From 465f90be29c3a14f3b82fb4b8047a32a953fc87d Mon Sep 17 00:00:00 2001
From: Dominic Kempf <dominic.kempf@iwr.uni-heidelberg.de>
Date: Fri, 7 Apr 2017 18:15:00 +0200
Subject: [PATCH] Stage 3 vertical vectorization
---
python/dune/perftool/sumfact/accumulation.py | 2 +-
python/dune/perftool/sumfact/symbolic.py | 2 +-
python/dune/perftool/sumfact/vectorization.py | 65 +++++++++----------
3 files changed, 32 insertions(+), 37 deletions(-)
diff --git a/python/dune/perftool/sumfact/accumulation.py b/python/dune/perftool/sumfact/accumulation.py
index 4d729613..20ab84ac 100644
--- a/python/dune/perftool/sumfact/accumulation.py
+++ b/python/dune/perftool/sumfact/accumulation.py
@@ -202,7 +202,7 @@ def generate_accumulation_instruction(visitor, accterm, measure, subdomain_id):
vecinames = ()
# TODO: evaluate whether the following line would be okay with vsf.vectorized
if vsf.vec_index(sf) is not None:
- iname = accum_iname((accterm.argument.restriction, restriction), vsf.horizontal_width, "vec")
+ iname = accum_iname((accterm.argument.restriction, restriction), vsf.vector_width, "vec")
vecinames = (iname,)
transform(lp.tag_inames, [(iname, "vec")])
from dune.perftool.tools import maybe_wrap_subscript
diff --git a/python/dune/perftool/sumfact/symbolic.py b/python/dune/perftool/sumfact/symbolic.py
index 1debd5ac..6487ce49 100644
--- a/python/dune/perftool/sumfact/symbolic.py
+++ b/python/dune/perftool/sumfact/symbolic.py
@@ -512,7 +512,7 @@ class VectorizedSumfactKernel(SumfactKernelBase, ImmutableRecord, prim.Variable)
@property
def dof_shape(self):
- return tuple(mat.basis_size for mat in self.matrix_sequence) + (self.horizontal_width,)
+ return tuple(mat.basis_size for mat in self.matrix_sequence) + (self.vector_width,)
@property
def dof_dimtags(self):
diff --git a/python/dune/perftool/sumfact/vectorization.py b/python/dune/perftool/sumfact/vectorization.py
index 229d070c..2c6f4225 100644
--- a/python/dune/perftool/sumfact/vectorization.py
+++ b/python/dune/perftool/sumfact/vectorization.py
@@ -42,41 +42,36 @@ def no_vectorization(sumfacts):
def vertical_vectorization_strategy(sumfact, depth):
- # For sake of simplicity we restrict us to stage 1 so far
- if sumfact.stage == 1:
- # Assert that this is not already sliced
- assert all(mat.slice_size is None for mat in sumfact.matrix_sequence)
-
- # Determine which of the matrices in the kernel should be sliced
- def determine_slice_direction():
- for i, mat in enumerate(sumfact.matrix_sequence):
- if mat.quadrature_size % depth == 0:
- return i
- elif mat.quadrature_size != 1:
- raise PerftoolError("Vertical vectorization is not possible!")
-
- sliced = determine_slice_direction()
-
- kernels = []
- oldtab = sumfact.matrix_sequence[sliced]
- for i in range(depth):
- seq = list(sumfact.matrix_sequence)
- seq[sliced] = oldtab.copy(slice_size=depth,
- slice_index=i)
- kernels.append(sumfact.copy(matrix_sequence=tuple(seq)))
-
- buffer = get_counted_variable("vertical_buffer")
- input = get_counted_variable("vertical_input")
-
- vsf = VectorizedSumfactKernel(kernels=tuple(kernels),
- buffer=buffer,
- input=input,
- vertical_width=depth,
- )
- return _cache_vectorization_info(sumfact, vsf)
- else:
- return _cache_vectorization_info(sumfact, sumfact.copy(buffer=get_counted_variable("buffer"),
- input=get_counted_variable("input")))
+ # Assert that this is not already sliced
+ assert all(mat.slice_size is None for mat in sumfact.matrix_sequence)
+
+ # Determine which of the matrices in the kernel should be sliced
+ def determine_slice_direction():
+ for i, mat in enumerate(sumfact.matrix_sequence):
+ if mat.quadrature_size % depth == 0:
+ return i
+ elif mat.quadrature_size != 1:
+ raise PerftoolError("Vertical vectorization is not possible!")
+
+ sliced = determine_slice_direction()
+
+ kernels = []
+ oldtab = sumfact.matrix_sequence[sliced]
+ for i in range(depth):
+ seq = list(sumfact.matrix_sequence)
+ seq[sliced] = oldtab.copy(slice_size=depth,
+ slice_index=i)
+ kernels.append(sumfact.copy(matrix_sequence=tuple(seq)))
+
+ buffer = get_counted_variable("vertical_buffer")
+ input = get_counted_variable("vertical_input")
+
+ vsf = VectorizedSumfactKernel(kernels=tuple(kernels),
+ buffer=buffer,
+ input=input,
+ vertical_width=depth,
+ )
+ return _cache_vectorization_info(sumfact, vsf)
def horizontal_vectorization_strategy(sumfacts):
--
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