NVREC: A High-Performance Recoverable Recommendation System for Non-Volatile Memory

Current recommendation systems are designed with the assumption that computer memory is volatile. Thus, they periodically execute checkpointing operations to save model weights and embeddings to slower solid-state drives (SSDs) for failure recovery. Such designs cannot leverage the unique characteristics of non-volatile memory (NVM). This paper proposes a new recommendation model training framework called NVrec , which uses a hybrid memory system consisting of DRAM and NVM to support computing and in-memory checkpointing. NVrec proposes three recommendation-oriented techniques. (1) It partitions embeddings so that only popular vectors are placed in DRAM to hide NVM-induced latency. (2) It maintains a dual-version scheme for in-memory checkpointing: each vector in NVM alternates between a persistent version for crash recovery and a runtime version for training updates. (3) It employs fine-grained parallelism to hide the checkpointing overhead of model parameters in DRAM and GPU. Our experiments demonstrate that NVrec improves recommendation-model training throughput by 1.05 × -6.85 × and the checkpointing process by more than 5 × compared to existing models when evaluated on the synthetic and real-world datasets.