This is mostly refactoring, along with adding an instruction count
within the subtrees and ensuring we only look at data edges.
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if the vector is not empty. This will ensure that calls to these functions
will reference elements in the vector.
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Allow schedulers to order DAG edges by critical path. This makes
DFS-based heuristics more stable and effective.
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This is a helper class for the AttributeSetImpl class. It holds a set of
attributes that apply to a single element: function, return type, or
parameter.
These are uniqued.
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SSPStrong applies a heuristic to insert stack protectors in these situations:
* A Protector is required for functions which contain an array, regardless of
type or length.
* A Protector is required for functions which contain a structure/union which
contains an array, regardless of type or length. Note, there is no limit to
the depth of nesting.
* A protector is required when the address of a local variable (i.e., stack
based variable) is exposed. (E.g., such as through a local whose address is
taken as part of the RHS of an assignment or a local whose address is taken as
part of a function argument.)
This patch implements the SSPString attribute to be equivalent to
SSPRequired. This will change in a subsequent patch.
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Collections of attributes are handled via the AttributeSet class now. This
finally frees us up to make significant changes to how attributes are structured.
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ErrorOr<void> represents an operation that returns nothing, but can still fail.
It should be used in cases where you need the aditional user data that ErrorOr
provides over error_code.
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Use the AttributeSet when we're talking about more than one attribute. Add a
function that adds a single attribute. No functionality change intended.
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Add the x32 environment kind to the triple, and separate the concept of
pointer size and callee save stack slot size, since they're not equal
on x32.
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This is a follow-up to r171845, which fixes the same issue in the Support code.
Only targets with >256 relocations (principally AArch64) should be affected.
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generic function calls and intrinsics. This is somewhat overlapping with
an existing intrinsic cost method, but that one seems targetted at
vector intrinsics. I'll merge them or separate their names and use cases
in a separate commit.
This sinks the test of 'callIsSmall' down into TTI where targets can
control it. The whole thing feels very hack-ish to me though. I've left
a FIXME comment about the fundamental design problem this presents. It
isn't yet clear to me what the users of this function *really* care
about. I'll have to do more analysis to figure that out. Putting this
here at least provides it access to proper analysis pass tools and other
such. It also allows us to more cleanly implement the baseline cost
interfaces in TTI.
With this commit, it is now theoretically possible to simplify much of
the inline cost analysis's handling of calls by calling through to this
interface. That conversion will have to happen in subsequent commits as
it requires more extensive restructuring of the inline cost analysis.
The CodeMetrics class is now really only in the business of running over
a block of code and aggregating the metrics on that block of code, with
the actual cost evaluation done entirely in terms of TTI.
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Previously we tried to infer it from the bit width size, with an added
IsIEEE argument for the PPC/IEEE 128-bit case, which had a default
value. This default value allowed bugs to creep in, where it was
inappropriate.
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This is more code to isolate the use of the Attribute class to that of just
holding one attribute instead of a collection of attributes.
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This cuts in half the number of virtual methods called to refill that word when compiling on a 64-bit
host, and will make 64-bit read operations faster.
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BLOB (i.e., large, performance intensive data) in a bitcode file was switched to
invoking one virtual method call per byte read. Now we do one virtual call per
BLOB.
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A SparseMultiSet adds multiset behavior to SparseSet, while retaining SparseSet's desirable properties. Essentially, SparseMultiSet provides multiset behavior by storing its dense data in doubly linked lists that are inlined into the dense vector. This allows it to provide good data locality as well as vector-like constant-time clear() and fast constant time find(), insert(), and erase(). It also allows SparseMultiSet to have a builtin recycler rather than keeping SparseSet's behavior of always swapping upon removal, which allows it to preserve more iterators. It's often a better alternative to a SparseSet of a growable container or vector-of-vector.
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is free. The whole CodeMetrics API should probably be reworked more, but
this is enough to allow deleting the duplicate code there for computing
whether an instruction is free.
All of the passes using this have been updated to pull in TTI and hand
it to the CodeMetrics stuff. Further, a dead CodeMetrics API
(analyzeFunction) is nuked for lack of users.
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depend on and use other analyses (as long as they're either immutable
passes or CGSCC passes of course -- nothing in the pass manager has been
fixed here). Leverage this to thread TargetTransformInfo down through
the inline cost analysis.
No functionality changed here, this just threads things through.
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a dynamic analysis done on each call to the routine. However, now it can
use the standard pass infrastructure to reference other analyses,
instead of a silly setter method. This will become more interesting as
I teach it about more analysis passes.
This updates the two inliner passes to use the inline cost analysis.
Doing so highlights how utterly redundant these two passes are. Either
we should find a cheaper way to do always inlining, or we should merge
the two and just fiddle with the thresholds to get the desired behavior.
I'm leaning increasingly toward the latter as it would also remove the
Inliner sub-class split.
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lowered cost.
Currently, this is a direct port of the logic implementing
isInstructionFree in CodeMetrics. The hope is that the interface can be
improved (f.ex. supporting un-formed instruction queries) and the
implementation abstracted so that as we have test cases and target
knowledge we can expose increasingly accurate heuristics to clients.
I'll start switching existing consumers over and kill off the routine in
CodeMetrics in subsequent commits.
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