[llvm-dev] [RFC] Interprocedural MIR-level outlining pass

Jessica Paquette via llvm-dev
2016-08-26T21:26:09Z

Hi everyone,

Since I haven't said anything on the mailing list before, a quick
introduction. I'm an intern at Apple, and over the summer I implemented a
prototype for an outlining pass for code size in LLVM. Now I'm seeking to
eventually upstream it. I have the preliminary code on GitHub right now,
but it's still very prototypical (see the code section).

===============================Motivation
===============================The goal of the internship was to create an
interprocedural pass that
would reduce code size as much as possible, perhaps at the cost of some
performance. This would be useful to, say, embedded programmers who only
have a few kilobytes to work with and a substantial amount of code to fit
in that space.


===============================Approach and Initial Results
===============================To do this, we chose to implement an outliner.
Outliners find sequences of
instructions which would be better off as a function call, by some measure
of "better". In this case, the measure of "better" is
"makes code
smaller".


===============================Results
===============================These results are from a fairly recent 64-bit
Intel processor, using a
version of Clang equipped with the outliner prototype versus an equivalent
version of Clang without the outliner.

CODE SIZE REDUCTION
For tests >=4 Kb in non-outlined size, the outliner currently provides an
average of 12.94% code size reduction on the LLVM test suite in comparison
to a default Clang, up to 51.4% code size reduction. In comparison to a
Clang with -Oz, the outliner provides an average of a 1.29% code size
reduction, up to a 37% code size reduction. I believe that the -Oz numbers
can be further improved by better tuning the outlining cost model.

EXECUTION TIME IMPACT
On average, the outliner increases execution time by 2% on the LLVM test
suite, but has been also shown to improve exection time by up to 16%.
These results were from a fairly recent Intel processor, so the results
may vary. Recent Intel processors have very low latency for function
calls, which may impact these results. Execution time improvements are
likely dependent on the latency of function calls, instruction caching
behaviour, and the execution frequency of the code being outlined. In
partucular, using a processor with heavy function call latency will likely
increase execution time overhead.


===============================Implementation
===============================The outliner, in its current state, is a
MachineModulePass. It finds
*identical* sequences of MIR, after register allocation, and pulls them
out into their own functions. Thus, it's effectively assembly-level.
Ultimately, the algorithm used is general, so it can sit anywhere, but MIR
was very convenient for the time being.

It requires two data structures.

1. A generalized suffix tree
2. A "terminated string"

1: The generalized suffix tree is constructed using Ukkonen's linear time
construction algorithm [1]. They require linear space and support
linear-time substring queries. In practice, the construction time for the
suffix tree is the most time consuming part, but I haven't noticed a
difference in compilation time on, say, 12 MB .ll files.

2: To support the suffix tree, we need a "terminated string." This is
a
generalized string with an unique terminator character appended to the
end. TerminatedStrings can be built from any type.

The algorithm is then roughly as follows.

1. For each MachineBasicBlock in the program, build a TerminatedString for
that block.
2. Build a suffix tree for that collection of strings.
3. Query the suffix tree for the longest repeated substring and place that
string in a candidate list. Repeat until none are found.
4. Create functions for each candidate.
5. Replace each candidate with a call to its respective function.

Currently, the program itself isn't stored in the suffix tree, but rather
a "proxy string" of integers. This isn't necessary at the MIR
level, but
may be for an IR level extension of the algorithm.


===============================Challenges
===============================1) MEMORY CONSUMPTION
Given a string of length n, a naive suffix tree implementation can take up
to 40n bytes of memory. However, this number can be reduced to 20n with a
bit of work [2]. Currently, the suffix tree stores the entire program,
including instructions which really ought not to be outlined, such as
returns. These instructions should not be included in the final
implementation, but should rather act as terminators for the strings. This
will likely curb memory consumption. Suffix trees have been used in the
past in sliding-window-based compression schemes, which may serve as a
source of inspiration for reducing memory overhead.[3]

Nonetheless, the outliner probably shouldn't be run unless it really has
to be run. It will likely be used mostly in embedded spaces, where the
programs have to fit into small devices anyway. Thus, memory overhead for
the compiler likely won't be a problem. The outliner should only be used
in -Oz compilations, and possibly should have its own flag.


2) EXECUTION TIME
Currently, the outliner isn't tuned for preventing execution time
increases. There is an average of a 2% execution time hit on the tests in
the LLVM test suite, with a few outliers of up to 30%. The outliers are
tests which contain hot loops. The outliner really ought to be able to use
profiling information and not outline from hot areas. Another suggestion
people have given me is to add a "never outline" directive which would
allow the user to say something along the lines of "this is a hot loop,
please never outline from it".

It's also important to note that these numbers are coming from a fairly
recent Intel processor.


3) CONSTRAINTS ON INSTRUCTIONS
The outliner currently won't pull anything out of functions which use a
red zone. It also won't pull anything out that uses the stack, instruction
pointer, uses constant pool indices, CFI indices, jump table indices, or
frame indices. This removes many opportunities for outlining which would
likely be available at a higher level (such as IR). Thus, there's a case
for moving this up to a higher level.


===============================Additional Applications
===============================The suffix tree itself could be used as a tool
for finding opportunities
to refactor code. For example, it could recognize places where the user
likely copied and pasted some code. This could be run on codebases to find
areas where people could manually outline things at the source level.

Using the terminated string class, it would also be possible to implement
other string algorithms on code. This may open the door to new ways to
analyze existing codebases.


===============================Roadmap
===============================The current outliner is *very* prototypical. The
version I would want to
upstream would be a new implementation. Here's what I'd like to address
and accomplish.

1. Ask "what does the LLVM community want from an outliner" and use
that
to drive development of the algorithm.
2. Reimplement the outliner, perhaps using a less memory-intensve data
structure like a suffix array.
3. Begin adding features to the algorithm, for example:
    i.   Teaching the algorithm about hot/cold blocks of code and taking
that into account.
    ii.  Simple parameter passing.
    iii. Similar function outlining-- eg, noticing that two outlining
candidates are similar and can be merged into one function with some
control flow.


===============================Code
===============================Note: This code requires MachineModulePasses

* Main pass:
https://github.com/ornata/llvm/blob/master/lib/CodeGen/MachineOutliner.h

* Suffix tree:
https://github.com/ornata/llvm/blob/master/include/llvm/ADT/SuffixTree.h

* TerminatedString and TerminatedStringList:
https://github.com/ornata/llvm/blob/master/include/llvm/ADT/TerminatedString.h

Here are a couple unit tests for the data structures.

* Suffix tree unit tests:
https://github.com/ornata/llvm/blob/master/unittests/ADT/SuffixTreeTest.cpp

* TerminatedString unit tests:
https://github.com/ornata/llvm/blob/master/unittests/ADT/TerminatedStringTest.cpp

* TerminatedStringList unit tests:
https://github.com/ornata/llvm/blob/master/unittests/ADT/TerminatedStringListTest.cpp


===============================References
===============================[1] Ukkonen's Algorithm:
https://www.cs.helsinki.fi/u/ukkonen/SuffixT1withFigs.pdf
[2] Suffix Trees and Suffix Arrays:
http://web.cs.iastate.edu/~cs548/suffix.pdf
[3] Extended Application of Suffix Trees to Data Compression:
http://www.larsson.dogma.net/dccpaper.pdf


Thanks for reading,
Jessica