[llvm-dev] RFC: a practical mechanism for applying Machine Learning for optimization policies in LLVM
Mircea Trofin via llvm-dev
llvm-dev at lists.llvm.org
Wed Apr 8 19:05:56 PDT 2020
+Yundi Qian <yundi at google.com> +Eugene Brevdo <ebrevdo at google.com> , our
team members from the ML side.
To avoid formatting issues, here is a link to the RFC
<https://docs.google.com/document/d/1BoSGQlmgAh-yUZMn4sCDoWuY6KWed2tV58P4_472mDE/edit?usp=sharing>,
open to comments.
Thanks!
On Wed, Apr 8, 2020 at 2:34 PM Mircea Trofin <mtrofin at google.com> wrote:
> Unfortunately I needed to update the links. Here they are, hopefully these
> work correctly.
>
> SPEC2006
> <https://docs.google.com/spreadsheets/d/e/2PACX-1vQNAcTDfyQvh6Jq7IRdCvK_fuluUFrzrsGL_75Ile29hX3caBSfT6_jHulxeCJ5MXIHp5SB--A_goEi/pubhtml?gid=987260531&single=true>
>
> Internal benchmarks, clang, opt
> <https://docs.google.com/spreadsheets/d/e/2PACX-1vQNAcTDfyQvh6Jq7IRdCvK_fuluUFrzrsGL_75Ile29hX3caBSfT6_jHulxeCJ5MXIHp5SB--A_goEi/pubhtml?gid=0&single=true>
>
> Thanks!
>
> On Wed, Apr 8, 2020 at 2:23 PM Mircea Trofin <mtrofin at google.com> wrote:
>
>> It turns out it's me, sorry. Let me see how I can sort this out. In the
>> meantime, here is the csv:
>>
>> SPEC2006 data:
>>
>> binary,base -Oz size,ML -Oz size,ML size shrink by,,perf: base -Oz
>> scores,perf: ML -Oz scores,ML improvement by
>> 400.perlbench,2054200,2086776,-1.59%,,2.9,2.9,0.00%
>> 401.bzip2,1129976,1095544,3.05%,,6.4,6.2,-3.13%
>> 403.gcc,4078488,4130840,-1.28%,,11.6,11.7,0.86%
>> 429.mcf,1089368,1054552,3.20%,,2.3,2.4,4.35%
>> 433.milc,1161336,1129592,2.73%,,0.5,0.5,0.00%
>> 444.namd,1324824,1290968,2.56%,,4.4,4.3,-2.27%
>> 445.gobmk,5003096,4992472,0.21%,,3.9,4.1,5.13%
>> 447.dealII,1003376,975024,2.83%,,162.1,197.8,22.02%
>> 450.soplex,1359416,1326008,2.46%,,0.1,0.1,0.00%
>> 453.povray,1921432,1952280,-1.61%,,32.9,32.7,-0.61%
>> 456.hmmer,1210744,1184632,2.16%,,1.5,1.5,0.00%
>> 458.sjeng,1185976,1155320,2.58%,,812.9,840.2,3.36%
>> 462.libquantum,1101144,1066712,3.13%,,12.3,12.2,-0.81%
>> 464.h264ref,1557176,1593272,-2.32%,,0.3,0.3,0.00%
>> 470.lbm,1091352,1056664,3.18%,,2.3,2.5,8.70%
>> 471.omnetpp,1045496,1046664,-0.11%,,27.9,28.4,1.79%
>> 473.astar,1106680,1071992,3.13%,,2.4,2.6,8.33%
>> 482.sphinx3,1216600,1182680,2.79%,,16.5,16.3,-1.21%
>> 483.xalancbmk,4666936,4669112,-0.05%,,9.7,9.6,-1.03%
>> ,,,,,,,
>> SIZE SUM,34307616,34061104,0.72%,TOTAL SCORES,5.7,5.8,1.75%
>>
>>
>> various benchmarks:
>>
>> binary,base -Oz size,ML -Oz size,ML shrink over base
>> llvm:opt,66318624,62124256,6.32%
>> math_1,6181552,5884144,4.81%
>> event_management_1,4998728,4802696,3.92%
>> llvm:lcalsBRaw,1270168,1222168,3.78%
>> llvm:lcalsBLambda,1270232,1222232,3.78%
>> llvm:lcalsARaw,1276248,1228248,3.76%
>> llvm:lcalsALambda,1276440,1228504,3.76%
>> llvm:lcalsCRaw,1278936,1231000,3.75%
>> llvm:lcalsCLambda,1279064,1231256,3.74%
>> llvm:Blur,1236888,1191192,3.69%
>> image_processing_diffusion,1236120,1190488,3.69%
>> llvm:Interpolation,1237208,1191576,3.69%
>> llvm:harris,1237208,1191768,3.67%
>> llvm:Dither,1238232,1192792,3.67%
>> event_management_2,4741096,4568584,3.64%
