TLDR Tech / Data
Optimizing Recommendation Systems with JDK's Vector API
Netflix significantly reduced CPU utilization for its Ranker service's serendipity scoring feature from 7.5% to about 1% per node by re-architecting its scoring logic. This involved moving from scalar dot products to batched, cache-friendly matrix multiplies with flat buffers, using the JDK Vector API for SIMD performance in pure Java, and eliminating unnecessary allocations. These changes resulted in a 7% CPU drop, 12% latency reduction, and a 10% improvement in CPU/RPS.
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