Merge pull request #2311 from devitocodes/patch-delta-compute

compiler: Optimize normalize_reductions_dense

devito/ir/clusters/algorithms.py

@@ -475,11 +475,15 @@ def pull_indexeds(expr, subs, mapper, parent=None):
     return cluster.rebuild(processed)
 
 
-@cluster_pass(mode='dense')
 def normalize_reductions_dense(cluster, sregistry, options):
     """
     Extract the right-hand sides of reduction Eq's in to temporaries.
     """
+    return _normalize_reductions_dense(cluster, sregistry, options, {})
+
+
+@cluster_pass(mode='dense')
+def _normalize_reductions_dense(cluster, sregistry, options, mapper):
     opt_mapify_reduce = options['mapify-reduce']
 
     dims = [d for d in cluster.ispace.itdims
@@ -499,20 +503,25 @@ def normalize_reductions_dense(cluster, sregistry, options):
             # `s += r[x]`
             # This makes it much easier to parallelize the map part regardless
             # of the target backend
+            lhs, rhs = e.args
 
-            if e.lhs.function.is_Array:
+            if lhs.function.is_Array:
                 # Probably a compiler-generated reduction, e.g. via
                 # recursive compilation; it's an Array already, so nothing to do
                 processed.append(e)
+            elif rhs in mapper:
+                # Seen this RHS already, so reuse the Array that was created for it
+                processed.append(e.func(lhs, mapper[rhs].indexify()))
             else:
                 # Here the LHS could be a Symbol or a user-level Function
                 # In the latter case we copy the data into a temporary Array
                 # because the Function might be padded, and reduction operations
                 # require, in general, the data values to be contiguous
                 name = sregistry.make_name()
-                a = Array(name=name, dtype=e.dtype, dimensions=dims)
-                processed.extend([Eq(a.indexify(), e.rhs),
-                                  e.func(e.lhs, a.indexify())])
+                a = mapper[rhs] = Array(name=name, dtype=e.dtype, dimensions=dims)
+
+                processed.extend([Eq(a.indexify(), rhs),
+                                  e.func(lhs, a.indexify())])
         else:
             processed.append(e)