-compiled CP2K. Report bugs to compiler vendors; they (and we) have an interest in fixing them. A list of tested compiler can be found here. Always run a make -j test (See point 5.) after compilation to identify these problems.

2d. BLAS and LAPACK (required, base functionality)

BLAS and LAPACK should be installed. Using vendor-provided libraries can make a very significant difference (up to 100%, e.g., ACML, MKL, ESSL), not all optimized libraries are bug free. Use the latest versions available, use the interfaces matching your compiler, and download all patches!

Please note that the BLAS/LAPACK implementation used by CP2K needs to be thread-safe (OpenMP). Examples are the sequential variant of the Intel MKL, the Cray libsci, the OpenBLAS OpenMP variant and the reference BLAS/LAPACK packages. If compiling with MKL, users must define -D__MKL to ensure the code is thread-safe. MKL with multiple OpenMP threads in CP2K requires that CP2K was compiled with the Intel compiler. If the cpp precompiler is used in a separate precompilation step in combination with the Intel Fortran compiler, -D__INTEL_LLVM_COMPILER (-D__INTEL_COMPILER) must be added explicitly (the Intel compiler sets D__INTEL_LLVM_COMPILER otherwise automatically).

On the Mac, BLAS and LAPACK may be provided by Apple's Accelerate framework. If using this framework, -D__ACCELERATE must be defined to account for some interface incompatibilities between Accelerate and reference BLAS/LAPACK.

When building on/for Windows using the Minimalist GNU for Windows (MinGW) environment, you must set -D__MINGW, -D__NO_STATM_ACCESS and -D__NO_SOCKETS to avoid undefined references during linking, respectively errors while printing the statistics.

2e. MPI and ScaLAPACK (optional, required for MPI parallel builds)

MPI (version 3) and SCALAPACK are needed for parallel code. (Use the latest versions available and download all patches!).

⚠️ Note that your MPI installation must match the used Fortran compiler. If your computing platform does not provide MPI, there are several freely available alternatives:

CP2K assumes that the MPI library implements MPI version 3. Older versions of MPI (e.g., MPI 2.0) are not supported and the old flag -D__MPI_VERSION in the arch file will be ignored. CP2K can make use of the mpi_f08 module. If its use is requested, set the flag -D__MPI_F08.

2f. FFTW (optional, improved performance of FFTs)

FFTW can be used to improve FFT speed on a wide range of architectures. It is strongly recommended to install and use FFTW3. The current version of CP2K works with FFTW 3.X (use -D__FFTW3). It can be downloaded from http://www.fftw.org

FFTW is also provided by MKL. Use -D__FFTW3_MKL to use the correct import path.

⚠️ Note that FFTW must know the Fortran compiler you will use in order to install properly (e.g., export F77=gfortran before configure if you intend to use gfortran).

⚠️ Note that on machines and compilers which support SSE you can configure FFTW3 with --enable-sse2. Compilers/systems that do not align memory (NAG f95, Intel IA32/gfortran) should either not use --enable-sse2 or otherwise set the define -D__FFTW3_UNALIGNED in the arch file. Since CP2K is OpenMP parallelized, the FFTW3 threading library libfftw3_threads (or libfftw3_omp) is required.

2g. LIBINT (optional, enables methods including HF exchange)

  • Hartree-Fock exchange (optional, use -D__LIBINT) requires the LIBINT package to be installed.
  • Recommended way to build LIBINT: Download a CP2K-configured LIBINT library from libint-cp2k. Build and install LIBINT by following the instructions provided there. Note that using a library configured for higher maximum angular momentum will increase build time and binary size of CP2K executable (assuming static linking).
  • CP2K is not hardwired to these provided libraries and any other LIBINT library (version >= 2.5.0) should be compatible as long as it was compiled with --enable-eri=1 and default ordering.
  • Avoid debugging information (-g flag) for compiling LIBINT since this will increase library size by a large factor.
  • In the arch file of CP2K: add -D__LIBINT to the DFLAGS. Add -L$(LIBINT_DIR)/lib -lint2 -lstdc++ to LIBS and -I$(LIBINT_DIR)/include to FCFLAGS. lstdc++ is needed if you use the GNU C++ compiler.
  • Libint 1 is no longer supported and the previously needed flags -D__LIBINT_MAX_AM and -D__LIBDERIV_MAX_AM1 are ignored.
  • -D__MAX_CONTR=4 (default=2) can be used to compile efficient contraction kernels up to l=4, but the build time will increase accordingly.

