Optimizing Recommendation Systems with JDK's Vector API
3 days ago
- #Performance Optimization
- #Recommendation Systems
- #Java Vector API
- Netflix's Ranker service uses video serendipity scoring to personalize recommendations by comparing new titles to a user's viewing history.
- The original implementation had high CPU usage (7.5% per node) due to sequential cosine similarity calculations between candidate and history embeddings.
- Optimizations included batching computations into matrix operations, improving memory layout with flat buffers, and reusing ThreadLocal buffers to reduce allocations.
- Initial attempts with BLAS libraries showed limited gains due to overheads, leading to adoption of JDK's Vector API for SIMD-optimized matrix multiplication in pure Java.
- Final optimizations reduced CPU usage by ~7%, latency by ~12%, and improved CPU/RPS by ~10%, making the service more efficient without sacrificing performance.