LP Planner: Synchronization-Aware Sharding Optimization for Large-Scale DLRM Model Training#

Mine Su Erturk, Greg Macnamara, Caner Gocmen, Isuru Janith Ranawaka, Felicity Liao, Ahmed Shuaibi, Alireza Tehrani, Hammad Ather, Shafeeq Ibraheem, Kaustubh Vartak, Nipun Gupta (Meta)

• Why: Sharding embedded-table models across devices is one of the hardest practical problems in large-scale distributed training, and this is a deployed optimizer with production numbers on some of Meta's highest-spend models — exactly the kind of firmly substantiated result worth a PM's attention.
• What: A tool that automatically generates DLRM sharding plans, deployed on multiple production models including some of the largest by training spend, delivering up to 8% QPS improvement and 14% peak memory reduction per model launch (from abstract only).
• How: The sharding problem is formulated as a linear program that jointly optimizes table placement and partitioning strategy across devices and device memory hierarchies, minimizing peak memory while maximizing training throughput; the "synchronization-aware" part accounts for cross-device sync costs in the objective. It is also integrated into agentic auto-tuning pipelines.
• Where applicable: DLRM/embedding-table specific — directly relevant if you run recommendation-model training; less so for dense LLM training. Ongoing work (critical-path modeling, shard-type selection, heterogeneous hardware awareness) indicates current limitations. Production numbers are Meta's.