2h. LIBXSMM (optional, improved performance for matrix multiplication)

  • A library for matrix operations and deep learning primitives: https://github.com/hfp/libxsmm/.
  • Add -D__LIBXSMM to enable it, with suitable include and library paths, e.g., FCFLAGS += -I${LIBXSMM_DIR}/include -D__LIBXSMM and LIBS += -L${LIBXSMM_DIR}/lib -lxsmmf -lxsmm -ldl
  • LIBSMM is not used if LIBXSMM is enabled.

2i. CUDA (optional, improved performance on GPU systems)

  • Specify OFFLOAD_CC (e.g., OFFLOAD_CC = nvcc) and OFFLOAD_FLAGS (e.g., OFFLOAD_FLAGS = -O3 -g -w --std=c++11) variables. Remember to include the support for the C++11 standard.
  • Use -D__OFFLOAD_CUDA to generally enable support for Nvidia GPUs
  • Use the -D__DBCSR_ACC and OFFLOAD_TARGET = cuda to enable accelerator support for matrix multiplications.
  • Add -lstdc++ -lcudart -lnvrtc -lcuda -lcublas to LIBS.
  • Specify the GPU type (e.g., GPUVER = P100), possible values are K20X, K40, K80, P100, V100, A100, H100, A40.
  • Specify the C++ compiler (e.g., CXX = g++) and the CXXFLAGS to support the C++11 standard.
  • CUFFT 7.0 has a known bug and is therefore disabled by default. NVIDIA's webpage list a patch (an upgraded version cufft i.e. >= 7.0.35) - use this together with -D__HAS_PATCHED_CUFFT_70.
  • Use -D__OFFLOAD_PROFILING to turn on Nvidia Tools Extensions. It requires to link -lnvToolsExt.
  • Link to a BLAS/ScaLAPACK library that accelerates large DGEMMs (e.g., libsci_acc)
  • Use -D__NO_OFFLOAD_GRID to disable the GPU backend of the grid library.
  • Use -D__NO_OFFLOAD_DBM to disable the GPU backend of the sparse tensor library.
  • Use -D__NO_OFFLOAD_PW to disable the GPU backend of FFTs and associated gather/scatter operations.

2j. LIBXC (optional, wider choice of xc functionals)

  • The version 5.1.0 (or later) of LIBXC can be downloaded from https://www.tddft.org/programs/libxc
  • CP2K does not make use of fourth derivates such that LIBXC may be configured with './configure --disable-lxc <other LIBXC configuration flags>'.
  • During the installation, the directories $(LIBXC_DIR)/lib and $(LIBXC_DIR)/include are created.
  • Add -D__LIBXC to DFLAGS, -I$(LIBXC_DIR)/include to FCFLAGS and -L$(LIBXC_DIR)/lib -lxcf03 -lxc to LIBS.
  • ⚠️ Note that the deprecated flags -D__LIBXC2 and -D__LIBXC3 are ignored.

2k. ELPA (optional, improved performance for diagonalization)

Library ELPA for the solution of the eigenvalue problem

  • ELPA replaces the ScaLAPACK SYEVD to improve the performance of the diagonalization
  • A version of ELPA can be downloaded from http://elpa.rzg.mpg.de/software.
  • During the installation the libelpa_openmp.a is created.
  • Minimal supported version of ELPA is 2018.05.001.
  • Add -D__ELPA to DFLAGS
  • Add -D__ELPA_NVIDIA_GPU, -D__ELPA_AMD_GPU, or -D__ELPA_INTEL_GPU to DFLAGS to enable GPU support for the respective vendor.
  • Add -I$(ELPA_INCLUDE_DIR)/modules to FCFLAGS
  • Add -I$(ELPA_INCLUDE_DIR)/elpa to FCFLAGS
  • Add -L$(ELPA_DIR) to LDFLAGS
  • Add -lelpa to LIBS
  • For specific architectures it can be better to install specifically optimized kernels (see BG) and/or employ a higher optimization level to compile it.

