[all-commits] [llvm/llvm-project] 26e375: Recommit "[RISCV] Add a pass to remove ADDI by rea...

[Craig Topper via All-commits]

  Branch: refs/heads/main
  Home:   https://github.com/llvm/llvm-project
  Commit: 26e375046dbde27957548d5e9a9885c3eff29019
      https://github.com/llvm/llvm-project/commit/26e375046dbde27957548d5e9a9885c3eff29019
  Author: Craig Topper <craig.topper at sifive.com>
  Date:   2025-02-19 (Wed, 19 Feb 2025)

  Changed paths:
    M llvm/lib/Target/RISCV/CMakeLists.txt
    M llvm/lib/Target/RISCV/RISCV.h
    A llvm/lib/Target/RISCV/RISCVFoldMemOffset.cpp
    M llvm/lib/Target/RISCV/RISCVTargetMachine.cpp
    M llvm/test/CodeGen/RISCV/O3-pipeline.ll
    A llvm/test/CodeGen/RISCV/fold-mem-offset.ll
    M llvm/test/CodeGen/RISCV/split-offsets.ll
    M llvm/test/CodeGen/RISCV/xtheadmemidx.ll

  Log Message:
  -----------
  Recommit "[RISCV] Add a pass to remove ADDI by reassociating to fold into load/store address. (#127151)"

Tests have been re-generated with recent scheduler changes.

Original message:

SelectionDAG will not reassociate adds to the end of a chain if
there are multiple users of later additions. This prevents isel
from folding the immediate into a load/store address.

One easy way to see this is accessing an array in a struct with
two different indices. An ADDI will be used to get to the start
of the array then 2 different SHXADD instructions will be used to
add the scaled indices. Finally the SHXADD will be used by different
load instructions. We can remove the ADDI by folding the offset into
each load.

This patch adds a new pass that analyzes how an ADDI constant
propagates through address arithmetic. If the arithmetic is only
used by a load/store and the offset is small enough, we can adjust
the load/store offset and remove the ADDI.

This pass is placed before MachineCSE to allow cleanups if some
instructions become common after removing offsets from their inputs.

This pass gives ~3% improvement on dynamic instruction count on
541.leela_r and 544.nab_r from SPEC2017 for the train data set. There's
a ~1% improvement on 557.xz_r.



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