Zheng Wang, University of California, San Diego; Yuke Wang, Boyuan Feng, and Guyue Huang, University of California, Santa Barbara; Dheevatsa Mudigere and Bharath Muthiah, Meta; Ang Li, Pacific Northwest National Laboratory; Yufei Ding, University of California, San Diego
The deployment of Deep Learning Recommendation Models (DLRMs) involves the parallelization of extra-large embedding tables (EMTs) on multiple GPUs. Existing works overlook the input-dependent behavior of EMTs and parallelize them in a coarse-grained manner, resulting in unbalanced workload distribution and inter-GPU communication.
To this end, we propose OPER, an algorithm-system co-design with OPtimality-guided Embedding table parallelization for large-scale Recommendation model training and inference. The core idea of OPER is to explore the connection between DLRM inputs and the efficiency of distributed EMTs, aiming to provide a near-optimal parallelization strategy for EMTs. Specifically, we conduct an in-depth analysis of various types of EMTs parallelism and propose a heuristic search algorithm to efficiently approximate an empirically near-optimal EMT parallelization. Furthermore, we implement a distributed shared memory-based system, which supports the lightweight but complex computation and communication pattern of fine-grained EMT parallelization, effectively converting theoretical improvements into real speedups. Extensive evaluation shows that OPER achieves 2.3× and 4.0× speedup on average in training and inference, respectively, over state-of-the-art DLRM frameworks.
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author = {Zheng Wang and Yuke Wang and Boyuan Feng and Guyue Huang and Dheevatsa Mudigere and Bharath Muthiah and Ang Li and Yufei Ding},
title = {{OPER}: {Optimality-Guided} Embedding Table Parallelization for Large-scale Recommendation Model},
booktitle = {2024 USENIX Annual Technical Conference (USENIX ATC 24)},
year = {2024},
isbn = {978-1-939133-41-0},
address = {Santa Clara, CA},
pages = {667--682},
url = {https://www.usenix.org/conference/atc24/presentation/wang},
publisher = {USENIX Association},
month = jul
}