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====== How to Compile CP2K with CUDA Support ======
Currently three major operations in CP2K support CUDA-acceleration:

  * Anything that uses ''dbcsr_multiply'', i.e. sparse matrix multiplication, when compiled with ''%%-D__ACC -D__DBCSR_ACC%%''. This benefits in particular the [[doi>10.1021/ct200897x| linear scaling DFT]] code. See also [[http://dbcsr.cp2k.org | the DBCSR project.]]
  * FFTs, when compiled with ''%%-D__PW_CUDA%%''.
  * If linked against an accelerated scalapack/blas library (in particular pdgemm/pdsyrk/dgemm) that executes these calls on the GPU. The impact of this is most visible for MP2 and RPA calculations. On the hybrid Cray XC50 linking against cray-libsci_acc makes this happen.

To enable all CUDA acceleration options the following lines have to be added to the ARCH-file:
<code>
NVCC    = /path_to_cuda/bin/nvcc
DFLAGS += -D__ACC -D__DBCSR_ACC -D__PW_CUDA
LIBS   += -lcudart -lcublas -lcufft -lrt
</code>

See [[https://github.com/cp2k/cp2k/blob/master/INSTALL.md#2j-cuda-optional-improved-performance-on-gpu-systems | here]] for details.
As a prerequisite the [[https://developer.nvidia.com/cuda-toolkit |Nvidia CUDA Toolkit ]] has to be installed.


===== Libcusmm =====
The acceleration of DBCSR is performed by libcusmm. This library provides a number of kernels. Each of these kernels can multiply blocks of specific blocksizes. The blocksizes of a simulation are determined by the employed basis-set. As of DBCSR 1.1, by default libcusmm is able to generate any kernel for {m,n,k}≤80, see [[ https://github.com/cp2k/dbcsr/blob/develop/src/acc/libsmm_acc/libcusmm/README.md | here]] for more details. The //DBCSR Statistics// are printed at the end of every CP2K-run, example

<code>
 -------------------------------------------------------------------------------
 -                                                                             -
 -                                DBCSR STATISTICS                             -
 -                                                                             -
 -------------------------------------------------------------------------------
 COUNTER                                      CPU                  ACC      ACC%
 number of processed stacks                   160                   64      28.6
 matmuls inhomo. stacks                     11880                    0       0.0
 matmuls total                             132360                53530      28.8
 flops  13 x   13 x   13                        0             33218640     100.0
 flops  24 x   13 x   13                        0             55177824     100.0
...
 flops total                           1452705420            657928368      31.2
 marketing flops                       2048000000
 -------------------------------------------------------------------------------
</code>

More supported GPUs can be added, please refer to [[https://github.com/cp2k/dbcsr/blob/develop/src/acc/libsmm_acc/libcusmm/tune.md | this howto]].

===== Profiling =====
If you are interested in profiling CP2K with nvprof have a look at [[dev:profiling#nvprof | these remarks]].
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