[llvm-dev] [RFC] BOLT: A Framework for Binary Analysis, Transformation, and Optimization

Tue Oct 20 11:39:18 PDT 2020

Hi All,

Please find below an RFC for adding a binary optimization framework to LLVM.

Thanks for the feedback,

Maksim & BOLT Team

[RFC] BOLT: A Framework for Binary Analysis, Transformation, and Optimization

1. Background and Motivation

BOLT is a static post-link binary optimizer successfully used inside and outside of Facebook for code optimizations that complement traditional compiler whole-program and link-time optimizations [1]. Last year Google reported that BOLT accelerates their key workloads by 2% to 6% [2]. Additionally, BOLT is used in academia as a framework for program instrumentation, transformation, and binary analysis [3].

We believe that including BOLT in the LLVM project will benefit the community in several ways [4]. First, BOLT is useful as a binary optimizer. It has been used at Facebook to accelerate the top services, and we would love to see more people benefit from the performance boost that BOLT brings. We would also love to see our partners adopt BOLT's new use-cases, such as optimizing mobile and embedded applications. Beyond the binary optimizer, BOLT is an impressive infrastructure that enables research in the following areas:

     *   Advanced disassembly
     *   Low-level program instrumentation
     *   Static analysis

2. Overview

While BOLT uses several LLVM libraries [5], it is currently a separate project based on an out-of-tree version of LLVM [6]. Most of the code lives under separate tools/llvm-bolt directory, and required changes to LLVM are included in the supplied patch [7]. The patch mainly consists of backported fixes and minor extensions of the existing interfaces to update debug info, frame information, and ORC.

BOLT has no external build dependencies outside of LLVM. For profile collection, we recommend using sampling with a Linux perf tool [8]. LBR (last branch records) feature [9] is recommended as it improves profile quality dramatically. BOLT can be supplied perf.data profile directly, but in general, we advise converting the profile first using the supplied perf2bolt utility. In case the sampling profiling is unavailable, e.g., while running under a hypervisor locally or in the cloud, BOLT provides a builtin instrumentation.

BOLT supports x86-64 ELF as the primary platform. We have also implemented the initial support for Aarch64, and the support for MachO is in the works.

3. USE CASES

3.1 Link-time and binary transformations and optimizations

Static profile-driven post-link optimization of an application was our primary goal when creating BOLT. The convenience and expandability that the model offers perhaps could only be exceeded by a dynamic binary optimization approach. E.g., to optimize a binary using a perf-generated profile, the user has to execute a single command:

$ llvm-bolt a.out -data perf.data -o a.out.bolt <optimization opts>

No recompilation of a.out is needed (*), but we ask to link with "--emit-relocs" for maximum performance gains. However, the latter is not a strict requirement, and the command works even on stripped binaries.

We have worked on reducing BOLT processing time and memory consumption. Overall, BOLT efficiency has been improved by over 3X during that process. It takes less than a minute to optimize HHVM [10] production binary with over 100 MB of code and less than 3 minutes to optimize another multi-gigabyte production binary with 500 MB of code. BOLT memory consumption is under 5 GB for HHVM and under 13 GB for the large binary (**).

Fast turn-around times for optimizing an application with BOLT without the need for source code allow us to design a system that automatically manages binary profiling and optimization. This is one of the most exciting directions in application optimization we are currently pursuing.

We thought it would be interesting to perform a fresh comparison of BOLT overhead to Propeller [11]. Sadly, Propeller relies on a custom version of an external create-llvm-prof tool that we could not build in our setup, and using a GitHub-hosted binary version of that tool in the virtual environment produced a profile that caused a performance regression of the optimized application. Using "-fbasic-block-sections=all" Propeller option without a profile resulted in fast linking times but also caused a performance regression.

Although code reordering is the primary optimization in BOLT and the source of most performance gains, it is not the only optimization in BOLT. The full list of optimizations includes late inlining, indirect call promotion, frame optimizations, and others. The convenience of adding new optimizations in whole-program post-link mode is one of the main advantages that the BOLT framework offers, be it for research or practical purposes.

Additionally, BOLT can add new code to a linked ELF binary. We have recently used that capability to allocate frequently-executed code on huge pages automatically. Even legacy applications can now use 2MB pages for code on x86-64 Linux systems to reduce the number of iTLB misses.

BOLT's ability to process and optimize binary code without source code, such as legacy/distributed binaries, or code from third-party and assembly-written code, provides another advantage over a pure compiler-based approach. This advantage could or could not be important for optimizations depending on the user scenario. However, the visibility into the code mentioned above could be critical for other code re-writing cases such as vulnerability mitigations, instrumentation, and static analysis.

*) Support for input with split functions is in the works. Before it is completed, we ask not to use "-freorder-blocks-and-partition" compiler option during the build. A similar or better result will be achieved by running the BOLT function splitting pass.

**) while running BOLT with "-reorder-blocks=cache+ -reorder-functions=hfsort -split-functions=1 -split-eh" optimization options. DWARF update takes extra time and memory.

3.2 Advanced Disassembly

Internally, BOLT identifies code in the binary, breaks it into functions, disassembles, and uses static analysis to build a control flow graph. This functionality could complement a traditional disassembler, as it adds the ability to display possible targets for indirect jumps/calls, providing a better understanding of the control flow in a function.