>> hashing_1,5189360,5004176,3.57%
>> compression_2,5329392,5140496,3.54%
>> math_2,6072336,5858992,3.51%
>> crypto_1,4721576,4556008,3.51%
>> math_4,27720208,26755568,3.48%
>> math_3,27732496,26767920,3.48%
>> infra_1,30673232,29630464,3.40%
>> hashing_2,5834544,5637168,3.38%
>> image_processing_entropy,5854960,5657008,3.38%
>> hashing_3,5831088,5634288,3.38%
>> memory_management_1,4957960,4790632,3.37%
>> hasing_4,5816048,5619888,3.37%
>> data_storage_2,5852976,5655792,3.37%
>> compression_1,5971984,5771120,3.36%
>> arena_unittest,6672944,6453584,3.29%
>> data_storage_3,44113232,42710960,3.18%
>> data_storage_1,22858992,22132960,3.18%
>> datastructures_1,6362032,6161264,3.16%
>> infra_2,56828720,55053496,3.12%
>> datastructures_2,164558704,160790320,2.29%
>> llvm:clang,77027416,75672088,1.76%
>> benchmark_infra,20221424,19944432,1.37%
>> search_1,169475360,167645632,1.08%
>> protobuf_3,24071504,23895440,0.73%
>> protobuf_1,24071504,23895504,0.73%
>> protobuf_2,24071504,23895504,0.73%
>> protobuf_4,24071504,23895504,0.73%
>> protobuf_5,24071504,23895504,0.73%
>> protobuf_6,23982800,23809424,0.72%
>>
>> On Wed, Apr 8, 2020 at 2:11 PM Johannes Doerfert <
>> johannesdoerfert at gmail.com> wrote:
>>
>>>
>>> Cool!
>>>
>>> I skipped to the end and tried to access the gdoc and the spreadsheet
>>> but it did tell me I need permissions. Can you make them accessible or
>>> am I the problem?
>>>
>>> Thanks,
>>> Johannes
>>>
>>> On 4/8/20 4:04 PM, Mircea Trofin via llvm-dev wrote:
>>> > TL;DR; We can improve compiler optimizations driven by heuristics by
>>> > replacing those heuristics with machine-learned policies (ML models).
>>> > Policies are trained offline and ship as part of the compiler.
>>> Determinism
>>> > is maintained because models are fixed when the compiler is operating
>>> in
>>> > production. Fine-tuning or regressions may be handled by
>>> incorporating the
>>> > interesting cases in the ML training set, retraining the compiler, and
>>> > redeploying it.
>>> >
>>> > For a first milestone, we chose inlining for size (-Oz) on X86-64. We
>>> were
>>> > able to train an ML model to produce binaries 1.5-6% smaller than -Oz
>>> of
>>> > tip-of-tree. The trained model appears to generalize well over a
>>> diverse
>>> > set of binaries. Compile time is increased marginally (under 5%). The
>>> model
>>> > also happens to produce slightly better - performing code under
>>> SPEC2006 -
>>> > total score improvement by 1.75%. As we only wanted to verify there
>>> is no
>>> > significant regression in SPEC, and given the milestone goals, we
>>> haven’t
>>> > dug any deeper into the speed results.
>>> >
>>> > We see these results as promising, and as a reasonable point for
>>> > contributing our current work as a build-time opt-in to LLVM to
>>> benefit the
>>> > community, in the hope of fostering collaboration and learning from
>>> the
>>> > community’s feedback, as we try to better understand the trade-offs
>>> such an
>>> > approach entails, and as we work on expanding the depth and breadth of
>>> > applying these techniques to compiler optimization problems.
