Given a constant operand, the MVE and DAGCombine combines could fight,
each redistributing in the opposite order. Add a guard to the MVE
vecreduce distribution to prevent that.
This distributes reductions based on the relative offset of loads, if
one is found from their operands. Given chains of reductions this will
then sort them in ascending load order, which in turn can help simple
prefetches latch on to increasing strides more easily.
Differential Revision: https://reviews.llvm.org/D106569
This adds a combine for adds of reductions, distributing them so that
they occur sequentially to enable better use of accumulating VADDVA
instructions. It combines:
add(X, add(vecreduce(Y), vecreduce(Z))) ->
add(add(X, vecreduce(Y)), vecreduce(Z))
and
add(add(A, reduce(B)), add(C, reduce(D))) ->
add(add(add(A, C), reduce(B)), reduce(D))
These together distribute the add's so that more reductions can be
selected to VADDVA.
Differential Revision: https://reviews.llvm.org/D106532
Under MVE we can use VADDV/VADDVA's to perform integer add reductions,
so it can be beneficial to use more reductions than summing subvectors
and reducing once. Especially for VMLAV/VMLAVA the mul can be
incorporated into the reduction, producing less instructions.
Some of the test cases currently get larger due to extra integer adds,
but will be improved in a followup patch.
Differential Revision: https://reviews.llvm.org/D106531
These are generated from SLP vectorization, for example
https://godbolt.org/z/ebxdPh1Kz. These backend tests
show cases that we can produce better code for.
David Green