Additionally, for performance analysis, the disassembly is annotated with execution counts, including those for indirect branch targets within a function (jump tables) and across functions (indirect and virtual function calls). E.g.:

  <Basic Block> .LFT35985

  Exec Count : 42

  Predecessors: .Ltmp935657

      00003c8b: leaq "JUMP_TABLE/foo/1.14"(%rip), %rcx

      00003c92: movslq (%rcx,%rax,4), %rax

      00003c96: addq %rcx, %rax

      00003c99: jmpq *%rax # JUMPTABLE @0x6e0f94

  Successors: .Ltmp935702 (count: 0, mispreds: 0),

              .Ltmp935705 (count: 41, mispreds: 26),

              .Ltmp935703 (count: 0, mispreds: 0),

              .Ltmp935704 (count: 1, mispreds: 0)

....

  <BasicBlock>.LFT43 (9 instructions, align : 1)

  Exec Count : 8

  Predecessors: .LBB01191

      00000028: movq %rdx, %rbx

      0000002b: leaq 0x20(%rsp), %rdi

      00000030: movl $0x2, %edx

      00000035: movq %rbx, %rsi

      00000038: callq *%rax # CallProfile: 8 (8 mispreds) :

              { f1: 3 (3 mispreds) },

              { f2: 1 (1 mispreds) },

              { f3: 4 (4 mispreds) }

      0000003a: movdqu 0x10(%rbx), %xmm0

      0000003f: movdqu %xmm0, 0x30(%rsp)

      00000045: movq %xmm0, %rcx

      0000004a: jmp .Ltmp26968

  Successors: .Ltmp26968 (count: 8, mispreds: 0)

With LLVM integration, the advanced disassembler can be added as a new standalone tool or as an extra feature to existing tools such as llvm-objdump.

3.3 Low-Level Program Instrumentation

Tools, such as memory sanitizers, rely on compiler-generated memory instrumentation to detect application errors at runtime. Suppose part of the application is written in assembly or comes from a library with no sources. In that case, such code could become a source of false positives and false negatives depending on the memory access types missed by the instrumentation. Since BOLT can instrument arbitrary code, independent of the source, it can complement compiler-based instrumentation and fill in the missing parts leading to a higher quality signal from the tool.

3.4 Static Analysis

BOLT offers an intuitive API to perform compiler-level analyses directly on machine code. Because BOLT reconstructs the control-flow graph of the program, it allows pass writers to extract arbitrary information beyond the scope of a single basic block with data-flow analyses. As an example, we can perform shrink wrapping by checking how stack-accessing instructions are using the frame in a given function and validating opportunities to move memory accesses in hot basic blocks to colder areas.

While this framework provides the optimization writer with greater reach over previously opaque external third-party binary code linked in the binary, perhaps the greatest value of this is in this code being visible at all. With static analysis, users can write passes to draw conclusions on safety concerns as well, such as checking if system or library calls are being used in a suspicious way without the need to execute the binary.

4. Plans

BOLT is a mature project that has been used in production for years, but we continue to develop BOLT and invest in new use-cases and optimizations. Below is a shortlist of areas that are currently under development:

  1.  Automatic profile collection and optimization
  2.  MachO support
  3.  LLD integration
  4.  Optimizing Linux kernel

--

BOLT Team

References

[1] Maksim Panchenko, Rafael Auler, Bill Nell, and Guilherme Ottoni. 2019. BOLT: a practical binary optimizer for data centers and beyond. In "Proceedings of the 2019 IEEE/ACM International Symposium on Code Generation and Optimization" (CGO 2019). IEEE Press, 2–14. https://research.fb.com/publications/bolt-a-practical-binary-optimizer-for-data-centers-and-beyond/

[2] https://lists.llvm.org/pipermail/llvm-dev/2019-September/135393.html

[3] Joe Savage and Timothy M. Jones. 2020. HALO: post-link heap-layout optimisation. In "Proceedings of the 18th ACM/IEEE International Symposium on Code Generation and Optimization" (CGO 2020). Association for Computing Machinery, New York, NY, USA, 94–106. DOI:https://doi.org/10.1145/3368826.3377914

[4] https://github.com/facebookincubator/BOLT/issues/46

[5] Maksim Panchenko. 2016. Building Binary Optimizer with LLVM. 2016 European LLVM Developers' Meeting. https://llvm.org/devmtg/2016-03/#presentation7

[6] https://github.com/facebookincubator/BOLT

[7] https://github.com/facebookincubator/BOLT/blob/master/llvm.patch

[8] perf: Linux profiling with performance counters. https://perf.wiki.kernel.org/index.php/Main_Page.

[9] Runtime Performance Optimization Blueprint: Intel® Architecture Optimizations with LBR. https://software.intel.com/content/www/us/en/develop/articles/runtime-optimization-blueprint-IA-optimization-with-last-branch-record.html<https://software.intel.com/content/www/us/en/develop/articles/runtime-optimization-blueprint-IA-optimization-with-last-branch-record.html>

[10] The HipHop Virtual Machine. https://hhvm.com/

[11] Propeller RFC. https://github.com/google/llvm-propeller/blob/plo-dev/Propeller_RFC.pdf. Updated performance results: https://lists.llvm.org/pipermail/llvm-dev/2019-October/136030.html

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