This shrinks MCInstrDesc (and hence the whole TargetInsts table) because
we can store a 16-bit offset value to access the operands info, instead
of a pointer. This also reduces the number of relocs that need to be
applied when LLVM is compiled as position-independent code.
Differential Revision: https://reviews.llvm.org/D142219
This shrinks MCInstrDesc (and hence the whole TargetInsts table) because
we can store a 16-bit offset value to access the implicit operands,
instead of a pointer. This also reduces the number of relocs that need
to be applied when LLVM is compiled as position-independent code.
Differential Revision: https://reviews.llvm.org/D142218
Combine the implicit uses and defs lists into a single list of uses
followed by defs. Instead of 0-terminating the list, store the number
of uses and defs. This avoids having to scan the whole list to find the
length and removes one pointer from MCInstrDesc (although it does not
get any smaller due to alignment issues).
Remove the old accessor methods getImplicitUses, getNumImplicitUses,
getImplicitDefs and getNumImplicitDefs as all clients are using the new
implicit_uses and implicit_defs.
Differential Revision: https://reviews.llvm.org/D142216
Add the calculation of a score, which will be used during ML training. The
score qualifies the quality of a regalloc policy, and is independent of
what we train (currently, just eviction), or the regalloc algo itself.
We can then use scores to guide training (which happens offline), by
formulating a reward based on score variation - the goal being lowering
scores (currently, that reward is percentage reduction relative to
Greedy's heuristic)
Currently, we compute the score by factoring different instruction
counts (loads, stores, etc) with the machine basic block frequency,
regardless of the instructions' provenance - i.e. they could be due to
the regalloc policy or be introduced previously. This is different from
RAGreedy::reportStats, which accummulates the effects of the allocator
alone. We explored this alternative but found (at least currently) that
the more naive alternative introduced here produces better policies. We
do intend to consolidate the two, however, as we are actively
investigating improvements to our reward function, and will likely want
to re-explore scoring just the effects of the allocator.
In either case, we want to decouple score calculation from allocation
algorighm, as we currently evaluate it after a few more passes after
allocation (also, because score calculation should be reusable
regardless of allocation algorithm).
We intentionally accummulate counts independently because it facilitates
per-block reporting, which we found useful for debugging - for instance,
we can easily report the counts indepdently, and then cross-reference
with perf counter measurements.
Differential Revision: https://reviews.llvm.org/D115195