2l. cuSOLVERMp (experimental, improved performance for diagonalization on Nvidia GPUs)

NVIDIA cuSOLVERMp is a high-performance, distributed-memory, GPU-accelerated library that provides tools for the solution of dense linear systems and eigenvalue problems.

  • cuSOLVERMp replaces the ScaLAPACK SYEVD to improve the performance of the diagonalization
  • A version of cuSOLVERMp can be downloaded from https://docs.nvidia.com/hpc-sdk/cusolvermp.
  • Add -D__CUSOLVERMP to DFLAGS
  • Add -lcusolverMp -lcusolver -lcal -lnvidia-ml to LIBS

2m. DLA-Future (optional, experimental, improved performance for diagonalization on Nvidia and AMD GPUs)

DLA-Future is a high-performance, distributed-memory, GPU-accelerated library that provides tools for the solution of eigenvalue problems, based on the pika runtime.

  • DLA-Future replaces the ScaLAPACK SYEVD to improve performance of the diagonalization
  • DLA-Future is available at https://github.com/eth-cscs/DLA-Future
  • DLA-Future is available via the Spack package manager
  • -D__DLAF is defined by CMake when -DCP2K_USE_DLAF=ON

2n. PEXSI (optional, low scaling SCF method)

The Pole EXpansion and Selected Inversion (PEXSI) method requires the PEXSI library and two dependencies (ParMETIS or PT-Scotch and SuperLU_DIST).

  • Download PEXSI (www.pexsi.org) and install it and its dependencies by following its README.md.
  • PEXSI versions 0.10.x have been tested with CP2K. Older versions are not supported.
  • PEXSI needs to be built with make finstall.

In the arch file of CP2K:

  • Add -lpexsi_${SUFFIX} -llapack -lblas -lsuperlu_dist_3.3 -lparmetis -lmetis, and their paths (with -L$(LIB_DIR)) to LIBS.
  • It is important that a copy of LAPACK and BLAS is placed before and after these libraries (replace -llapack and -lblas with the optimized versions as needed).
  • In order to link in PT-Scotch instead of ParMETIS replace -lparmetis -lmetis with: -lptscotchparmetis -lptscotch -lptscotcherr -lscotchmetis -lscotch -lscotcherr
  • Add -I$(PEXSI_DIR)/fortran/ to FCFLAGS.
  • Add -D__LIBPEXSI to DFLAGS.

Below are some additional hints that may help in the compilation process:

  • For building PT-Scotch, the flag -DSCOTCH_METIS_PREFIX in Makefile.inc must not be set and the flag -DSCOTCH_PTHREAD must be removed.
  • For building SuperLU_DIST with PT-Scotch, you must set the following in make.inc:
METISLIB = -lscotchmetis -lscotch -lscotcherr
PARMETISLIB = -lptscotchparmetis -lptscotch -lptscotcherr

2o. QUIP (optional, wider range of interaction potentials)

QUIP - QUantum mechanics and Interatomic Potentials Support for QUIP can be enabled via the flag -D__QUIP.

For more information see http://www.libatoms.org.

2p. PLUMED (optional, enables various enhanced sampling methods)

CP2K can be compiled with PLUMED 2.x (-D__PLUMED2).

See https://cp2k.org/howto:install_with_plumed for full instructions.

2q. spglib (optional, crystal symmetries tools)

A library for finding and handling crystal symmetries

2r. SIRIUS (optional, plane wave calculations)

SIRIUS is a domain specific library for electronic structure calculations.

2s. FPGA (optional, plane wave FFT calculations)

  • Use -D__PW_FPGA to enable FPGA support for PW (fft) calculations. Currently tested only for Intel Stratix 10 and Arria 10 GX1150 FPGAs.
  • Supports single precision and double precision fft calculations with the use of dedicated APIs.
  • Double precision is the default API chosen when set using the -D__PW_FPGA flag.
  • Single precision can be set using an additional -D__PW_FPGA_SP flag along with the -D__PW_FPGA flag.
  • Kernel code must be synthesized separately and copied to a specific location.
  • See https://github.com/pc2/fft3d-fpga/ for the kernel code and instructions for synthesis.
  • Read src/pw/fpga/README.md for information on the specific location to copy the binaries to.
  • Currently supported FFT3d sizes - 16^3, 32^3, 64^3.
  • Include aocl compile flags and -D__PW_FPGA -D__PW_FPGA_SP to CFLAGS, aocl linker flags to LDFLAGS and aocl libs to LIBS.
  • When building FPGA and OFFLOAD together then -D__NO_OFFLOAD_PW must be used.

2t. COSMA (Distributed Communication-Optimal Matrix-Matrix Multiplication Algorithm)

  • COSMA is an alternative for the pdgemm routine included in ScaLAPACK. The library supports both CPU and GPUs.
  • Add -D__COSMA to the DFLAGS to enable support for COSMA.
  • See https://github.com/eth-cscs/COSMA for more information.

2u. LibVori (Voronoi Integration for Electrostatic Properties from Electron Density)

  • LibVori is a library which enables the calculation of electrostatic properties (charge, dipole vector, quadrupole tensor, etc.) via integration of the total electron density in the Voronoi cell of each atom.
  • Add -D__LIBVORI to the DFLAGS to enable support for LibVori.
  • See https://brehm-research.de/libvori for more information.
  • LibVori also enables support for the BQB file format for compressed trajectories, please see https://brehm-research.de/bqb for more information as well as the bqbtool to inspect BQB files.

2v. Torch (Machine Learning Framework needed for NequIP)

2w. ROCM/HIP (Support for AMD GPU)

The code for the HIP based grid backend was developed and tested on Mi100 but should work out of the box on Nvidia hardware as well.

  • Use -D__OFFLOAD_HIP to generally enable support for AMD GPUs
  • Use -D__NO_OFFLOAD_GRID to disable the GPU backend of the grid library.
  • Use -D__NO_OFFLOAD_DBM to disable the GPU backend of the sparse tensor library.
  • Use -D__NO_OFFLOAD_PW to disable the GPU backend of FFTs and associated gather/scatter operations.
  • Add -D__OFFLOAD_UNIFIED_MEMORY to enable unified memory support (experimental and only supports Mi250X and above)
  • Add GPUVER=Mi50, Mi60, Mi100, Mi250
  • Add OFFLOAD_CC = hipcc
  • Add -lamdhip64 to the LIBS variable
  • Add OFFLOAD_FLAGS = '-munsafe-fp-atomics -fopenmp -m64 -pthread -fPIC -D__GRID_HIP -O2 --offload-arch=gfx908 --rocm-path=$(ROCM_PATH)' where ROCM_PATH is the path where the rocm sdk resides. Architectures Mi300(A,X) (gfx1103), Mi250 (gfx90a), Mi100 (gfx908), Mi50 (gfx906) the hip backend for the grid library supports nvidia hardware as well. It uses the same code and can be used to validate the backend in case of access to Nvidia hardware only. To get the compilation working, follow the steps above and set the OFFLOAD_FLAGS with right nvcc parameters (see the cuda section of this document). The environment variable HIP_PLATFORM should be set to HIP_PLATFORM=nvidia to indicate to hipcc to use the nvcc compiler instead.
  • Specify the C++ compiler (e.g., CXX = g++). Remember to set the CXXFLAGS flags to support C++11 standard and OpenMP.
  • When the HIP backend is enabled for DBCSR using -D__DBCSR_ACC, then add -D__HIP_PLATFORM_AMD__ to CXXFLAGS and set OFFLOAD_TARGET = hip.
  • Use -D__OFFLOAD_PROFILING to turn on the AMD ROC TX and Tracer libray. It requires to link -lroctx64 -lroctracer64.

