Wax: Optimizing Data Center Applications With Stale Profile

Data center applications' large instruction footprints cause frequent front-end stalls by overwhelming on-chip micro-architectural structures such as instruction cache (I-cache), instruction translation look-aside buffer (iTLB), and branch target buffer (BTB). To reduce pressure on these structures, data center providers leverage profile-guided optimizations by reordering binary layout along a relatively small number of hot code paths. Such reordering provides the highest benefit if profile collection and optimization happen on the same version of the binary. In practice, companies have to optimize and deploy a fresh version of the binary with a profile from a previous version, making a large fraction of the profile stale. In this paper, we propose Wax. We open source our work at https://github.com/ice-rlab/wax, a novel technique to optimize data center applications with stale profiles. Wax's key insight is to leverage the debug and source code information while optimizing fresh binaries with stale profiles. We evaluate Wax for 5 data center applications to show that Wax provides significant (5.76%-26.46%) performance speedups. Wax achieves 1.20%-7.86% greater speedups than the state of the art, obtaining 65%-93% of fresh profiles' benefits.