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Whole Module Optimization in Swift
==================================

State of Whole Module Optimization in Swift 2.0
===============================================

Since Swift 1.2 / Xcode 6.3, the Swift optimizer has included support
for whole module optimization (WMO).

To date (Swift 2.0 / Xcode 7), the differences in the optimization
pipeline and specific optimization passes when WMO is enabled have
been relatively minimal, and have provided high value at low
implementation cost. Examples of this include inferring final on
internal methods, and removing functions that are not referenced
within the module and cannot be referenced from outside the module.

Additionally, compiling with WMO has some natural consequences that
require no enhancements to passes. For example the increased scope of
compilation that results from having the entire module available makes
it possible for the inliner to inline functions that it would
otherwise not be able to inline in normal separate compilation. Other
optimizations similarly benefit, for example generic specialization
(since it has more opportunities for specializae) and function
signature optimization (since it has more call sites to rewrite).


Whole Module Optimization for Swift.Next
========================================

As it stands, WMO provides significant benefit with minimal complexity,
but also has many areas in which it can be improved. Some of these
areas for improvement are architectural, e.g. improvements to the pass
management scheme, while others involve adding new interprocedural
analyses and updating existing passes to make use of the results of
these analyses.

Passes and Pass Management
--------------------------

Our current pass management scheme intersperses module passes and
function passes in a way that results in module passes being run on
functions that are partially optimized, which is suboptimal. Consider
inlining as one example. If we run the inliner when callee functions
are only partially optimized we may make different inlining decisions
in a caller than if we ran it on a caller only after its callees have
been fully optimized. This particular issue and others mentioned below
are not specific to WMO per-se, but are more generally a problem for
any interprocedural optimization that we currently do (most of which
happen in per-file builds as well).

This also affects interprocedural optimizations where we can compute
summary information for a function and use that information when
optimizing callers of that function. We can obtain better results by
processing strongly connected components (SCCs) of the call graph
rather than individual functions, and running the full optimization
pipeline on a given SCC before moving to the next SCC in a post-order
traversal of the call graph (i.e. bottom-up). A similar approach can
be taken for running transforms that benefit from information that is
propagated in reverse-post-order in the call graph (i.e. top-down).

Processing one SCC at a time versus one function at a time is
primarily for the benefit of improved analyses. For example consider
escape analysis. If there is an SCC in the call graph and we process
one function at a time, there are cases where we would have to be
pessimistic and assume a value escapes, when in fact the value may be
used within the SCC such that it never escapes. The same pessimism can
happen in other analyses, e.g. dead argument analysis.

In our current set of passes, several are implemented as
SILModuleTransforms but simply iterate over the functions in the
module in whatever order they happen to be in and do not appear to
benefit from being module passes at this time (although some would
benefit from optimizing the functions in bottom-up order in the call
graph). Other SILModuleTransforms currently walk the functions
bottom-up in the call graph, and do gain some benefit from doing so.

Moving forward we should eliminate the notion of module passes and
instead have SCC passes as well as the existing function passes. We
should change the pass manager to run the SCC passes as a bottom-up
traversal of the call graph. As previously mentioned we may also want
to consider having the flexibility of being able to run passes in a
top-down order (so we could create a pass manager with passes that
benefit from running in this order, not because we would want to run
any arbitrary pass in that order).

For each SCC we would run the entire set of passes before moving on to
the next SCC, so when we go to optimize an SCC any functions that it
calls (when going bottom-up - or calls into it when going top-down)
have been fully optimized. As part of this, analyses that benefit from
looking at an SCC-at-a-time will need to be modified to do
so. Existing analyses that would benefit from looking at an
SCC-at-a-time but have not yet been updated to do so can be run on
each function in the SCC in turn, producing potentially pessimistic
results. In time these can be updated to analyze an entire
SCC. Likewise function passes would be run on each function in the SCC
in turn. SCC function passes would be handed an entire SCC and be able
to ask for the analysis results for that SCC, but can process each
function individually based on those results.