>>> >
>>> > https://reviews.llvm.org/D77752
>>> > Motivation
>>> >
>>> > Optimization problems, such as inlining or register allocation, are
>>> hard:
>>> > we don’t know of algorithms that are guaranteed to produce the optimum
>>> > solution in all cases in a timely fashion. Instead, we use heuristics:
>>> > algorithms that we expect to produce some approximation of the
>>> optimum,
>>> > with some expected degree of generality, in a practical amount of
>>> time.
>>> >
>>> > Heuristics have some common characteristics. Taking inlining as a case
>>> > study, it traverses the problem space in some way (bottom-up
>>> traversal of
>>> > the SCC graph), extracts some properties (let’s call them “features”)
>>> of
>>> > the program being optimized, and combine them with some weights
>>> (“tune”),
>>> > to produce a cost (InlineCost), which allows for trade-off analysis.
>>> We
>>> > validate the effectiveness of a heuristic over some accepted set of
>>> > benchmarks. Over time, we react to regressions or pathological cases
>>> > observed in the field, by manually analyzing such cases, figuring out
>>> an
>>> > enhancement to the heuristic, and then re-validating over that set of
>>> > benchmarks (maybe augmented by adding the newly found cases).
>>> >
>>> > Because heuristics are code that needs to be maintained, there is
>>> pressure
>>> > to reduce complexity: adding more features means we need to reason
>>> about
>>> > the interactions between the old and new features, which scales
>>> > combinatorially. Re-tuning because of the new features adds a similar
>>> kind
>>> > of complexity. Potentially, we miss out on optimization improvements
>>> as a
>>> > result.
>>> >
>>> > Because tuning is manual, there is pressure to keep the number of
>>> > benchmarks that can be studied in depth to a humanly-manageable size,
>>> which
>>> > potentially affects the generality of a heuristic or heuristic tuning.
>>> >
>>> > The main advantage of manual heuristics is arguably their relatively
>>> lower
>>> > overhead: no additional dependencies and more transparent to human
>>> analysis
>>> > and improvement.
>>> >
>>> > Machine learning, in particular reinforcement learning, can address
>>> the
>>> > tensions found in manual heuristics: once features are extracted from
>>> the
>>> > program, the way they are combined and tuned can easily be scaled up
>>> > through automation, improving effectiveness and generality. A major
>>> > drawback, at least at this point in time, of machine learning, is
>>> that we
>>> > don’t yet have a fully developed systematic approach for improving
>>> policy
>>> > effectiveness.
>>> > High level design
>>> >
>>> > We identify two scenarios for a compiler using ML policies:
>>> development and
>>> > release.
>>> >
>>> > The release scenario is equivalent to the regular compilation we have
>>> today
>>> > - the only difference is that it uses a pre-trained model (trained in
>>> the
>>> > development scenario beforehand) to make decisions instead of the
>>> > heuristics. Determinism is guaranteed since the model in the release
>>> > scenario is fixed. We imagine teams wishing to fine tune the
>>> effectiveness
>>> > of the optimization to their scenarios would train a different model.
>>> >
>>> > The decision previously evaluated using a human-crafted heuristic is
>>> > optionally replaced by:
>>> >
>>> > -
>>> >
>>> > a compiler-specific component, extracting features from IR (i.e. a
>>> > vector of values)
>>> > -
>>> >
>>> > an evaluation of an ML model using those features, to obtain a
>>> result.
>>> > In ML nomenclature, this is referred to using the model for
>>> inference (as
>>> > opposed to training it)
>>> >
>>> > For example, when we replaced the decision of whether to inline a
>>> callsite,
>>> > the ML model produces a boolean (inline/don’t inline) based on a
>>> features
>>> > vector characterizing the call site and some broader module-wide
>>> context.
>>> >
>>> > Training/development is more complicated, and happens offline - akin
>>> to
>>> > how, today, attempts to improve an optimizing pass also happen
>>> offline. A
>>> > description of the high level design and the specifics we used for the
>>> > current scope are given in Appendix.
>>> > Current Scope
>>> >
>>> > The goal of our first milestone was to evaluate end to end an
>>> integration
>>> > of ML with LLVM, and get a first promising result. To that end, we
>>> chose
>>> > inlining for size (-Oz) as a stepping stone, as we perceived it to be
>>> more
>>> > likely to require a simpler evaluation setup than performance-oriented
>>> > optimizations might. At this point, we only train whether a call site
>>> may
>>> > be inlined or not, leaving the SCC traversal order as-is.
