7 articles by Phil Hornshaw.
Hierarchical Interest Representation is a research area for Meta Ads. We’re exploring an upstream representation layer over the universe of Ads entities – users, advertisers, products, services – learning unified embeddings that connect users’ inferred interests with the breadth of what advertisers offer in their deep funnel ads. The innovations in Hierarchical Interest Representation are […] Read
TL; DR At Meta’s scale, a few milliseconds of latency degradation can have a significant negative impact on ads performance. When a Linux kernel upgrade risked regressing latency across Meta’s ad serving fleet, we turned to sched_ext — the upstream, BPF-based extensible scheduling framework — to build a scheduling policy customized to the Ads delivery […]
At Meta, we are constantly pushing the boundaries of LLM inference systems to power applications such as the Meta AI App. We’re sharing how we developed and implemented advanced parallelism techniques to optimize key performance metrics related to resource efficiency, throughput, and latency. The rapid evolution of large language models (LLMs) has ushered in a […]
We’re sharing details on our journey to scale Meta’s Backbone network to support the increasing demands of new and existing AI workloads. We’ve developed new technologies and designs to address our 10x scaling needs and applying some of these same principles to help extend our AI clusters between multiple data centers. Meta’s Backbone network is […]
Key Takeways: With billions of Android app users, we’re always looking to improve the Meta app experience, and in this post, we explore the ways we’ve leveraged Android’s Baseline Profiles to significantly improve their performance. We discuss the performance challenges we’ve faced as Meta’s apps, how the needs of users have become more complex over […]
The Kotlin incremental compiler has been a true gem for developers chasing faster compilation since its introduction in build tools. Now, we’re excited to bring its benefits to Buck2 – Meta’s build system – to unlock even more speed and efficiency for Kotlin developers. Unlike a traditional compiler that recompiles an entire module every time, […]
In this post, we explore the ways we’re evolving Meta’s data warehouse to facilitate productivity and security to serve both human users and AI agents. We detail how we’re developing agents that help users making data access requests to get to the data they need, and that help data owners process requests and maintain security. […]