From 2c9cdfc6fc19bfb688030af871f806425550b68e Mon Sep 17 00:00:00 2001
From: Dominic Kempf <dominic.kempf@iwr.uni-heidelberg.de>
Date: Mon, 3 Apr 2017 14:31:14 +0200
Subject: [PATCH] First improvement of vectorization strategy
---
python/dune/perftool/sumfact/symbolic.py | 31 +++++++++----
python/dune/perftool/sumfact/vectorization.py | 46 +++++++++----------
2 files changed, 44 insertions(+), 33 deletions(-)
diff --git a/python/dune/perftool/sumfact/symbolic.py b/python/dune/perftool/sumfact/symbolic.py
index e0f1c658..f44cecc3 100644
--- a/python/dune/perftool/sumfact/symbolic.py
+++ b/python/dune/perftool/sumfact/symbolic.py
@@ -130,12 +130,35 @@ class SumfactKernel(ImmutableRecord, prim.Variable):
mapper_method = "map_sumfact_kernel"
+ #
+ # Some cache key definitions
+ # Watch out for the documentation to see which key is used unter what circumstances
+ #
+
+ @property
+ def cache_key(self):
+ """ The cache key that can be used in generation magic
+ Any two sum factorization kernels having the same cache_key
+ are realized simulatenously!
+ """
+ return (self.matrix_sequence, self.restriction, self.stage, self.buffer)
+
+ @property
+ def input_key(self):
+ """ A cache key for the input coefficients
+ Any two sum factorization kernels having the same input_key
+ work on the same input coefficient (and are suitable for simultaneous
+ treatment because of that)
+ """
+ return (self.restriction, self.stage, self.coeff_func, self.element, self.component, self.accumvar)
+
#
# Some convenience methods to extract information about the sum factorization kernel
#
@property
def length(self):
+ """ The number of matrices to apply """
return len(self.matrix_sequence)
@property
@@ -146,14 +169,6 @@ class SumfactKernel(ImmutableRecord, prim.Variable):
def transposed(self):
return next(iter(self.matrix_sequence)).transpose
- @property
- def cache_key(self):
- """ The cache key that can be used in generation magic,
- Any two sum factorization kernels having the same cache_key
- are realized simulatneously!
- """
- return hash((self.matrix_sequence, self.restriction, self.stage, self.buffer))
-
@property
def vec_index(self):
""" A tuple with the vector index
diff --git a/python/dune/perftool/sumfact/vectorization.py b/python/dune/perftool/sumfact/vectorization.py
index 8ca28d17..ea058464 100644
--- a/python/dune/perftool/sumfact/vectorization.py
+++ b/python/dune/perftool/sumfact/vectorization.py
@@ -39,13 +39,12 @@ def no_vectorization(sumfacts):
input=get_counted_variable("input")))
-def decide_stage_vectorization_strategy(sumfacts, stage, restriction):
- stage_sumfacts = frozenset([sf for sf in sumfacts if sf.stage == stage and sf.restriction == restriction])
- if len(stage_sumfacts) in (3, 4):
+def horizontal_vectorization_strategy(sumfacts):
+ if len(sumfacts) in (3, 4):
# Map the sum factorization to their position in the joint kernel
position_mapping = {}
available = set(range(4))
- for sf in stage_sumfacts:
+ for sf in sumfacts:
if sf.preferred_position is not None:
# This asserts that no two kernels want to take the same position
# Later on, more complicated stuff might be necessary here.
@@ -54,7 +53,7 @@ def decide_stage_vectorization_strategy(sumfacts, stage, restriction):
position_mapping[sf] = sf.preferred_position
# Choose a position for those that have no preferred one!
- for sumf in stage_sumfacts:
+ for sumf in sumfacts:
if sumf.preferred_position is None:
position_mapping[sumf] = available.pop()
@@ -64,37 +63,37 @@ def decide_stage_vectorization_strategy(sumfacts, stage, restriction):
# Collect the large matrices!
large_matrix_sequence = []
- for i in range(len(next(iter(stage_sumfacts)).matrix_sequence)):
+ for i in range(len(next(iter(sumfacts)).matrix_sequence)):
# Assert that the matrices of all sum factorizations have the same size
- assert len(set(tuple(sf.matrix_sequence[i].rows for sf in stage_sumfacts))) == 1
- assert len(set(tuple(sf.matrix_sequence[i].cols for sf in stage_sumfacts))) == 1
+ assert len(set(tuple(sf.matrix_sequence[i].rows for sf in sumfacts))) == 1
+ assert len(set(tuple(sf.matrix_sequence[i].cols for sf in sumfacts))) == 1
# Collect the derivative information
derivative = [False] * 4
- for sf in stage_sumfacts:
+ for sf in sumfacts:
derivative[position_mapping[sf]] = sf.matrix_sequence[i].derivative
- large = BasisTabulationMatrixArray(rows=next(iter(stage_sumfacts)).matrix_sequence[i].rows,
- cols=next(iter(stage_sumfacts)).matrix_sequence[i].cols,
- transpose=next(iter(stage_sumfacts)).matrix_sequence[i].transpose,
+ large = BasisTabulationMatrixArray(rows=next(iter(sumfacts)).matrix_sequence[i].rows,
+ cols=next(iter(sumfacts)).matrix_sequence[i].cols,
+ transpose=next(iter(sumfacts)).matrix_sequence[i].transpose,
derivative=tuple(derivative),
- face=next(iter(stage_sumfacts)).matrix_sequence[i].face,
+ face=next(iter(sumfacts)).matrix_sequence[i].face,
)
large_matrix_sequence.append(large)
- for sumf in stage_sumfacts:
+ for sumf in sumfacts:
_cache_vectorization_info(sumf,
sumf.copy(matrix_sequence=tuple(large_matrix_sequence),
buffer=buf,
input=inp,
index=position_mapping[sumf],
padding=frozenset(available),
- insn_dep=frozenset().union(sf.insn_dep for sf in stage_sumfacts),
+ insn_dep=frozenset().union(sf.insn_dep for sf in sumfacts),
)
)
else:
# Disable vectorization strategy
- no_vectorization(stage_sumfacts)
+ no_vectorization(sumfacts)
def decide_vectorization_strategy():
@@ -108,12 +107,9 @@ def decide_vectorization_strategy():
if not get_option("vectorize_grads"):
no_vectorization(sumfacts)
else:
- res = (Restriction.NONE, Restriction.POSITIVE, Restriction.NEGATIVE)
- # Stage 1 kernels
- for restriction in res:
- decide_stage_vectorization_strategy(sumfacts, 1, restriction)
-
- # Stage 3 kernels
- import itertools as it
- for restriction in it.product(res, res):
- decide_stage_vectorization_strategy(sumfacts, 3, restriction)
+ # Currently we base our idea here on the fact that we only group sum
+ # factorization kernels with the same input.
+ inputkeys = set(sf.input_key for sf in sumfacts)
+ for inputkey in inputkeys:
+ sumfact_filter = [sf for sf in sumfacts if sf.input_key == inputkey]
+ horizontal_vectorization_strategy(sumfact_filter)
--
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