Proactive Embedding on Cold Data for Deep Learning Recommendation Model Training

Abstract

Deep learning recommendation model (DLRM) is an important class of deep learning networks that are commonly used in many applications. DRLM presents unique challenges, especially for scale-out training since it not only has compute and memory-intensive components but the communication between the multiple GPUs is also on the critical path. In this work, we propose how cold data in DLRM embedding tables can be exploited to propose proactive embedding. In particular, proactive embedding allows embedding table accesses to be done in advance to reduce the impact of the memory access latency by overlapping the embedding access with communication. Our analysis of proactive embedding demonstrates that it can improve overall training performance by 46%.