diff --git a/python/dune/perftool/sumfact/vectorization.py b/python/dune/perftool/sumfact/vectorization.py
index d9ca54c855a9bd304fcc9c7f81ccb26392fa9cff..3635b5b26bd6f6df08e952d435ed52f304695587 100644
--- a/python/dune/perftool/sumfact/vectorization.py
+++ b/python/dune/perftool/sumfact/vectorization.py
@@ -271,8 +271,7 @@ def level1_optimal_vectorization_strategy(sumfacts, width):
         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"), "costmodel-verification", "targetstrat_{}.txt".format(int(float(get_form_option("vectorization_target")))))
+        filename = "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]))))
 
@@ -280,10 +279,9 @@ def level1_optimal_vectorization_strategy(sumfacts, width):
         from dune.testtools.parametertree.parser import parse_ini_file
         inifile = parse_ini_file(get_option("ini_file"))
         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:
+        with open("mapping.csv", 'a') as f:
             f.write(" ".join((identifier, str(cost), short_stringify_vectorization_strategy((qp, optimal_strategies[qp])))) + "\n")
 
     return qp, optimal_strategies[qp]