2x. OpenCL Devices

OpenCL devices are currently supported for DBCSR and DBM/DBT, and can cover GPUs and other devices. Kernels can be automatically tuned.

Note: the OpenCL backend uses some functionality from LIBXSMM (dependency). CP2K's offload-library serving DBM/DBT and other libraries depends on DBCSR's OpenCL backend.

  • Installing OpenCL and preparing the runtime environment
    • Installing an OpenCL runtime depends on the operating system and the device vendor. Debian for instance brings two packages called opencl-headers and ocl-icd-opencl-dev which can be present in addition to a vendor-specific installation. The OpenCL header files are only necessary if CP2K/DBCSR is compiled from source. Please note, some implementations ship with outdated OpenCL headers which can prevent using latest features (if an application discovers such features only at compile-time). When building from source, for instance libOpenCL.so is sufficient at link-time (ICD loader). However, an Installable Client Driver (ICD) is finally necessary at runtime.
    • Nvidia CUDA, AMD HIP, and Intel OneAPI are fully equipped with an OpenCL runtime (if opencl-headers package is not installed, CPATH can be needed to point into the former installation, similarly LIBRARY_PATH for finding libOpenCL.so at link-time). Installing a minimal or stand-alone OpenCL is also possible, e.g., following the instructions for Debian (or Ubuntu) as given for every release of the Intel Compute Runtime.
    • The environment variable ACC_OPENCL_VERBOSE prints information at runtime of CP2K about kernels generated (ACC_OPENCL_VERBOSE=2) or executed (ACC_OPENCL_VERBOSE=3) which can be used to check an installation.
  • Building CP2K with OpenCL-based DBCSR
    • CP2K's toolchain supports --enable-opencl to select DBCSR's OpenCL backend. This can be combined with --enable-cuda (--gpu-ver is then imposed) to use a GPU for CP2K's GRID and PW components (no OpenCL support yet) with DBM's CUDA implementation to be preferred.
    • For manually writing an ARCH-file, add -D__OPENCL and -D__DBCSR_ACC to CFLAGS and add -lOpenCL to the LIBS variable, i.e., OFFLOAD_CC and OFFLOAD_FLAGS can duplicate CC and CFLAGS (no special offload compiler needed). Please also set OFFLOAD_TARGET = opencl to enable the OpenCL backend in DBCSR. For OpenCL, it is not necessary to specify a GPU version (e.g., GPUVER = V100 would map/limit to exts/dbcsr/src/acc/opencl/smm/params/tune_multiply_V100.csv). In fact, GPUVER limits tuned parameters to the specified GPU, whereas by default all tuned parameters are embedded (exts/dbcsr/src/acc/opencl/smm/params/*.csv) and applied at runtime. If auto-tuned parameters are not available for DBCSR, well-chosen defaults will be used to populate kernels at runtime.
    • Auto-tuned parameters are embedded into the binary, i.e., CP2K does not rely on a hard-coded location. Setting OPENCL_LIBSMM_SMM_PARAMS=/path/to/csv-file environment variable can supply parameters for an already built application, or OPENCL_LIBSMM_SMM_PARAMS=0 can disable using tuned parameters. Refer to https://cp2k.github.io/dbcsr/ on how to tune kernels (parameters).
  • Building CP2K with OpenCL-based DBM library
    • For manually writing an ARCH-file, add -D__OFFLOAD_OPENCL to CFLAGS in addition to following above instructions for "Building CP2K with OpenCL-based DBCSR". An additional Makefile rule can be necessary to transform OpenCL code into a ressource header file.