>>> >
>>> > We are proposing an initial change demonstrating the inference
>>> mechanism
>>> > using a pre-trained model, as a build-time opt-in to llvm. The
>>> compiler
>>> > components needed to perform training are also included in this first
>>> > change. Subsequent changes would include more training-related
>>> components.
>>> >
>>> > At a high level, the changes we are proposing consist of:
>>> >
>>> > 1.
>>> >
>>> > a new module analysis pass, InliningAdvisor. By default, its
>>> > implementation does nothing.
>>> > 2.
>>> >
>>> > minimal hooks into Inliner.cpp.
>>> > 3.
>>> >
>>> > the implementation of InliningAdvisor, activated when we opt-in
>>> ML. This
>>> > is available in Analysis/ML, together with:
>>> > 1.
>>> >
>>> > Rel/Dev specific ML model handing, also under Analysis/ML
>>> > 2.
>>> >
>>> > a pre-trained model for inlining for size
>>> > (Analysis/ML/models/inlining)
>>> > 3.
>>> >
>>> > a pre-trained model for predicting native size from IR
>>> > (Analysis/ML/models/ir_2_native_x86_64), used in Dev mode only.
>>> > 4.
>>> >
>>> > Some refactorings in PassBuilder, to allow opting into running
>>> mandatory
>>> > inlining first - some compilation speedup for the ML case, minimal,
>>> > noise-like size effect. Also simplifies testing (these would be
>>> introduced
>>> > as a preliminary patch).
>>> >
>>> > We discuss ‘rel’ mode here, and ‘dev’ mode in the Appendix, as it is
>>> more
>>> > involved.
>>> > Inference Opt-In Mechanism
>>> >
>>> > The feature is primarily controlled by the cmake flag
>>> > LLVM_USE_ML_POLICY={“Rel”|”Dev”}. Each has different dependencies. The
>>> > “Rel”ease case requires specifying the location of the pip tensorflow
>>> > package (currently, that’s tf_nightly, and it should soon be
>>> available in
>>> > tensorflow)
>>> >
>>> > To opt in the ‘Rel’ case:
>>> >
>>> > 1.
>>> >
>>> > install tensorflow pip package
>>> >
>>> > pip3 install tf_nightly --user
>>> >
>>> > 1.
>>> >
>>> > configure llvm build
>>> >
>>> > cmake ../llvm -DLLVM_USE_ML_POLICY=Rel \
>>> >
>>> > -DLLVM_TF_AOT_RUNTIME=~/.local/lib/python3.7/site-packages/tensorflow
>>> \
>>> >
>>> > {-DLLVM_TF_AOT_COMPILER=<path to saved_model_cli tool, if needed -
>>> it’s
>>> > usually in the path>}
>>> >
>>> >
>>> >
>>> > 1.
>>> >
>>> > build llvm as usual.
>>> > 2.
>>> >
>>> > pass -mllvm -enable-ml-inliner -mllvm -mandatory-inlinings-first to
>>> > clang.
>>> >
>>> > Details
>>> >
>>> > The ML model is captured as a TensorFlow ‘saved model’. When building
>>> llvm,
>>> > we use TensorFlow’s XLA native compiler (saved_model_cli) to compile
>>> the
>>> > saved model into a native static library and a header file. Insofar
>>> as LLVM
>>> > is concerned, there are minimal additional runtime requirements,
>>> packaged
>>> > as source within the pip package: C++ wrappers around the compiled
>>> model.
>>> > These will also be statically linked in the LLVM target. The compiled
>>> code
>>> > is otherwise just a complex arithmetical computation, with no special
>>> > requirements - it is single threaded and runs natively on the targeted
>>> > architecture. Together with the aforementioned runtime dependencies,
>>> it
>>> > adds ~115KB to the clang binary (0.08% increase)
>>> >
>>> > Runtime-wise, we observed a ~10% increase in the time spent in the
>>> inliner,
>>> > for a large (33MB) binary IR module; inlining typically consumes
>>> ~10-15% of
>>> > total compilation time, so the overall compile time overhead of the
>>> > approach is arguably negligible. This cost is almost in entirety
>>> > attributable to feature extraction.
>>> >
>>> > Memory-wise, the precompiled model has a fixed size buffer for its
>>> inputs,
>>> > and performs a fixed amount of computations, so the memory overhead
>>> > inherent to our approach is independent from the program being
>>> optimized.
