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--- howto:libcusmm [2019/04/09 12:45]
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-====== Howto Optimize Cuda Kernels for Libcusmm ======
-**Python version required:** python3.6
-If you are about to autotune parameters for a new GPU (i.e. a GPU for which there are no autotuned parameters yet), please first follow [[https://github.com/cp2k/dbcsr/tree/develop/src/acc/libsmm_acc/libcusmm#adding-support-for-a-new-gpu-card|the instructions for a new GPU]].
-=== Step 1: Go to the libcusmm directory ===
-<code>
-$ cd dbcsr/src/acc/libsmm_acc/libcusmm
-</code>
-=== Step 2: Adapt tune_setup.py to your environment ===
-The ''tune_setup.py'' script generates job files. You have to adapt the script to the environment of your supercomputer and your personal settings.
-<code python>
-...
-  def gen_jobfile(outdir, m, n, k):
-      t = "/tune_%dx%dx%d" % (m, n, k)
-      all_exe_src = [os.path.basename(fn) for fn in glob(outdir + t + "_*_main.cu")]
-      all_exe = sorted([fn.replace("_main.cu", "") for fn in all_exe_src])
-      output = "#!/bin/bash -l\n"
-      output += "#SBATCH --nodes=%d\n" % len(all_exe)
-      output += "#SBATCH --time=0:30:00\n"
-      output += "#SBATCH --account=s238\n"
-      output += "#SBATCH --partition=normal\n"
-      output += "#SBATCH --constraint=gpu\n"
-      output += "\n"
-      output += "source ${MODULESHOME}/init/sh;\n"
-      output += "module load daint-gpu\n"
-      output += "module unload PrgEnv-cray\n"
-      output += "module load PrgEnv-gnu/6.0.3\n"
-      output += "module load cudatoolkit/8.0.54_2.2.8_ga620558-2.1\n"
-      output += "module list\n"
-      output += "export CRAY_CUDA_MPS=1\n"
-      output += "cd $SLURM_SUBMIT_DIR \n"
-      output += "\n"
-      output += "date\n"
-      for exe in all_exe:
-          output += (
-              "srun --nodes=1 --bcast=/tmp/${USER} --ntasks=1 --ntasks-per-node=1 --cpus-per-task=12 make -j 24 %s &\n" %
-              exe)
-   ...
-...
-</code>
-=== Step 3: Run the script tune_setup.py ===
-Specify which GPU you are autotuning for by passing the appropriate ''parameters_GPU.json'' file as an argument with ''-p''.
-In addition, the script takes as arguments the blocksizes you want to add to libcusmm. For example, if the system you want to autotune for contains blocks of size 5 and 8, run:
-<code>
-$ ./tune_setup.py 5 8 -p parameters_P100.json
-Found 23 parameter sets for 5x5x5
-Found 31 parameter sets for 5x5x8
-Found 107 parameter sets for 5x8x5
-Found 171 parameter sets for 5x8x8
-Found 75 parameter sets for 8x5x5
-Found 107 parameter sets for 8x5x8
-Found 248 parameter sets for 8x8x5
-Found 424 parameter sets for 8x8x8
-</code>
-The script will create a directory for each combination of the blocksizes:
-<code>
-$ ls -d tune_*
-tune_5x5x5  tune_5x5x8  tune_5x8x5  tune_5x8x8  tune_8x5x5  tune_8x5x8  tune_8x8x5  tune_8x8x8
-</code>
-Each directory contains a number of files:
-<code>
-$ ls -1 tune_8x8x8/
-Makefile
-tune_8x8x8_exe0_main.cu
-tune_8x8x8_exe0_part0.cu
-tune_8x8x8_exe0_part1.cu
-tune_8x8x8_exe0_part2.cu
-tune_8x8x8_exe0_part3.cu
-tune_8x8x8_exe0_part4.cu
-tune_8x8x8.job
-</code>
-For each possible parameter-set a //launcher// is generated. A launcher is a small snippet of C code, which launches the kernel by using the cuda specific ''%%<<< >>>%%''-notation. It also instantiates the C++ template which contains the actual kernel code.
-In order to parallelize the benchmarking, the launchers are distributed over multiple executables.
-Currently, up to 10'000 launchers are benchmarked by one //executable//. Each executable is linked together from several ''tune_*_part???.o'' and a ''tune_*_main.o''. Each part-files contains up to 100 launchers. This allows to parallelize the compilation over multiple CPU cores.
-=== Step 4: Adapt tune_submit.py to your environment ===
-The script ''tune_submit.py'' was written for the slurm batch system as used e.g. by CRAY supercomputers. If your computer runs a different batch system, you have to adapt ''tune_submit.py'' accordingly.
-=== Step 5: Submit Jobs ===
-Each tune-directory contains a job file.
-Since there might be many tune-directories, the convenience script ''tune_submit.py'' can be used to submit jobs. It will go through all the ''tune_*''-directories and check if its job has already been submitted or run. For this, the script calls ''squeue'' in the background and it searches for ''slurm-*.out'' files.
-When ''tune_submit.py'' is called without arguments, it will just list the jobs that could be submitted:
-<code>
-$ ./tune_submit.py
-          tune_5x5x5: Would submit, run with "doit!"
-          tune_5x5x8: Would submit, run with "doit!"
-          tune_5x8x5: Would submit, run with "doit!"
-          tune_5x8x8: Would submit, run with "doit!"
-          tune_8x5x5: Would submit, run with "doit!"
-          tune_8x5x8: Would submit, run with "doit!"
-          tune_8x8x5: Would submit, run with "doit!"
-          tune_8x8x8: Would submit, run with "doit!"
-Number of jobs submitted: 8
-</code>
-Only when ''tune_submit.py'' is called with ''doit!'' as its first argument, will it actually submit jobs:
-<code>
-$ ./tune_submit.py doit!
-          tune_5x5x5: Submitting
-Submitted batch job 277987
-          tune_5x5x8: Submitting
-Submitted batch job 277988
-          tune_5x8x5: Submitting
-Submitted batch job 277989
-          tune_5x8x8: Submitting
-Submitted batch job 277990
-          tune_8x5x5: Submitting
-Submitted batch job 277991
-          tune_8x5x8: Submitting
-Submitted batch job 277992
-          tune_8x8x5: Submitting
-Submitted batch job 277993
-          tune_8x8x8: Submitting
-Submitted batch job 277994
-Number of jobs submitted: 8
-</code>
-=== Step 6: Collect Results ===
-Run ''tune_collect.py'' to parse all log files and determine the best kernel for each blocksize:
-<code>
-$ ./tune_collect.py
-Reading: tune_5x5x5/tune_5x5x5_exe0.log
-Reading: tune_5x5x8/tune_5x5x8_exe0.log
-Reading: tune_5x8x5/tune_5x8x5_exe0.log
-Reading: tune_5x8x8/tune_5x8x8_exe0.log
-Reading: tune_8x5x5/tune_8x5x5_exe0.log
-Reading: tune_8x5x8/tune_8x5x8_exe0.log
-Reading: tune_8x8x5/tune_8x8x5_exe0.log
-Reading: tune_8x8x8/tune_8x8x8_exe0.log
-Kernel_dnt_tiny(m=5, n=5, k=5, split_thread=32, threads=64, grouping=16, minblocks=1) , # 27.9623 GFlops
-Kernel_dnt_tiny(m=5, n=5, k=8, split_thread=32, threads=96, grouping=16, minblocks=1) , # 37.8978 GFlops
-Kernel_dnt_medium(m=5, n=8, k=5, tile_m=1, tile_n=1, threads=96, grouping=16, minblocks=8) , # 32.9231 GFlops
-Kernel_dnt_tiny(m=5, n=8, k=8, split_thread=32, threads=96, grouping=16, minblocks=1) , # 47.0366 GFlops
-Kernel_dnt_medium(m=8, n=5, k=5, tile_m=1, tile_n=1, threads=96, grouping=16, minblocks=12) , # 33.1999 GFlops
-Kernel_dnt_medium(m=8, n=5, k=8, tile_m=1, tile_n=1, threads=96, grouping=16, minblocks=12) , # 49.3499 GFlops
-Kernel_dnt_tiny(m=8, n=8, k=5, split_thread=32, threads=96, grouping=16, minblocks=1) , # 62.8469 GFlops
-Kernel_dnt_tiny(m=8, n=8, k=8, split_thread=32, threads=128, grouping=16, minblocks=1) , # 90.7763 GFlops
-Wrote parameters.json
-</code>
-The file ''parameters.json'' now contains the newly autotuned parameters.
-=== Step 7: Merge new parameters with original parameter-file ===
-Run ''tune_merge.py'' to merge the new parameters with the original ones:
-<code>
-$ ./tune_merge.py
-Merging parameters.json with parameters_P100.json
-Wrote parameters.new.json
-</code>
-The file ''parameters.new.json'' can now be used as a parameter file. Rename it to ''parameters_GPU.json'', with the appropriate ''GPU''.
-=== Step 8: Contribute parameters to the community ===
-**Contribute new optimal parameters**
-Submit a pull request updating the appropriate ''parameters_GPU.json'' file to the [[https://github.com/cp2k/dbcsr|DBCSR repository]].
-**Contribute autotuning data**
-See [[https://github.com/cp2k/dbcsr-data#contributing|instructions]] in DBCSR's [[https://github.com/cp2k/dbcsr-data|data repository]].

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