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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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Beyond Data and Model Parallelism for Deep Neural Networks
Existing deep learning systems commonly parallelize deep neural network (DNN) training using data or model parallelism, but these strategies often result in suboptimal parallelization performance. We introduce SOAP, a more comprehensive search space of parallelization strategies for DNNs that includes strategies to parallelize a DNN in the Sample, Operator, Attribute, and Parameter dimensions. We present FlexFlow, a deep learning engine that uses guided randomized search of the SOAP space to find a fast parallelization strategy for a specific parallel machine. To accelerate this search, FlexFlow introduces a novel execution simulator that can accurately predict a parallelization strategy’s performance and is three orders of magnitude faster than prior approaches that execute each strategy. We evaluate FlexFlow with six real-world DNN benchmarks on two GPU clusters and show that FlexFlow increases training throughput by up to 3.3× over state-of-the-art approaches, even when including its search time, and also improves scalability.
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- Award ID(s):
- 1651570
- PAR ID:
- 10129644
- Date Published:
- Journal Name:
- SysML 2019
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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