a print method.
This was formulated on a bad idea, but sadly I didn't uncover how bad
this was until I got further down the path. I had hoped that we could
provide a low boilerplate way of printing analyses, but it just doesn't
seem like this really fits the needs of the analyses. Not all analyses
really want to do printing, and those that do don't all use the same
interface. Instead, with the new pass manager let's just take advantage
of the fact that creating an explicit printer pass like the LCG has is
pretty low boilerplate already and rely on that for testing.
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I'm adding generic analysis printing utility pass support which will
require such a method (or a specialization) so this will let the
existing printing logic satisfy that.
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units.
This was debated back and forth a bunch, but using references is now
clearly cleaner. Of all the code written using pointers thus far, in
only one place did it really make more sense to have a pointer. In most
cases, this just removes immediate dereferencing from the code. I think
it is much better to get errors on null IR units earlier, potentially
at compile time, than to delay it.
Most notably, the legacy pass manager uses references for its routines
and so as more and more code works with both, the use of pointers was
likely to become really annoying. I noticed this when I ported the
domtree analysis over and wrote the entire thing with references only to
have it fail to compile. =/ It seemed better to switch now than to
delay. We can, of course, revisit this is we learn that references are
really problematic in the API.
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This is to be consistent with StringSet and ultimately with the standard
library's associative container insert function.
This lead to updating SmallSet::insert to return pair<iterator, bool>,
and then to update SmallPtrSet::insert to return pair<iterator, bool>,
and then to update all the existing users of those functions...
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operations on the call graph. This one forms a cycle, and while not as
complex as removing an internal edge from an SCC, it involves
a reasonable amount of work to find all of the nodes newly connected in
a cycle.
Also somewhat alarming is the worst case complexity here: it might have
to walk roughly the entire SCC inverse DAG to insert a single edge. This
is carefully documented in the API (I hope).
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just connects an SCC to one of its descendants directly. Not much of an
impact. The last one is the hard one -- connecting an SCC to one of its
ancestors, and thereby forming a cycle such that we have to merge all
the SCCs participating in the cycle.
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of SCCs in the SCC DAG. Exercise them in the big graph test case. These
will be especially useful for establishing invariants in insertion
logic.
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edge entirely within an existing SCC. Shockingly, making the connected
component more connected is ... a total snooze fest. =]
Anyways, its wired up, and I even added a test case to make sure it
pretty much sorta works. =D
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bits), and discover that it's totally broken. Yay tests. Boo bug. Fix
the basic edge removal so that it works by nulling out the removed edges
rather than actually removing them. This leaves the indices valid in the
map from callee to index, and preserves some of the locality for
iterating over edges. The iterator is made bidirectional to reflect that
it now has to skip over null entries, and the skipping logic is layered
onto it.
As future work, I would like to track essentially the "load factor" of
the edge list, and when it falls below a threshold do a compaction.
An alternative I considered (and continue to consider) is storing the
callees in a doubly linked list where each element of the list is in
a set (which is essentially the classical linked-hash-table
datastructure). The problem with that approach is that either you need
to heap allocate the linked list nodes and use pointers to them, or use
a bucket hash table (with even *more* linked list pointer overhead!),
etc. It's pretty easy to get 5x overhead for values that are just
pointers. So far, I think punching holes in the vector, and periodic
compaction is likely to be much more efficient overall in the space/time
tradeoff.
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contract (and be much more useful). It now provides exactly the
post-order traversal a caller might need to perform on newly formed
SCCs.
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API requirements much more obvious.
The key here is that there are two totally different use cases for
mutating the graph. Prior to doing any SCC formation, it is very easy to
mutate the graph. There may be users that want to do small tweaks here,
and then use the already-built graph for their SCC-based operations.
This method remains on the graph itself and is documented carefully as
being cheap but unavailable once SCCs are formed.
Once SCCs are formed, and there is some in-flight DFS building them, we
have to be much more careful in how we mutate the graph. These mutation
operations are sunk onto the SCCs themselves, which both simplifies
things (the code was already there!) and helps make it obvious that
these interfaces are only applicable within that context. The other
primary constraint is that the edge being mutated is actually related to
the SCC on which we call the method. This helps make it obvious that you
cannot arbitrarily mutate some other SCC.
I've tried to write much more complete documentation for the interesting
mutation API -- intra-SCC edge removal. Currently one aspect of this
documentation is a lie (the result list of SCCs) but we also don't even
have tests for that API. =[ I'm going to add tests and fix it to match
the documentation next.
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them, just skip over any DFS-numbered nodes when finding the next root
of a DFS. This allows the entry set to just be a vector as we populate
it from a uniqued source. It also removes the possibility for a linear
scan of the entry set to actually do the removal which can make things
go quadratic if we get unlucky.
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the DFS stack for leaves in the call graph. As mentioned in my previous
commit, this is particularly interesting for graphs which have high fan
out but low connectivity resulting in many leaves. For such graphs, this
can remove a large % of the DFS stack traffic even though it doesn't
make the stack much smaller.
It's a bit easier to formulate this for the full algorithm because that
one stops completely for each SCC. For example, I was able to directly
eliminate the "Recurse" boolean used to continue an outer loop from the
inner loop.
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makes working through the worklist much cleaner, and makes it possible
to avoid the 'bool-to-continue-the-outer-loop' hack. Not a huge
difference, but I think this is approaching as polished as I can make
it.
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processed in the DFS out of the stack completely. Keep it exclusively in
a variable. Re-shuffle some code structure to make this easier. This can
have a very dramatic effect in some cases because call graphs tend to
look like a high fan-out spanning tree. As a consequence, there are
a large number of leaf nodes in the graph, and this technique causes
leaf nodes to never even go into the stack. While this only reduces the
max depth by 1, it may cause the total number of round trips through the
stack to drop by a lot.
Now, most of this isn't really relevant for the incremental version. =]
But I wanted to prototype it first here as this variant is in ways more
complex. As long as I can get the code factored well here, I'll next
make the primary walk look the same. There are several refactorings this
exposes I think.
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graph in any way because we don't track edges in the SCC graph, just
nodes. This also lets us add a nice assert about the invariant that
we're working on at least a certain number of nodes within the SCC.
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a helper function. Also factor the other two places where we did the
same thing into the helper function. =] Much cleaner this way. NFC.
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SCCMap to test for nodes that have been re-added to the root SCC rather
than a set vector. We already have done the SCCMap lookup, we juts need
to test it in two different ways. In turn, do most of the processing of
these nodes as they go into the root SCC rather than lazily. This
simplifies the final loop to just stitch the root SCC into its
children's parent sets. No functionlatiy changed.
However, this makes a few things painfully obvious, which was my intent.
=] There is tons of repeated code introduced here and elsewhere. I'm
splitting the refactoring of that code into helpers from this change so
its clear that this is the change which switches the datastructures used
around, and the other is a pure factoring & deduplication of code
change.
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remove the nodes in the SCC from the SCC map entirely prior to the DFS
walk. This allows the SCC map to represent both the state of
not-yet-re-added-to-an-SCC and added-back-to-this-SCC independently. The
first is being missing from the SCC map, the second is mapping back to
'this'. In a subsequent commit, I'm going to use this property to
simplify the new node list for this SCC.
In theory, I think this also makes the contract for orphaning a node
from the graph slightly less confusing. Now it is also orphaned from the
SCC graph. Still, this isn't quite right either, and so I'm not adding
test cases here. I'll add test cases for the behavior of orphaning nodes
when the code *actually* supports it. The change here is mostly
incidental, my goal is simplifying the algorithm.
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child from the worklist, wait until we actually need to pop another
element off of the worklist and skip over any that were already visited
by the DFS. This also enables swapping the nodes of the SCC into the
worklist. No functionality changed.
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thing, just mucking up the code. I feel bad that I even wrote this loop.
Very sorry. The diff is huge because of the indent change, but I promise
all this is doing is realizing that the outer two loops were actually
the exact same loops, and we didn't need two of them.
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factored into a more reasonable form, replace the tail call with
a simple outer-loop continuation. It's sad that C++ makes this so
awkward to write, but it seems more direct and clear than the tail call
at this point.
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Chandler Carruth
David Blaikie
Alp Toker