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--- howto:compile_with_cuda [2019/04/09 09:49] – [Libcusmm] alazzaro
+++ howto:compile_with_cuda [2026/10/14 12:11] (current) – fstein
@@ Line 1: / Line 1: @@
 ====== 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.]]
+This has been moved to [[https://manual.cp2k.org/trunk/technologies/accelerators/index.html]].
-  * 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 XC30 linking against [[http://docs.cray.com/cgi-bin/craydoc.cgi?mode=Show;q=;f=man/xk_libscim/11/cat3/intro_libsci_acc.3s.html|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 more 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 the description [[ https://github.com/cp2k/dbcsr/blob/develop/src/acc/libsmm_acc/libcusmm/README.md | here ]].
-New kernel parameters have to be optimized, which [[howto:libcusmm | this howto]] explains in detail.
-===== Profiling =====
-If you are interested in profiling CP2K with nvprof have a look at [[dev:profiling#nvprof | these remarks ]].