  This paper proposes TRAININGCXL that can efficiently process large-scale
recommendation datasets in the pool of disaggregated memory while making
training fault tolerant with low overhead. To this end, i) we integrate
persistent 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, such
that GPU can access PMEM without software intervention. TRAININGCXL introduces
computing and checkpointing logic near the CXL controller, thereby training
data and managing persistency in an active manner. Considering PMEM's
vulnerability, ii) we utilize the unique characteristics of recommendation
models and take the checkpointing overhead off the critical path of their
training. Lastly, iii) TRAININGCXL employs an advanced checkpointing technique
that relaxes the updating sequence of model parameters and embeddings across
training batches. The evaluation shows that TRAININGCXL achieves 5.2x training
performance improvement and 76% energy savings, compared to the modern
PMEM-based recommendation systems.