2y. matrix-matrix multiplication offloading on GPU using SPLA

The SPLA library is a hard dependency of SIRIUS but can also be used as a standalone library. It provides a generic interface to the blas gemm family with offloading on GPU. Offloading supports both CUDA and ROCM.

To make the functionality available, add the flag -D__SPLA -D__OFFLOAD_GEMM to the DFLAGS variable and compile SPLA with Fortran interface and GPU support. Please note that only the functions replacing the dgemm calls with offload_dgemm will eventually be offloaded to the GPU. The SPLA library has internal criteria to decide if it is worth to do the operation on GPU or not. Calls to offload_dgemm also accept pointers on GPU or a combination of them.

2y. libgrpp (optional, enables calculations with ECPs)

  • libgrpp is a library for the calculation of integrals with GTOs and ECPs
  • The libgrpp library can be found under https://github.com/aoleynichenko/libgrpp
  • During the installation, the directories $(LIBGRPP_DIR)/lib and $(LIBGRPP_DIR)/include are created.
  • Add -D__LIBGRPP to DFLAGS, -I$(LIBGRPP_DIR)/include to FCFLAGS and -L$(LIBGRPP_DIR)/lib -llibgrpp to LIBS

2y. DeePMD-kit (optional, wider range of interaction potentials)

DeePMD-kit - Deep Potential Molecular Dyanmics. Support for DeePMD-kit can be enabled via the flag -D__DEEPMD.

2z. DFTD4 (optional, dispersion correction)

  • dftd4 - Generally Applicable Atomic-Charge Dependent London Dispersion Correction.
  • For more information see https://github.com/dftd4/dftd4
  • Add -D__DFTD4 to DFLAGS, -ldftd4 -lmstore -lmulticharge -lmctc-lib to LIBS and -I'${DFTD4_DFTD4}/../..' -I'${DFTD4_DFTD4}' -I'${DFTD4_MCTC}' to CFLAGS

3. Compile

3a. ARCH files

The location of compiler and libraries needs to be specified. Examples for several common architectures can be found in arch folder. The names of these files match architecture.version e.g., Linux-x86-64-gfortran.sopt. Alternatively, https://dashboard.cp2k.org provides sample arch files as part of the testing reports (click on the status field, search for 'ARCH-file').

Conventionally, there are six versions:

Acronym Meaning
sdbg OpenMP + debug settings
sopt OpenMP + OMP_NUM_THREADS=1
ssmp OpenMP
pdbg MPI + OpenMP + debug settings
popt MPI + OpenMP + OMP_NUM_THREADS=1
psmp MPI + OpenMP

You'll need to modify one of these files to match your system's settings.

You can now build CP2K using these settings (where -j N allows for a parallel build using N processes):

make -j N ARCH=architecture VERSION=version

e.g.

make -j N ARCH=Linux-x86-64-gfortran VERSION=sopt

as a short-cut, you can build several version of the code at once

make -j N ARCH=Linux-x86-64-gfortran VERSION="sopt popt ssmp psmp"

An executable should appear in the ./exe/ folder.

All compiled files, libraries, executables, etc. of all architectures and versions can be removed with

make distclean

To remove only objects and mod files (i.e., keep exe) for a given ARCH/VERSION use, e.g.,

make ARCH=Linux-x86-64-gfortran VERSION=sopt clean

to remove everything for a given ARCH/VERSION use, e.g.,

make ARCH=Linux-x86-64-gfortran VERSION=sopt realclean

3b. Compilation Flags

The following flags should be present (or not) in the arch file, partially depending on installed libraries (see 2.)