>>> > Using a small example to avoid effects such as memory use differences
>>> due
>>> > to different inlinings, we observed an 300KB increase in the maximum
>>> > resident size.
>>> >
>>> > A table showing effect on -Oz compiled binaries’ size is given in
>>> Appendix.
>>> > Next directions
>>> >
>>> > Our next milestone has two main high level goals: detailing a
>>> systematic
>>> > approach to driving policy effectiveness; and exploring in depth the
>>> type
>>> > of features and training algorithms most appropriate for compiler
>>> problems,
>>> > or at least problems like inlining. For the latter, we expect
>>> embedding
>>> > more of the call graph structure to play an important role, as well
>>> as,
>>> > potentially, delegating the problem space traversal to the ML model.
>>> >
>>> > We plan to include inlining for speed as part of our work on these
>>> goals.
>>> > AppendixTraining - High Level
>>> >
>>> > We use Reinforcement Learning (RL) to train the Inline-for-size
>>> model. At a
>>> > high level, it is composed of 3 parts: training data collection, model
>>> > training, and iterative data collection/model training. We use
>>> TensorFlow
>>> > as our ML framework.
>>> >
>>> > Related, we also needed to learn a separate model to evaluate the
>>> native
>>> > size of a function, given its IR, in order to calculate a more precise
>>> > reward for the reinforcement learning algorithm (“IR2Native”). We
>>> evaluated
>>> > ‘just counting IR’ and TargetTransformInfo, but they appeared to
>>> provide
>>> > too noisy of a signal for the reward, insofar as the RL training
>>> algorithm
>>> > for the inlining model was concerned. This model is only used during
>>> > training.
>>> >
>>> > RL - Training data collection: the training data we need to feed into
>>> a
>>> > reinforcement learning algorithm are sequences consisting of: state
>>> of the
>>> > problem (i.e. features); action (inline/not inline), and reward
>>> (native
>>> > size shrinkage after inline/not inline, using ir2native). To collect
>>> the
>>> > sequences, we hook the logging infrastructure into LLVM Inliner that
>>> is
>>> > able to produce logs after the inline optimization pass.
>>> >
>>> > RL - Model training: We use DQN (Deep Q-Network) to train our
>>> > inlining-for-size ML policy. On a high level, the DQN algorithm
>>> trains a
>>> > neural network to predict the value of different actions --- the DQN
>>> policy
>>> > then chooses to take the action with the highest predicted value. In
>>> our
>>> > scenario, we have two actions: 1) inline; 2) not inline, so the neural
>>> > network predicts the size reduction of these two actions based on
>>> features,
>>> > and then decides to conduct inlining if the neural network believes
>>> doing
>>> > inlining leads to higher size reduction. After the training finishes,
>>> it
>>> > produces a TensorFlow SavedModel that takes features as input and
>>> generates
>>> > inline decisions (whether to inline or not) as output.
>>> >
>>> > The choice of the features and reward are essential in model
>>> training. The
>>> > features are chosen with the consideration of being helpful in making
>>> the
>>> > decision --- the input to the cost model is a good starting point.
>>> Ideally,
>>> > the reward in the inline-for-size problem is the native size shrinkage
>>> > after inline/not inline. It is difficult to obtain precisely, so we
>>> use the
>>> > estimate as an alternative. This means that, for training, we also
>>> need a
>>> > model (not necessarily machine learned) for estimating rewards.
>>> >
>>> > RL - Iterative data collection/model training: Reinforcement learning
>>> is
>>> > ideally an iterative model/policy improvement process that: 1) the
>>> trained
>>> > model is deployed to the field to collect new data; 2) newly
>>> collected data
>>> > are used to update the model. Thus, we need to do iterative data
>>> > collection/model training. To facilitate data collection (avoid
>>> complex
>>> > build dependencies and time spent before/after the pass being
>>> trained), we
>>> > isolate out IR modules captured right before the optimization we are
>>> > interested in, and iterate on them with opt. We bootstrap the
>>> training from
>>> > the current heuristic, and stop the process once we are happy with the
>>> > outcome.
>>> >
>>> > IR2Native: We collect IR features (different from the ones used for
>>> > inlining) for each function at the end of inlining, right before we
>>> perform
>>> > function simplification passes, and right after. This means we have
>>> two IR
>>> > ‘shapes’ of the same function, and we know no further inlinings will
>>> be
>>> > performed, so whatever changes happen are based on that IR. We then
>>> extract
>>> > the native size at the end of compilation. Together, this data forms
>>> two
>>> > records per function that can be used in supervised learning - the
>>> features
>>> > are those extracted from IR, and the label is the native size.
