Efficiently Process Large RM Datasets In Underlying Memory Pool, Disaggregated Over CXL (KAIST)

A technical paper titled "Failure Tolerant Training with Persistent Memory Disaggregation over CXL" was published (preprint) by researchers at KAIST and Panmnesia. "TRAININGCXL can efficiently process large-scale recommendation datasets in the pool of disaggregated memory while making training fault tolerant with low overhead," states the paper. Find the technical paper here. or here (IEE... » read more