One can install
OpenMP-only version (no MPI dependence)
pip install block2
Hybrid openMP/MPI version (requiring openMPI 4.0.x and
mpi4pybased on the same openMPI library installed)
pip install block2-mpi
Binary format are prepared via
pipfor python 3.7, 3.8, and 3.9 with macOS (no-MPI) or Linux (no-MPI/openMPI). If these binaries have some problems, you can use the
pipto force building from source (for example,
pip install block2 --no-binary block2).
One should only install one of
block2-mpicovers all features in
block2, but its dependence on mpi library can sometimes be difficult to deal with. Some guidance for resolving environment problems can be found in github issue #7.
blas + lapack).
For distributed parallel calculation,
mpi library is required.
cmake (version >= 3.0) can be used to compile C++ part of the code, as follows
mkdir build cd build cmake .. -DUSE_MKL=ON -DBUILD_LIB=ON -DLARGE_BOND=ON -DMPI=ON make -j 10
Which will build the python extension library.
You may need to add the
build directory to your environment
-DUSE_MKL=ON is not given,
lapack are required (with limited support for multi-threading).
-DUSE_MKL64=ON instead of
-DUSE_MKL=ON to enable using matrices with 64-bit integer type.
By default, the C++ templates will be explicitly instantiated in different compilation units, so that parallel compilation is possible.
Alternatively, one can do single-file compilation using
-DEXP_TMPL=NONE, then total compilation time can be
saved by avoiding unnecessary template instantiation, as follows
cmake .. -DUSE_MKL=ON -DBUILD_LIB=ON -DEXP_TMPL=NONE make -j 1
This may take 5 minutes, need 7 to 10 GB memory.
-DMPI=ON will build MPI parallel version. The C++ compiler and MPI library must be matched.
If necessary, environment variables
MPIHOME can be used to explicitly set the path.
mpirun --bind-to none -n ... or
mpirun --bind-to core --map-by ppr:$NPROC:node:pe=$NOMPT ... to execute binary.
To build unit tests and binary executable (instead of python extension), use the following
cmake .. -DUSE_MKL=ON -DBUILD_TEST=ON
TBB (Intel Threading Building Blocks)¶
Adding (optional) option
-DTBB=ON will utilize
This can improve multi-threading performance.
If gnu openMP library
libgomp is not available, one can use intel openMP library.
The following will switch to intel openMP library (incompatible with
cmake .. -DUSE_MKL=ON -DBUILD_LIB=ON -DOMP_LIB=INTEL
The following will use sequential mkl library
cmake .. -DUSE_MKL=ON -DBUILD_LIB=ON -DOMP_LIB=SEQ
The following will use tbb mkl library
cmake .. -DUSE_MKL=ON -DBUILD_LIB=ON -DOMP_LIB=TBB -DTBB=ON
CSR sparse MKL + ThreadingTypes::Operator, if
it is not possible to set both
n_threads_mkl not equal to 1 and
n_threads_op not equal to 1.
In other words, nested openMP is not possible for CSR sparse matrix (generating wrong result/non-convergence).
-DOMP_LIB=SEQ, CSR sparse matrix is okay (non-nested openMP).
-DOMP_LIB=TBB, nested openMP + TBB MKL is okay.
-DTBB=ON can be combined with any
Maximal bond dimension¶
The default maximal allowed bond dimension per symmetry block is
-DSMALL_BOND=ON will change this value to
-DLARGE_BOND=ON will change this value to
The release mode is controlled by CMAKE_BUILD_TYPE.
The following option will use optimization flags such as -O3 (default)
cmake .. -DCMAKE_BUILD_TYPE=Release
The following enables debug flags
cmake .. -DCMAKE_BUILD_TYPE=Debug
An incorrectly installed
mpi4py may produce this error:
undefined symbol: ompi_mpi_logical8
when you execute
from mpi4py import MPI in a
anaconda, please make sure that
mpi4py is linked with the same
mpi library as the one used for compiling
We can create an
anaconda virtual environment (optional):
conda create -n block2 python=3.8 anaconda conda activate block2
Then make sure that a working
mpi library is in the environment, using, for example:
module load openmpi/4.0.4 module load gcc/9.2.0
Then we should install
mpi4py using this
mpi library via
--no-binary option of
python -m pip install --no-binary :all: mpi4py
Sometimes, the above procedure may still give the
undefined symbol: ompi_mpi_logical8 error.
Then it is possible that the
mpi4py is still linked to the
mpich (version 3 or lower) library installed in
If this is the case, one should first
conda uninstall mpich and then
python -m pip -v install --no-binary :all: mpi4py
and if the installation is successful, we can
ldd $(python -c 'from mpi4py import MPI;print(MPI.__file__)')
to check the linkage of the
libmpi.so. Ideally it should points to the
openmpi/4.0.4 library or any other version 4.0 mpi
library. Alternatively, if you do not want to uninstall the
anaconda, you may install
block2 from source using
Supported operating systems and compilers¶
Linux + gcc 9.2.0 + MKL 2019
MacOS 10.15 + Apple clang 12.0 + MKL 2021
MacOS 10.15 + icpc 2021.1 + MKL 2021
Windows 10 + Visual Studio 2019 (MSVC 14.28) + MKL 2021
block2 together with other python extensions¶
Sometimes, when you have to use
block2 together with other python modules (such as
it may have some problem coexisting with each other.
In general, change the import order may help.
import block2 at the very beginning of the script may help.
cmake .. -DUSE_MKL=OFF -DBUILD_LIB=ON -OMP_LIB=SEQ -DLARGE_BOND=ON may help.
Using C++ Interpreter cling¶
block2 is designed as a header-only C++ library, it can be conveniently executed
using C++ interpreter cling
(which can be installed via anaconda)
without any compilation. This can be useful for testing samll changes in the C++ code.
Example C++ code for
cling can be found at