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# Examples
- [`dbcsr_example_1`](dbcsr_example_1.F): how to create a dbcsr matrix (fortran)
- [`dbcsr_example_2`](dbcsr_example_2.F): how to set a dbcsr matrix (fortran)
- `dbcsr_example_3`: how to multiply two dbcsr matrices ([fortran](dbcsr_example_3.F) and [cpp](dbcsr_example_3.cpp))
- [`dbcsr_tensor_example_1`](dbcsr_tensor_example_1.F): how to create a dbcsr matrix (fortran)
- the example can be run with different parameters, controlling block size, sparsity, verbosity and more
- [`dbcsr_tensor_example_2`](dbcsr_tensor_example_2.cpp): tensor contraction example (cpp)
- tensor1 x tensor2 = tensor3, (13|2)x(54|21)=(3|45)
## Build
Compile the DBCSR library, using `-DUSE_MPI=ON -DWITH_EXAMPLES=ON`.
The examples require MPI. Furthermore, if you are using threading, MPI_THREAD_FUNNELED mode is required.
## Run
You can run the examples, for instance from the `build` directory, as follows:
```bash
srun -N 1 --ntasks-per-core 2 --ntasks-per-node 12 --cpus-per-task 2 ./examples/dbcsr_example_1
```
### Run tensor examples
How to run (this example and DBCSR for tensors in general):
* best performance is obtained by running with mpi and one openmp thread per rank.
* ideally number of mpi ranks should be composed of small prime factors (e.g. powers of 2).
* for sparse data & heterogeneous block sizes, DBCSR should be run on CPUs with libxsmm backend.
* for dense data best performance is obtained by choosing homogeneous block sizes of 64 and by compiling with GPU support.
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