diff --git a/mlair/run_modules/pre_processing.py b/mlair/run_modules/pre_processing.py
index 8443b10d4d16b71819b795c0579b4d61cb739b70..0e416acbca4d66d5844e1179c7653ac5a9934f28 100644
--- a/mlair/run_modules/pre_processing.py
+++ b/mlair/run_modules/pre_processing.py
@@ -114,17 +114,17 @@ class PreProcessing(RunEnvironment):
         +------------+-------------------------------------------+---------------+---------------+---------------+---------+-------+--------+
 
         """
-        meta_data = ['station_name', 'station_lon', 'station_lat', 'station_alt']
+        meta_cols = ['station_name', 'station_lon', 'station_lat', 'station_alt']
         meta_round = ["station_lon", "station_lat", "station_alt"]
         precision = 4
         path = os.path.join(self.data_store.get("experiment_path"), "latex_report")
         path_config.check_path_and_create(path)
         names_of_set = ["train", "val", "test"]
-        df = self.create_info_df(meta_data, meta_round, names_of_set, precision)
+        df = self.create_info_df(meta_cols, meta_round, names_of_set, precision)
         column_format = tables.create_column_format_for_tex(df)
         tables.save_to_tex(path=path, filename="station_sample_size.tex", column_format=column_format, df=df)
         tables.save_to_md(path=path, filename="station_sample_size.md", df=df)
-        df_nometa = df.drop(meta_data, axis=1)
+        df_nometa = df.drop(meta_cols, axis=1)
         column_format = tables.create_column_format_for_tex(df)
         tables.save_to_tex(path=path, filename="station_sample_size_short.tex", column_format=column_format,
                            df=df_nometa)
@@ -150,15 +150,35 @@ class PreProcessing(RunEnvironment):
         df_descr = df_descr[df_descr_colnames]
         return df_descr
 
-    def create_info_df(self, meta_data, meta_round, names_of_set, precision):
-        df = pd.DataFrame(columns=meta_data + names_of_set)
+    def create_info_df(self, meta_cols, meta_round, names_of_set, precision):
+        use_multiprocessing = self.data_store.get("use_multiprocessing")
+        max_process = self.data_store.get("max_number_multiprocessing")
+        df = pd.DataFrame(columns=meta_cols + names_of_set)
         for set_name in names_of_set:
             data = self.data_store.get("data_collection", set_name)
-            for station in data:
-                station_name = str(station.id_class)
-                df.loc[station_name, set_name] = station.get_Y()[0].shape[0]
-                if df.loc[station_name, meta_data].isnull().any():
-                    df.loc[station_name, meta_data] = station.id_class.meta.loc[meta_data].values.flatten()
+            n_process = min([psutil.cpu_count(logical=False), len(data), max_process])  # use only physical cpus
+            if n_process > 1 and use_multiprocessing is True:  # parallel solution
+                logging.info(f"use parallel create_info_df ({set_name})")
+                pool = multiprocessing.Pool(n_process)
+                logging.info(f"running {getattr(pool, '_processes')} processes in parallel")
+                output = [pool.apply_async(f_proc_create_info_df, args=(station, meta_cols)) for station in data]
+                for i, p in enumerate(output):
+                    res = p.get()
+                    station_name, shape, meta = res["station_name"], res["Y_shape"], res["meta"]
+                    df.loc[station_name, set_name] = shape
+                    if df.loc[station_name, meta_cols].isnull().any():
+                        df.loc[station_name, meta_cols] = meta
+                    logging.info(f"...finished: {station_name} ({int((i + 1.) / len(output) * 100)}%)")
+                pool.close()
+                pool.join()
+            else:  # serial solution
+                logging.info(f"use serial create_info_df ({set_name})")
+                for station in data:
+                    res = f_proc_create_info_df(station, meta_cols)
+                    station_name, shape, meta = res["station_name"], res["Y_shape"], res["meta"]
+                    df.loc[station_name, set_name] = shape
+                    if df.loc[station_name, meta_cols].isnull().any():
+                        df.loc[station_name, meta_cols] = meta
             df.loc["# Samples", set_name] = df.loc[:, set_name].sum()
             assert len(data) == df.loc[:, set_name].count() - 1
             df.loc["# Stations", set_name] = len(data)
@@ -380,6 +400,13 @@ def f_proc(data_handler, station, name_affix, store, return_strategy="", tmp_pat
         return _tmp_file, station
 
 
+def f_proc_create_info_df(data, meta_cols):
+        station_name = str(data.id_class)
+        res = {"station_name": station_name, "Y_shape": data.get_Y()[0].shape[0],
+               "meta": data.id_class.meta.loc[meta_cols].values.flatten()}
+        return res
+
+
 def f_inspect_error(formatted):
     for i in range(len(formatted) - 1, -1, -1):
         if "mlair/mlair" not in formatted[i]: