Remove fromlist vectorization strategy

It would be hard to implement on top of the latest feature and contradict
features projected for the near future.

python/dune/perftool/options.py

@@ -78,7 +78,7 @@ class PerftoolFormOptionsArray(ImmutableRecord):
     fastdg = PerftoolOption(default=False, helpstr="Use FastDGGridOperator from PDELab.")
     sumfact = PerftoolOption(default=False, helpstr="Use sumfactorization")
     vectorization_quadloop = PerftoolOption(default=False, helpstr="whether to generate code with explicit vectorization")
-    vectorization_strategy = PerftoolOption(default="none", helpstr="The identifier of the vectorization cost model. Possible values: none|explicit|model|fromlist")
+    vectorization_strategy = PerftoolOption(default="none", helpstr="The identifier of the vectorization cost model. Possible values: none|explicit|model")
     vectorization_horizontal = PerftoolOption(default=None, helpstr="an explicit value for horizontal vectorization read by the 'explicit' strategy")
     vectorization_vertical = PerftoolOption(default=None, helpstr="an explicit value for vertical vectorization read by the 'explicit' strategy")
     vectorization_padding = PerftoolOption(default=None, helpstr="an explicit value for the allowed padding in vectorization")

python/dune/perftool/sumfact/vectorization.py

@@ -62,12 +62,6 @@ def costmodel(sf):
     return sf.operations * vertical_penalty * scalar_penalty
 
 
-@backend(interface="vectorization_strategy", name="fromlist")
-def fromlist_costmodel(sf):
-    # The fromlist strategy needs to reuse the cost model!
-    return costmodel(sf)
-
-
 @backend(interface="vectorization_strategy", name="explicit")
 def explicit_costfunction(sf):
     # Read the explicitly set values for horizontal and vertical vectorization
@@ -181,40 +175,6 @@ def decide_vectorization_strategy():
     width = get_vcl_type_size(dtype_floatingpoint())
     qp, sfdict = level1_optimal_vectorization_strategy(active_sumfacts, width)
 
-
-#   TODO: Check how the 'fromlist' generator fits into the new overall picture
-#
-#     if get_form_option("vectorization_strategy") == "fromlist":
-#         # This is a bit special and does not follow the minimization procedure at all
-#
-#         def _choose_strategy_from_list(stage1_sumfacts):
-#             strategy = 0
-#             for qp in quad_points:
-#                 for strat in fixed_quad_vectorization_opportunity_generator(frozenset(stage1_sumfacts), width, qp):
-#                     if strategy == int(get_form_option("vectorization_list_index")):
-#                         # Output the strategy and its cost into a separate file
-#                         if get_global_context_value("form_type") == "jacobian_apply":
-#                             with open("strategycosts.csv", "a") as f:
-#                                 f.write("{} {}\n".format(strategy, strategy_cost((qp, strat))))
-#                         return qp, strat
-#                     strategy = strategy + 1
-#
-#             raise PerftoolVectorizationError("Specified vectorization list index '{}' was too high!".format(get_form_option("vectorization_list_index")))
-#
-#         s1_sumfacts = frozenset(sf for sf in active_sumfacts if sf.stage == 1)
-#
-#         total = sum(len([s for s in fixed_quad_vectorization_opportunity_generator(frozenset(s1_sumfacts), width, qp)]) for qp in quad_points)
-#         print("'fromlist' vectorization is attempting to pick #{} of {} strategies...".format(int(get_form_option("vectorization_list_index")),
-#                                                                                               total))
-#         qp, sfdict = _choose_strategy_from_list(s1_sumfacts)
-#
-#         keys = frozenset(sf.input_key for sf in active_sumfacts if sf.stage != 1)
-#         for key in keys:
-#             key_sumfacts = frozenset(sf for sf in active_sumfacts if sf.input_key == key)
-#             minimum = min(fixed_quad_vectorization_opportunity_generator(key_sumfacts, width, qp),
-#                           key=fixedqp_strategy_costfunction(qp))
-#             sfdict = add_to_frozendict(sfdict, minimum)
-
     set_quadrature_points(qp)
 
     logger.debug("decide_vectorization_strategy: Decided for the following strategy:"