NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining
Modern recommendation models have increased to trillions of parameters. As cluster scales expand to O(1k), distributed training bottlenecks shift from computation and memory to data movement, especially lookup and communication latency associated with embeddings. Existing solutions either optimize only one bottleneck or improve throughput by sacrificing training consistency. This paper presents NestPipe, a large-scale decentralized embedding training framework that tackles both bottlenecks while preserving synchronous training semantics. NestPipe exploits two hierarchical sparse parallelism opportunities through nested pipelining. At the inter-batch level, Dual-Buffer Pipelining (DBP) con...
| Authors | Zhida Jiang, Zhaolong Xing, Huichao Chai et al. |
|---|---|
| Primary category | cs.DC |
| Published | 8 April 2026 |
| Last updated at source | 8 April 2026 |
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