DBCSR: Distributed Block Compressed Sparse Row matrix library
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DBCSR: Distributed Block Compressed Sparse Row matrix library

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DBCSR is a library designed to efficiently perform sparse matrix matrix multiplication, among other operations. It is MPI and OpenMP parallel and can exploit GPUs via CUDA.

DBCSR logo


You absolutely need:

  • GNU make
  • a Fortran compiler which supports at least Fortran 2003 (respectively 2008+TS when using the C-bindings)
  • a LAPACK implementation (reference, OpenBLAS-bundled and MKL have been tested)
  • a BLAS implementation (reference, OpenBLAS-bundled and MKL have been tested)
  • a Python version installed (2.7 or 3.6+ have been tested)


  • libxsmm (1.8.2+ with make-only, 1.10+ with cmake) for Small Matrix Multiplication acceleration
  • CMake (3.10+)

To build with CUDA support you further need:

  • CUDA Toolkit
  • a C++ compiler which supports at least C++11 standard

We test against GNU and Intel compilers on Linux systems.

Getting started

Download either a release tarball or clone the latest version from Git using:

git clone --recursive https://github.com/cp2k/dbcsr.git


make help

to list all possible targets.

Update the provided Makefile.inc to fit your needs (read the documentation inside the file for further explanations) and then run

make <target>

Some examples on how to use the library (which is the only current documentation) are available under the examples directory (see readme).

C/C++ Interface

You can compile with

make CINT=1

to generate the C interface. Make sure your Fortran compiler supports F2008 standard (including the TS) by updating the flag in the Makefile.inc.


Building with CMake is also supported:

mkdir build
cd build
cmake ..

The configuration flags are (default first):


Contributing to DBCSR

Your contribution to the project is welcome! Please see Contributing.md and this wiki page. For any help, please notify the other developers.