**TBD: Do we really need SCC transforms at all, or is it sufficient to
simply have function transforms that are always passed an SCC, and
have them ask for the results of an analyses for the entire SCC and
then iterate over all functions in the SCC?**

In some cases we have transforms that generate new work in a top-down
fashion, for example the devirtualizer as well as any pass that
clones. These can be handled by allowing function passes to push new
work at the top of the stack of work items, and then upon finishing a
pass the pass pipeline will be restarted with those new functions at
the top of the work stack, and the previous function buried beneath
them, to be reprocessed after all the callees are processed.

**TBD: This may result in re-running some early passes multiple times
for any given function, and it may mean we want to front-load the
passes that generate new callees like this so that they are early in
the pipeline. We could also choose not to rerun portions of the
pipeline on the function that's already had some processing done on
it.***

SCC-based analyses
------------------

There are a variety of analyses that can be done on an SCC to produce
better information than can be produced by looking at a single
function at a time, even when processing in (reverse-)post-order on
the call graph. For example, a dead argument analysis can provide
information about arguments that are not actually used, making it
possible for optimizations like DCE and dead-store optimization (which
we do as part of load/store opts) to eliminate code, independent of a
pass like function signature optimization running (and thus we
eliminate a phase-ordering issue). It benefits from looking at an
SCC-at-a-time because some arguments getting passed into the SCC might
be passed around the SCC, but are never used in any other way by a
function within the SCC (and are never passed outside of the SCC).

Similarly, escape analysis, alias analysis, and mod/ref analysis
benefit from analyzing an SCC rather than a function.

This list is not meant to be exhaustive, but is probably a good
initial set of analyses to plan for once we have the new pass
framework up.

Incremental Whole Module Optimization
-------------------------------------

Building with WMO enabled can result in slower build times due to
reduced parallelization of the build process. We currently parallelize
some of the last-mile optimization and code generation through LLVM,
but do not attempt to parallelize any of the SIL passes (see the next
section).

With that in mind, it seems very worthwhile to examine doing
incremental WMO in order to minimize the amount of work done for each
compile.

One way to accomplish this is to serialize function bodies after
canonical SIL is produced (i.e. after the mandatory passes) and only
reoptimize those functions which change between builds. Doing just
this, however, is not sufficient since we've used information gleaned
from examining other functions, and we've also done things like inline
functions into one another. As a result, we need to track dependencies
between functions in order to properly do incremental compilation
during WMO.

Some approaches to tracking these dependencies could be very
burdensome, requiring passes to explicitly track exactly which
information they actually use during optimization. This seems error
prone and difficult to maintain.

Another approach might be to recompile adjacent functions in the call
graph when a given function changes. This might be somewhat practical
if we only have analyses which propagate information bottom-up, but it
would be more expensive than necessary, and impractical if we also
have analyses that propagate information top-down since it could
result in a full recompile of the module in the worst case.

A more reasonable approach would be to serialize the results of the
interprocedural analyses at the end of the pass pipeline, and use
these serialized results to drive some of the dependency tracking
(along with some manual tracking, e.g. tracking which functions are
inlined at which call sites). These serialized analysis results would
then be compared against the results of running the same analyses at
the end of the compilation pipeline on any function which has changed
since the previous compile. If the results of an analysis changes, the
functions which use the results of that analysis would also need to be
recompiled.

**TBD: Properly tracking dependencies for functions generated from
other functions via cloning. Is this any different from tracking for
inlining?**

Parallel Whole Module Optimization
----------------------------------

We could also explore the possibility of doing more work in parallel
during WMO builds. For example, it may be feasible to run the SIL
optimization passes in parallel. It may also be feasible to do IRGen
in parallel, although there are shared mutating structures that would
need to be guarded.

It's TBD whether this is actually going to be practical and
worthwhile, but it seems worth investigating and scoping out the work
involved to some first-level of approximation.
