Add cuFFTmp example on JUWELS Booster

DESCRIPTION.md

@@ -32,7 +32,7 @@ You can also use JUBE to download and build GROMACS: `jube run benchmark/jube/ju
 
 If you use JUBE, you need to adjust the CMake and module parameters in `jube_gromacs_build.xml` for your platform.
 
-You may choose optimized FFT and BLAS libraries in accordance with the general rules for the exchange and usage of libraries. In particular the STMV benchmark requires a build with multi-GPU PME to scale (c.f. [Using CuFFTMp](https://manual.gromacs.org/current/install-guide/index.html#using-cufftmp) in the GROMACS manual.)
+You may choose optimized FFT and BLAS libraries in accordance with the general rules for the exchange and usage of libraries. In particular the STMV benchmark requires a build with multi-GPU PME to scale (c.f. [Using CuFFTMp](https://manual.gromacs.org/current/install-guide/index.html#using-cufftmp) in the GROMACS manual, or sketched quickly below.)
 
 ### Modifications
 
@@ -41,6 +41,20 @@ exploits the architecture, the patch must be submitted with the results and be p
 
 You may also use a later released version of GROMACS.
 
+### Example Compilation with Distributed FFT
+
+As GROMACS is a widely used application, many compile optimization are available. As an example, instructions are sketched here for using a distributed FFT library, cuFFTmp, for the STMV sub-benchmark. As this is a highly platform-specific optimization, it is not included automatically in the JUBE script, but hints are given at the according places in the JUBE file.
+
+Taking JUWELS Booster as an example, cuFFTmp needs to be available in the environment and enabled through the proper compiler switches.
+
+```bash
+ml NVHPC OpenMPI NCCL CMake Python-bundle-PyPI
+export LD_LIBRARY_PATH=$EBROOTNVHPC/Linux_x86_64/2023/comm_libs/12.2/nvshmem_cufftmp_compat/lib:$EBROOTNVHPC/Linux_x86_64/2023/math_libs/12.2/lib64:$LD_LIBRARY_PATH
+cmake ../ -DGMX_OPENMP=ON -DGMX_MPI=ON -DGMX_BUILD_OWN_FFTW=ON -DGMX_GPU=CUDA  -DGMX_CUDA_TARGET_SM=80 -DCMAKE_BUILD_TYPE=Release -DGMX_DOUBLE=off -DGMX_USE_CUFFTMP=ON -DcuFFTMp_ROOT=$EBROOTNVHPC/Linux_x86_64/2023/math_libs/12.2
+```
+
+(The environment variables `$EBROOTNVHPC` are set through the `NVHPC` environment module and point to the NVIDIA HPC SDK.)
+
 
 ## Execution
 
@@ -95,6 +109,19 @@ mpiexec -n 22 gmx_mpi mdrun -s ion_channel.tpr -maxh 0.50 -resetstep 60000 -noco
 mpiexec -n 8 gmx_mpi mdrun -s stmv.28M.tpr -maxh 2.90 -resetstep 10000 -noconfout -nsteps 170000 -g logfile -ntomp $threadspertask -tunepme no -pme gpu -npme 1 -bonded gpu -nstlist 400
 ```
 
+##### Example Execution with Distributed FFT Library
+
+Picking up the previous example of using cuFFTmp as a highly-optimized library with platform-specific tuning, an example execution of the case looks like the following on JUWELS Booster:
+
+```bash
+ml NVHPC OpenMPI NCCL CMake Python-bundle-PyPI
+export LD_LIBRARY_PATH=$EBROOTNVHPC/Linux_x86_64/2023/comm_libs/12.2/nvshmem_cufftmp_compat/lib:$EBROOTNVHPC/Linux_x86_64/2023/math_libs/12.2/lib64:$LD_LIBRARY_PATH
+export GMX_ENABLE_DIRECT_GPU_COMM=ON
+export GMX_GPU_PME_DECOMPOSITION=1
+
+srun -N 128 -n 512 -c 6 --time 00:30:00 --gres=gpu:4 --threads-per-core=2 gromacs/build/bin/gmx_mpi mdrun -s GROMACS_TestCaseC/stmv.28M.tpr -maxh 0.5 -resetstep 10000 -noconfout -nsteps 170000 -g logfile -ntomp 6 -tunepme no -pme gpu -npme 128 -bonded gpu -nstlist 400 -cpt -1 -pin on 
+```
+
 #### JUBE
 
 Using JUBE you start the benchmarks with