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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 ''​$CP2K_ROOT/​src/​dbcsr/​cuda/​libcusmm''​ === 
-=== Step 2: Run the script === 
-The script takes as arguments the blocksizes you want to add to libcusmm. For example if your system contains the blocks of size 5 and 8 type: 
-$ ./ 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 
-The script will create a directory for each combination of the blocksizes: 
-$ ls -d tune_* 
-tune_5x5x5 ​ tune_5x5x8 ​ tune_5x8x5 ​ tune_5x8x8 ​ tune_8x5x5 ​ tune_8x5x8 ​ tune_8x8x5 ​ tune_8x8x8 
-Each directory contains a number of files: 
-$ ls -1 tune_8x8x8/ 
-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 ''​''​ 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 ''​''​ is called without arguments it will just list the jobs that could be submitted: 
-$ ./​ ​ 
-          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 
-Only when ''​''​ is called with ''​doit!''​ as its first argument it will actually submit job: 
-$ ./ 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 
-=== Step 4: Collect Results === 
-Run ''​''​ to parse all log files and to determine the best kernel for each blocksize: 
-$ ./​ 
-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 ​ 
howto/libcusmm.1396016816.txt.gz ยท Last modified: 2014/03/28 14:26 by oschuett