>>> Training
>>> > IR2Native happens independently from the training of the inliner
>>> model.
>>> > Training support for the current scope
>>> >
>>> > The initial change includes the logging mechanism, an ir2native model
>>> > trained for x86-64, and the means to rapidly iterate over development
>>> ML
>>> > models. For the components that will be included in subsequent
>>> changes, the
>>> > rest of this section describes the mechanisms we employed. These
>>> components
>>> > are detailed further below.
>>> >
>>> > To build LLVM with the ML policy in ‘Dev’ mode, we need the
>>> tensorflow C
>>> > API library <https://www.tensorflow.org/install/lang_c>. We then
>>> configure
>>> > the build:
>>> >
>>> > cmake .. -DLLVM_USE_ML_POLICY=Dev \
>>> >
>>> > -DLLVM_TF_C_LIB=<path to unarchived package> \
>>> >
>>> > {-DCMAKE_INSTALL_RPATH_USE_LINK_PATH=True, to copy tensorflow shared
>>> > library over, if it’s not on LD_LIBRARY_PATH}
>>> >
>>> >
>>> > To extract IR right before inlining, we hacked on top of the ThinLTO
>>> > infrastructure, interrupting its pre-link pipeline right before
>>> inlining.
>>> > This means clang produces binary IR files captured at that stage. We
>>> then
>>> > built a large target, obtaining a corpus of ~25K modules. We intend to
>>> > provide a clean mechanism in a subsequent change.
>>> >
>>> > To obtain features/labels for training this “IR to Native Size”
>>> model, we
>>> > had to make some changes to the AsmPrinter (to get real native sizes)
>>> and
>>> > llvm-readobj, as well as add an analysis pass for extracting the IR
>>> > features for this model. We plan on upstreaming these changes
>>> subsequently.
>>> >
>>> > Finally, the infrastructure driving the policy training is currently
>>> built
>>> > on proprietary APIs, as it benefits from a distributed computing
>>> > infrastructure. We are currently evaluating options for open sourcing
>>> it.
>>> > In the meantime, we are presenting the high level implementation
>>> details.
>>> >
>>> > To train a new model, the infrastructure performs 2 steps: extracting
>>> the
>>> > logs, and using them in a training algorithm.
>>> >
>>> > Log extraction is highly parallelizable: for each IR module in the
>>> training
>>> > corpus, we need to run opt once (or a few times, when we explore
>>> > improvements). Specifically, each run is this invocation:
>>> >
>>> > opt -passes=scc-oz-module-inliner -ml-inliner-ir2native-model=<path to
>>> > ir2native> -training-log=<path to training log output>
>>> -enable-ml-inliner
>>> > -mandatory-inlinings-first -o <output> <module.o>
>>> >
>>> > Then collect the logs, and pass them to the next step.
>>> >
>>> >
>>> > Experimental results
>>> >
>>> > Experimental results are available as follows:
>>> >
>>> >
>>> > -
>>> >
>>> > SPEC2006
>>> >
>>> <
>>> https://docs.google.com/spreadsheets/d/e/2PACX-1vQv0bAsUlgnG114zMYy_zKR6x-lTjcXVNt7VEeSwq2-pDr5oTxdASCscPRRg6L7iYLu2AVJ44G2xEkp/pubhtml?gid=1870752756&single=true
>>> >
>>> > binary sizes (-Oz) and ‘test run’ scores.
>>> > -
>>> >
>>> > Size report
>>> >
>>> <
>>> https://docs.google.com/spreadsheets/d/e/2PACX-1vQv0bAsUlgnG114zMYy_zKR6x-lTjcXVNt7VEeSwq2-pDr5oTxdASCscPRRg6L7iYLu2AVJ44G2xEkp/pubhtml?gid=935959003&single=true
>>> >
>>> > from some internal benchmarks and binaries, including opt and clang
>>> >
>>> >
>>> > _______________________________________________
>>> > LLVM Developers mailing list
>>> > llvm-dev at lists.llvm.org
>>> > https://lists.llvm.org/cgi-bin/mailman/listinfo/llvm-dev
>>>
>>>
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