write_hadoop_input(input_filename, timing_run_parameters, n_steps, SEED=gen_seed) n_tasks = len(num_rows_list)*len(num_cols_list)*len(num_clusters_list)*len(num_splits_list)*5 # Create a dummy table data file table_data=dict(T=[],M_c=[],X_L=[],X_D=[]) fu.pickle(table_data, table_data_filename) if do_local: xu.run_script_local(input_filename, script_filename, output_filename, table_data_filename) print('Local Engine for automated timing runs has not been completely implemented/tested') elif do_remote: hadoop_engine = HE.HadoopEngine(which_engine_binary=which_engine_binary, output_path=output_path, input_filename=input_filename, table_data_filename=table_data_filename) xu.write_support_files(table_data, hadoop_engine.table_data_filename, dict(command='time_analyze'), hadoop_engine.command_dict_filename) hadoop_engine.send_hadoop_command(n_tasks=n_tasks) was_successful = hadoop_engine.get_hadoop_results() if was_successful: hu.copy_hadoop_output(hadoop_engine.output_path, output_filename) parse_timing.parse_timing_to_csv(output_filename, outfile=parsed_out_file) coeff_list = find_regression_coeff(parsed_out_file, parameter_list) else: print('remote hadoop job NOT successful') else: # print what the command would be hadoop_engine = HE.HadoopEngine(which_engine_binary=which_engine_binary, output_path=output_path, input_filename=input_filename, table_data_filename=table_data_filename)
fu.pickle(table_data, table_data_filename) if do_local: xu.run_script_local(input_filename, script_filename, output_filename, table_data_filename) print( 'Local Engine for automated timing runs has not been completely implemented/tested' ) elif do_remote: hadoop_engine = HE.HadoopEngine( which_engine_binary=which_engine_binary, output_path=output_path, input_filename=input_filename, table_data_filename=table_data_filename) xu.write_support_files(table_data, hadoop_engine.table_data_filename, dict(command='time_analyze'), hadoop_engine.command_dict_filename) hadoop_engine.send_hadoop_command(n_tasks=n_tasks) was_successful = hadoop_engine.get_hadoop_results() if was_successful: hu.copy_hadoop_output(hadoop_engine.output_path, output_filename) parse_timing.parse_timing_to_csv(output_filename, outfile=parsed_out_file) coeff_list = find_regression_coeff(parsed_out_file, parameter_list) else: print('remote hadoop job NOT successful') else: # print what the command would be hadoop_engine = HE.HadoopEngine( which_engine_binary=which_engine_binary,