  • -D__parallel builds an MPI parallel CP2K binary (implies the use and thus the availabiltity of the ScaLAPACK/BLACS libraries)
  • -D__LIBINT use LIBINT (needed for HF exchange)
  • -D__LIBXC use LIBXC
  • -D__LIBGRPP use libgrpp (for calculations with ECPs)
  • -D__ELPA use ELPA in place of SYEVD to solve the eigenvalue problem
  • -D__FFTW3 FFTW version 3 is recommended
  • -D__MKL link the MKL library for linear algebra and/or FFT
  • -D__GRID_CORE=X (with X=1..6) specific optimized core routines can be selected. Reasonable defaults are provided but trial-and-error might yield (a small ~10%) speedup.
  • -D__PILAENV_BLOCKSIZE: can be used to specify the blocksize (e.g., -D__PILAENV_BLOCKSIZE=1024), which is a hack to overwrite (if the linker allows this) the PILAENV function provided by ScaLAPACK. This can lead to much improved PDGEMM performance. The optimal value depends on hardware (GPU?) and precise problem. Alternatively, Cray provides an environment variable to this effect (e.g., export LIBSCI_ACC_PILAENV=4000)
  • -D__STATM_RESIDENT or -D__STATM_TOTAL toggles memory usage reporting between resident memory and total memory
  • -D__CRAY_PM_ACCEL_ENERGY or -D__CRAY_PM_ENERGY switch on energy profiling on Cray systems
  • -D__NO_ABORT to avoid calling abort, but STOP instead (useful for coverage testing, and to avoid core dumps on some systems)
  • -D__HDF5 enables hdf5 support. This is a hard dependency for SIRIUS, but can also be used by itself to allow read/write functionalities of QCSchema files in the active space module.

Features useful to deal with legacy systems

  • -D__NO_MPI_THREAD_SUPPORT_CHECK - Workaround for MPI libraries that do not declare they are thread safe (serialized).
  • -D__NO_SOCKETS disables the socket interface in case of troubles compiling on systems that do not support POSIX sockets.
  • -D__HAS_IEEE_EXCEPTIONS disables trapping temporarily for libraries like ScaLAPACK.
  • The Makefile automatically compiles in the path to the data directory via the flag -D__DATA_DIR. If you want to compile in a different path, set the variable DATA_DIR in your arch-file.
  • -D__NO_STATM_ACCESS - Do not try to read from /proc/self/statm to get memory usage information. This is otherwise attempted on several. Linux-based architectures or using with the NAG, gfortran, compilers.
  • -D__CHECK_DIAG Debug option which activates an orthonormality check of the eigenvectors calculated by the selected eigensolver

3c. Building CP2K as a library

You can build CP2K for use as a library by adding libcp2k as an option to your make command, e.g.

make -j N ARCH=Linux-x86-64-gfortran VERSION=sopt libcp2k

This will create libcp2k.a in the relevant subdirectory of ./lib/. You will need to add this subdirectory to the library search path of your compiler (typically via the LD_LIBRARY_PATH environment variable or the -L option to your compiler) and link to the library itself with -lcp2k.

In order to use the functions in the library you will also require the libcp2k.h header file. This can be found in ./src/start/ directory. You should add this directory to the header search path of your compiler (typically via the CPATH environment variable or the -I option to your compiler).

For Fortran users, you will require the module interface file (.mod file) for every MODULE encountered in the source. These are compiler specific and are to be found in the subdirectory of ./obj/ that corresponds to your build, e.g.,

./obj/Linux-x86-64-gfortran/sopt/

In order for your compiler to find these, you will need to indicate their location to the compiler as is done for header files (typically via the CPATH environment variable or the -I option to your compiler).

4. If it doesn't work

If things fail, take a break... go back to 2a (or skip to step 6).

5. Regtesting

If compilation works fine, it is recommended to test the generated binary, to exclude errors in libraries, or miscompilations, etc.

make -j ARCH=... VERSION=... test

should work if you can locally execute CP2K without the need for, e.g., batch submission.

In the other case, you might need to configure the underlying testing script as described more systematically at https://www.cp2k.org/dev:regtesting

6. Talk to us

In any case please tell us your comments, praise, criticism, thanks, etc. see https://www.cp2k.org.

7. Manual

A reference manual of CP2K can be found on the web: https://manual.cp2k.org or can be generated using the cp2k executable, see https://manual.cp2k.org/trunk/generate_manual_howto.html

8. Happy computing

The CP2K team.