A bit more verbosity

python/dune/perftool/sumfact/vectorization.py

@@ -239,16 +239,29 @@ def level1_optimal_vectorization_strategy(sumfacts, width):
 
     # If we are using the 'target' strategy, we might want to log some information.
     if get_form_option("vectorization_strategy") == "target":
+        # Print the achieved cost and the target cost on the screen
         set_form_option("vectorization_strategy", "model")
         cost = strategy_cost((qp, optimal_strategies[qp]))
         print("The target cost was:   {}".format(get_form_option("vectorization_target")))
         print("The achieved cost was: {}".format(cost))
+        set_form_option("vectorization_strategy", "target")
+        print("The score in 'target' logic was: {}".format(strategy_cost((qp, optimal_strategies[qp]))))
+
+        # Print the employed vectorization strategy into a file
         from os.path import join
-        filename = join(get_option("project_basedir"), "targetstrat_{}".format(int(float(get_form_option("vectorization_target")))))
+        filename = join(get_option("project_basedir"), "costmodel-verification", "targetstrat_{}.txt".format(int(float(get_form_option("vectorization_target")))))
         with open(filename, 'w') as f:
             f.write("\n".join(stringify_vectorization_strategy((qp, optimal_strategies[qp]))))
-        set_form_option("vectorization_strategy", "target")
-        print("The score in 'target' logic was: {}".format(strategy_cost((qp, optimal_strategies[qp]))))
+
+        # Write an entry into a csvfile which connects the given measuring identifier with a cost
+        from dune.testtools.parametertree.parser import parse_ini_file
+        inifile = parse_ini_file(get_option("inifile"))
+        identifier = inifile["identifier"]
+        filename = join(get_option("project_basedir"), "costmodel-verification", "mapping.csv")
+
+        #TODO: Depending on the number of samples, we might need a file lock here.
+        with open(filename, 'a') as f:
+            f.write("{} {}".format(identifier, cost))
 
     return qp, optimal_strategies[qp]