Identifying Top-Scoring Arms in Ranking Bandits With Linear Payoffs in Real-Time

Pingora handles 35M+ requests per second, so saving a few microseconds per request can translate to thousands of dollars saved on computing costs. In this post, we share how we freed up over 500 CPU cores by optimizing one function and announce trie-hard, the open source crate that we created to do it.

A good day to trie-hard: saving compute 1% at a time

In this post, we discuss the performance optimizations we've implemented for our WAF ML product. We'll guide you through specific code examples and benchmark numbers, and we'll share the impressive latency reduction numbers observed after the rollout

Making WAF ML models go brrr: saving decades of processing time

How we killed SQL and built a machine learning model in its place

End-to-end Keyword Bidding for Apple Search Ads

As teams and applications experience growth, it’s critical to adopt architectures that optimize for clear code ownership, build isolation, and provide efficient delivery of code. While many projects start small with just one or two repositories (for example, frontend and backend), this approach often becomes difficult to maintain as the codebases expand. At LinkedIn, we develop many applications that receive regular contributions from a multitude of teams, with each team owning distinct products or features. Our infrastructure teams enable developers to work effectively within these […]

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Posts about #optimization from the top innovative companies, all in one place.