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howto:libcusmm [2014/03/28 14:26]
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-====== Howto Optimize Cuda Kernels for Libcusmm ====== 
-=== Step 1: Go to the directory ''$CP2K_ROOT/src/dbcsr/cuda/libcusmm'' === 
-=== Step 2: Run the script tune.py === 
-The script takes as arguments the blocksizes you want to add to libcusmm. For example if your system contains the blocks of size 5 and 8 type: 
-<code> 
-$ ./tune.py 5 8 
-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 snipped of C code, which launches the kernel using the cuda specifica ''<<< >>>''-notation . It also instantiates the C++ template which contains the actual kernel code. 
- 
-In order to parallelize the compilation and the benchmarking the launchers are distributed over several files. 
-Currently, up to 10000 launchers are compiled into one //executable//. Each executable is linked together from several //parts// and a ''tune_*_main.o'' . Each parts contains up to 100 launchers and is compiled into a separate object file ''tune_*_part???.o''. 
- 
-=== Step 3: Submit Jobs === 
-Each tune-directory contains a job file. 
-Since, there might be many tune-directories the convince script ''submit.py'' can be used. It will go through all the ''tune_*''-directories and check if it has already been submited or run. For this the script calls ''squeue'' in the background and it searches for ''slurm-*.out'' files. 
- 
-When ''submit.py'' is called without arguments it will just list the jobs that could be submitted: 
-<code> 
-$ ./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 ''submit.py'' is called with ''doit!'' as its first argument it will actually submit job: 
-<code> 
-$ ./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 4: Collect Results === 
-Run ''collect.py'' to parse all log files and to determine the best kernel for each blocksize: 
-<code> 
-$ ./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  
-</code> 
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