diff options
Diffstat (limited to 'Tools/performance_tests/run_automated.py')
| -rw-r--r-- | Tools/performance_tests/run_automated.py | 74 |
1 files changed, 37 insertions, 37 deletions
diff --git a/Tools/performance_tests/run_automated.py b/Tools/performance_tests/run_automated.py index dca038c6c..8f79750d4 100644 --- a/Tools/performance_tests/run_automated.py +++ b/Tools/performance_tests/run_automated.py @@ -24,7 +24,7 @@ parser.add_argument('--commit', parser.add_argument('--automated', dest='automated', action='store_true', - default=False, + default=False, help='Use to run the automated test list') parser.add_argument('--n_node_list', dest='n_node_list', @@ -34,7 +34,7 @@ parser.add_argument('--start_date', dest='start_date' ) parser.add_argument('--compiler', choices=['gnu', 'intel'], - default='intel', + default='intel', help='which compiler to use') parser.add_argument('--architecture', choices=['cpu', 'knl'], @@ -42,14 +42,14 @@ parser.add_argument('--architecture', help='which architecture to cross-compile for NERSC machines') parser.add_argument('--mode', choices=['run', 'read', 'browse_output_files', 'write_csv'], - default='run', + default='run', help='whether to run perftests or read their perf output. run calls read') args = parser.parse_args() n_node_list_string = args.n_node_list.split(',') n_node_list = [int(i) for i in n_node_list_string] start_date = args.start_date -# Set behavior variables +# Set behavior variables ######################## write_csv = False browse_output_files = False @@ -74,7 +74,7 @@ if args.automated == True: # Each instance of this class contains information for a single test. class test_element(): - def __init__(self, input_file=None, n_node=None, n_mpi_per_node=None, + def __init__(self, input_file=None, n_node=None, n_mpi_per_node=None, n_omp=None, n_cell=None, n_step=None): self.input_file = input_file self.n_node = n_node @@ -99,35 +99,35 @@ test_list_unq = [] n_repeat = 2 # n_node is kept to None and passed in functions as an external argument # That way, several test_element_instance run with the same n_node on the same batch job -test_list_unq.append( test_element(input_file='automated_test_1_uniform_rest_32ppc', - n_mpi_per_node=8, - n_omp=8, - n_cell=[128, 128, 128], +test_list_unq.append( test_element(input_file='automated_test_1_uniform_rest_32ppc', + n_mpi_per_node=8, + n_omp=8, + n_cell=[128, 128, 128], n_step=10) ) -test_list_unq.append( test_element(input_file='automated_test_2_uniform_rest_1ppc', - n_mpi_per_node=8, - n_omp=8, - n_cell=[256, 256, 512], +test_list_unq.append( test_element(input_file='automated_test_2_uniform_rest_1ppc', + n_mpi_per_node=8, + n_omp=8, + n_cell=[256, 256, 512], n_step=10) ) -test_list_unq.append( test_element(input_file='automated_test_3_uniform_drift_4ppc', - n_mpi_per_node=8, - n_omp=8, - n_cell=[128, 128, 128], +test_list_unq.append( test_element(input_file='automated_test_3_uniform_drift_4ppc', + n_mpi_per_node=8, + n_omp=8, + n_cell=[128, 128, 128], n_step=10) ) -test_list_unq.append( test_element(input_file='automated_test_4_labdiags_2ppc', - n_mpi_per_node=8, - n_omp=8, - n_cell=[64, 64, 128], +test_list_unq.append( test_element(input_file='automated_test_4_labdiags_2ppc', + n_mpi_per_node=8, + n_omp=8, + n_cell=[64, 64, 128], n_step=50) ) -test_list_unq.append( test_element(input_file='automated_test_5_loadimbalance', - n_mpi_per_node=8, - n_omp=8, - n_cell=[128, 128, 128], +test_list_unq.append( test_element(input_file='automated_test_5_loadimbalance', + n_mpi_per_node=8, + n_omp=8, + n_cell=[128, 128, 128], n_step=10) ) -test_list_unq.append( test_element(input_file='automated_test_6_output_2ppc', - n_mpi_per_node=8, - n_omp=8, - n_cell=[128, 256, 256], +test_list_unq.append( test_element(input_file='automated_test_6_output_2ppc', + n_mpi_per_node=8, + n_omp=8, + n_cell=[128, 256, 256], n_step=0) ) test_list = [copy.deepcopy(item) for item in test_list_unq for _ in range(n_repeat) ] @@ -160,7 +160,7 @@ if args.mode == 'run': start_date = datetime.datetime.now().strftime("%Y_%m_%d_%H_%M_%S") # Set default options for compilation and execution config_command = '' - config_command += 'module unload darshan;' + config_command += 'module unload darshan;' config_command += 'module load craype-hugepages4M;' if args.architecture == 'knl': if args.compiler == 'intel': @@ -182,7 +182,7 @@ if args.mode == 'run': config_command += 'module load craype-haswell;' # Create main result directory if does not exist if not os.path.exists(res_dir_base): - os.mkdir(res_dir_base) + os.mkdir(res_dir_base) # Recompile if requested # ---------------------- @@ -206,8 +206,8 @@ if args.mode == 'run': store_git_hash(repo_path=amrex_dir , filename=cwd + 'store_git_hashes.txt', name='amrex' ) store_git_hash(repo_path=warpx_dir , filename=cwd + 'store_git_hashes.txt', name='warpx' ) -# This function runs a batch script with -# dependencies to perform the analysis +# This function runs a batch script with +# dependencies to perform the analysis # after all performance tests are done. def process_analysis(): dependencies = '' @@ -246,7 +246,7 @@ def process_analysis(): # Loop over the tests and run all simulations: # One batch job submitted per n_node. Several -# tests run within the same batch job. +# tests run within the same batch job. # -------------------------------------------- if args.mode == 'run': if os.path.exists( 'log_jobids_tmp.txt' ): @@ -310,10 +310,10 @@ for n_node in n_node_list: updated_df = df_base.append(df_newline, ignore_index=True) else: updated_df = df_newline - # Write dataframe to file perf_database_file + # Write dataframe to file perf_database_file # (overwrite if file exists) updated_df.to_hdf(perf_database_file, key='all_data', mode='w') - + # Rename directory with precise date+hour for archive purpose if rename_archive == True: loc_counter = 0 @@ -331,7 +331,7 @@ for n_node in n_node_list: # csv file and copy this file to perf_logs repo # ------------------------------------------------- if write_csv: - # Extract small data from data frame and write them to + # Extract small data from data frame and write them to # First, generate csv files df = pd.read_hdf( perf_database_file ) # One large file |