Sophisticated Orchestrating Concurrent DLRM Training on CPU/GPU Platform

Abstract

Recommendation systems are essential to the operation of the majority of internet services, with Deep Learning Recommendation Models (DLRMs) serving as a crucial component. However, due to distinct computation, data access, and memory usage characteristics of recommendation models, the trainning of DLRMs may suffer from low resource utilization on prevalent heterogeneous CPU-GPU hardware platforms. Furthermore, as the majority of high-performance computing systems presently depend on multi-GPU computing nodes, the challenge of addressing low resource utilization becomes even more pronounced. Existing concurrent training solutions cannot be straightforwardly applied to DLRM due to various factors, such as insufficient fine-grained memory management and the lack of collaborative CPU-GPU scheduling. In this paper, we introduce RMixer, a scheduling framework that addresses these challenges by providing an efficient job management and scheduling mechanism for DLRM training jobs on heterogeneous CPU-GPU platforms. To facilitate training co-location, we first estimate the peak memory consumption of each job. Additionally, we track and collect resource utilization for DLRM training jobs. Based on the information of computational patterns, a batched job dispatcher with dynamic resource-complementary scheduling policy is proposed to co-locate DLRM training jobs on CPU-GPU platform. Scheduling strategies for both intra-GPU and inter-GPU scenarios were meticulously devised, with a focus on thoroughly examining individual GPU resource utilization and achieving a balanced state across multiple GPUs. Experimental results demonstrate that our implementation achieved up to 5.3× and 7.5× higher throughput on single GPU and 4 GPU respectively for training jobs involving various recommendation models.