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This paper proposes TRAININGCXL that can efficiently process large-scalerecommendation datasets in the pool of disaggregated memory while makingtraining fault tolerant with low overhead. To this end, i) we integratepersistent memory (PMEM) and GPU into a cache-coherent domain as Type-2.Enabling CXL allows PMEM to be directly placed in GPU's memory hierarchy, suchthat GPU can access PMEM without software intervention. TRAININGCXL introducescomputing and checkpointing logic near the CXL controller, thereby trainingdata and managing persistency in an active manner. Considering PMEM'svulnerability, ii) we utilize the unique characteristics of recommendationmodels and take the checkpointing overhead off the critical path of theirtraining. Lastly, iii) TRAININGCXL employs an advanced checkpointing techniquethat relaxes the updating sequence of model parameters and embeddings acrosstraining batches. The evaluation shows that TRAININGCXL achieves 5.2x trainingperformance improvement and 76% energy savings, compared to the modernPMEM-based recommendation systems.

Failure Tolerant Training with Persistent Memory Disaggregation over CXL