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

Jessica Paquette via llvm-dev llvm-dev at lists.llvm.org
Sat Aug 27 21:11:15 PDT 2016

That’s actually something that’s been brought up a few times! I’m thinking of ways to use the suffix tree based approach as a tool for that, and maybe for finding areas where code could be refactored. For example, people could find sequences of instructions which appear suspiciously often, and deduce that they are copied and pasted pieces of code.

It also may be interesting to investigate other string algorithms to see if they may have similar applications. Techniques used in bioinformatics are interesting to me in particular, since they work on finding structural similarities across gene sequences. It’s not exactly the same thing, but it might be possible to repurpose their techniques to find certain similarities across codebases.

- Jessica

> On Aug 27, 2016, at 1:38 PM, Philip Reames <listmail at philipreames.com> wrote:
> Thanks for a well written and informative writeup.  This was a pleasure to read and laid out the issues well for consideration.
> On a technical level, I could see this having a secondary use for developers of the compiler itself.  Seeing the same pattern of assembly many times across a large binary is often a hint of a missed optimization.  Using this type of technique to generate candidates for further investigation seems interesting.
> Philip
> On 08/26/2016 02:26 PM, Jessica Paquette via llvm-dev wrote:
>> 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.
>> 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.
>> 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
>> ================================
>> 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.
>> 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.
>> 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
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