We have some operations defined on DataFlowGraph purely to work around borrow-checker issues with InstructionData and other data on DataFlowGraph. Part of the problem is that indexing the DFG directly hides the fact that we're only indexing the insts field of the DFG.
This PR makes the insts field of the DFG public, but wraps it in a newtype that only allows indexing. This means that the borrow checker is better able to tell when operations on memory held by the DFG won't conflict, which comes up frequently when mutating ValueLists held by InstructionData.
* Alias analysis: refactor for use by other driver loops.
This PR pulls the core of the alias analysis infrastructure into a
`process_inst()` method that operates on a single instruction, and
allows another compiler pass to apply store-to-load forwarding and
redundant-load elimination interleaved with other work. The existing
behavior remains unchanged; the pass's toplevel loop calls this
extracted method.
This refactor is a prerequisite for using the alias analysis as part of
a refactored egraph-based optimization framework.
* Review feedback: remove unneeded mut.
* Initial forward-edge CFI implementation
Give the user the option to start all basic blocks that are targets
of indirect branches with the BTI instruction introduced by the
Branch Target Identification extension to the Arm instruction set
architecture.
Copyright (c) 2022, Arm Limited.
* Refactor `from_artifacts` to avoid second `make_executable` (#1)
This involves "parsing" twice but this is parsing just the header of an
ELF file so it's not a very intensive operation and should be ok to do
twice.
* Address the code review feedback
Copyright (c) 2022, Arm Limited.
Co-authored-by: Alex Crichton <alex@alexcrichton.com>
This PR adds a basic *alias analysis*, and optimizations that use it.
This is a "mid-end optimization": it operates on CLIF, the
machine-independent IR, before lowering occurs.
The alias analysis (or maybe more properly, a sort of memory-value
analysis) determines when it can prove a particular memory
location is equal to a given SSA value, and when it can, it replaces any
loads of that location.
This subsumes two common optimizations:
* Redundant load elimination: when the same memory address is loaded two
times, and it can be proven that no intervening operations will write
to that memory, then the second load is *redundant* and its result
must be the same as the first. We can use the first load's result and
remove the second load.
* Store-to-load forwarding: when a load can be proven to access exactly
the memory written by a preceding store, we can replace the load's
result with the store's data operand, and remove the load.
Both of these optimizations rely on a "last store" analysis that is a
sort of coloring mechanism, split across disjoint categories of abstract
state. The basic idea is that every memory-accessing operation is put
into one of N disjoint categories; it is disallowed for memory to ever
be accessed by an op in one category and later accessed by an op in
another category. (The frontend must ensure this.)
Then, given this, we scan the code and determine, for each
memory-accessing op, when a single prior instruction is a store to the
same category. This "colors" the instruction: it is, in a sense, a
static name for that version of memory.
This analysis provides an important invariant: if two operations access
memory with the same last-store, then *no other store can alias* in the
time between that last store and these operations. This must-not-alias
property, together with a check that the accessed address is *exactly
the same* (same SSA value and offset), and other attributes of the
access (type, extension mode) are the same, let us prove that the
results are the same.
Given last-store info, we scan the instructions and build a table from
"memory location" key (last store, address, offset, type, extension) to
known SSA value stored in that location. A store inserts a new mapping.
A load may also insert a new mapping, if we didn't already have one.
Then when a load occurs and an entry already exists for its "location",
we can reuse the value. This will be either RLE or St-to-Ld depending on
where the value came from.
Note that this *does* work across basic blocks: the last-store analysis
is a full iterative dataflow pass, and we are careful to check dominance
of a previously-defined value before aliasing to it at a potentially
redundant load. So we will do the right thing if we only have a
"partially redundant" load (loaded already but only in one predecessor
block), but we will also correctly reuse a value if there is a store or
load above a loop and a redundant load of that value within the loop, as
long as no potentially-aliasing stores happen within the loop.
