# BOLT: A Practical Binary Optimizer for Data Centers and Beyond

arXiv: https://arxiv.org/abs/1807.06735 · HTML: https://mlsanity.com/paper/1807.06735/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2018.

## Built on by (most cited first, 4 of 4)

- [Profile Guided Optimization without Profiles: A Machine Learning Approach](https://mlsanity.com/paper/2112.14679/) (2021)
- [VEXIR2Vec: An Architecture-Neutral Embedding Framework for Binary Similarity](https://mlsanity.com/paper/2312.00507/) (2023)
- [AI-PROPELLER: Warehouse-Scale Interprocedural Code Layout Optimization with AlphaEvolve](https://mlsanity.com/paper/2606.00131/) (2026)
- [Learning Semantics, Not Addresses: Runtime Neural Prefetching for Far Memory](https://mlsanity.com/paper/2506.00384/) (2025)

## Abstract (arXiv)

Performance optimization for large-scale applications has recently become more important as computation continues to move towards data centers. Data-center applications are generally very large and complex, which makes code layout an important optimization to improve their performance. This has motivated recent investigation of practical techniques to improve code layout at both compile time and link time. Although post-link optimizers had some success in the past, no recent work has explored their benefits in the context of modern data-center applications. In this paper, we present BOLT, a post-link optimizer built on top of the LLVM framework. Utilizing sample-based profiling, BOLT boosts the performance of real-world applications even for highly optimized binaries built with both feedback-driven optimizations (FDO) and link-time optimizations (LTO). We demonstrate that post-link performance improvements are complementary to conventional compiler optimizations, even when the latter are done at a whole-program level and in the presence of profile information. We evaluated BOLT on both Facebook data-center workloads and open-source compilers. For data-center applications, BOLT achieves up to 8.0% performance speedups on top of profile-guided function reordering and LTO. For the GCC and Clang compilers, our evaluation shows that BOLT speeds up their binaries by up to 20.4% on top of FDO and LTO, and up to 52.1% if the binaries are built without FDO and LTO.