diff --git a/python/dune/perftool/sumfact/vectorization.py b/python/dune/perftool/sumfact/vectorization.py
index 0b3c068eed7e6fd75d50747b4afd3545f8605941..58df262f7158fa9bd8d855bfe665b10172bc1f04 100644
--- a/python/dune/perftool/sumfact/vectorization.py
+++ b/python/dune/perftool/sumfact/vectorization.py
@@ -30,7 +30,6 @@ import itertools as it
 import loopy as lp
 import numpy as np
 import math
-import sys
 
 
 @generator_factory(item_tags=("vecinfo", "dryrundata"), cache_key_generator=lambda o, n: o)
@@ -161,33 +160,6 @@ def stringify_vectorization_strategy(strategy):
     return result
 
 
-def minimize(iterable, key=lambda x: x):
-    """ A minimization function that is capable of asynchronous
-    evaluation of the iterable if the python version supports it.
-    """
-    version = sys.version_info
-    if version.major == 3 and version.minor > 5:
-        import asyncio
-        from concurrent.futures import ThreadPoolExecutor
-
-        loop = asyncio.get_event_loop()
-        executor = ThreadPoolExecutor(max_workers=1)
-
-        @asyncio.coroutine
-        def key_coro(i):
-            return loop.run_in_executor(executor, key, i)
-
-        tasks = {}
-        for i in iterable:
-            tasks[i] = asyncio.async(key_coro(i), loop=loop)
-
-        loop.run_until_complete(asyncio.gather(*tasks.values()))
-
-        return min(tasks.items(), key=lambda t: t[1].result())[0]
-    else:
-        return min(iterable, key=key)
-
-
 def short_stringify_vectorization_strategy(strategy):
     """ A short string decribing the vectorization strategy. This is used
     in costmodel validation plots to describe what a data point does
@@ -300,7 +272,7 @@ def level1_optimal_vectorization_strategy(sumfacts, width):
         # Print the achieved cost and the target cost on the screen
         set_form_option("vectorization_strategy", "model")
         target = float(get_form_option("vectorization_target"))
-        qp = minimize(optimal_strategies, key=lambda qp: abs(strategy_cost((qp, optimal_strategies[qp])) - target))
+        qp = min(optimal_strategies, key=lambda qp: abs(strategy_cost((qp, optimal_strategies[qp])) - target))
         cost = strategy_cost((qp, optimal_strategies[qp]))
 
         print("The target cost was:   {}".format(target))
@@ -328,7 +300,7 @@ def level1_optimal_vectorization_strategy(sumfacts, width):
         with open("mapping.csv", 'a') as f:
             f.write(" ".join((identifier, str(cost), short_stringify_vectorization_strategy((qp, optimal_strategies[qp])))) + "\n")
     else:
-        qp = minimize(optimal_strategies, key=lambda qp: strategy_cost((qp, optimal_strategies[qp])))
+        qp = min(optimal_strategies, key=lambda qp: strategy_cost((qp, optimal_strategies[qp])))
 
     return qp, optimal_strategies[qp]
 
@@ -344,8 +316,8 @@ def level2_optimal_vectorization_strategy(sumfacts, width, qp):
         key_sumfacts = frozenset(sf for sf in sumfacts if sf.parallel_key == key)
 
         # Minimize over all the opportunities for the subset given by the current key
-        key_strategy = minimize(level2_optimal_vectorization_strategy_generator(key_sumfacts, width, qp),
-                                key=fixedqp_strategy_costfunction(qp))
+        key_strategy = min(level2_optimal_vectorization_strategy_generator(key_sumfacts, width, qp),
+                           key=fixedqp_strategy_costfunction(qp))
         sfdict = add_to_frozendict(sfdict, key_strategy)
 
     return sfdict