Publication

Lightning BOLT: Powerful, Fast, and Scalable Binary Optimization

International Conference on Compiler Construction (CC)


Abstract

Profile-guided binary optimization has proved to be an important technology to achieve peak performance, particularly for large-scale binaries that are typical for data-center applications. By applying the profile data at the same representation where sampling-based profiling is collected, binary optimizers can provide double-digit speedups over binaries compiled with profile-guided optimizations using similarly collected profile data. The main blocker for adoption of binary optimizers in practice is the overhead that they add to the already long and demanding build pipelines used for producing highly optimized binaries, which already include aggressive compiler optimizations guided by profile data and also link-time optimizations. This paper addresses the overheads of binary optimizers in the context of BOLT, a modern and powerful open-source binary optimizer. More specifically, this paper describes Lightning BOLT, which is an improved version of the BOLT binary optimizer that drastically reduces BOLT’s processing time and memory requirements, while preserving BOLT’s effectiveness in improving the final binary’s performance. Using a set of real-world data-center and open-source applications, we show that Lightning BOLT speeds up BOLT’s processing by an average of 4.71× and reduces BOLT’s memory consumption by 70.5% on average. Furthermore, Lightning BOLT also provides an adjustable mechanism to further reduce BOLT’s overheads at the cost of some lost performance for the final binary.

Related Publications

All Publications

HPCA - March 3, 2021

Heterogeneous Dataflow Accelerators for Multi-DNN Workloads

Hyoukjun Kwon, Liangzhen La, Michael Pellauer, Tushar Krishna, Yu-Hsin Chen, Vikas Chandra

MLSys - April 8, 2021

CPR: Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery

Kiwan Maeng, Shivam Bharuka, Isabel Gao, Mark C. Jeffrey, Vikram Saraph, Bor-Yiing Su, Caroline Trippel, Jiyan Yang, Mike Rabbat, Brandon Lucia, Carole-Jean Wu

TSE - January 1, 2020

Approximate Oracles and Synergy in Software Energy Search Spaces

Bobby R. Bruce, Justyna Petke, Mark Harman, Earl T. Barr

OOPSLA - October 25, 2019

Getafix: Learning to Fix Bugs Automatically

Johannes Bader, Andrew Scott, Michael Pradel, Satish Chandra

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy