From e8b3fb9ac260c667b6706e952a0be5db0ba7c56d Mon Sep 17 00:00:00 2001
From: Dominic Kempf <dominic.kempf@iwr.uni-heidelberg.de>
Date: Tue, 6 Mar 2018 09:43:46 +0100
Subject: [PATCH] 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 | 2 +-
python/dune/perftool/sumfact/vectorization.py | 40 -------------------
2 files changed, 1 insertion(+), 41 deletions(-)
diff --git a/python/dune/perftool/options.py b/python/dune/perftool/options.py
index 067d6da6..0e59ff24 100644
--- a/python/dune/perftool/options.py
+++ b/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")
diff --git a/python/dune/perftool/sumfact/vectorization.py b/python/dune/perftool/sumfact/vectorization.py
index 97a197c9..4f7abe67 100644
--- a/python/dune/perftool/sumfact/vectorization.py
+++ b/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:"
--
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