MARS: Exploiting Multi-Level Parallelism for DNN Workloads on Adaptive Multi-Accelerator Systems

• URL: http://arxiv.org/abs/2307.12234v1
• Date: Sun, 23 Jul 2023 05:50:37 GMT
• Title: MARS: Exploiting Multi-Level Parallelism for DNN Workloads on Adaptive Multi-Accelerator Systems
• Authors: Guan Shen, Jieru Zhao, Zeke Wang, Zhe Lin, Wenchao Ding, Chentao Wu, Quan Chen, Minyi Guo
• Abstract summary: We propose a novel mapping framework that can perform computation-aware accelerator selection and apply communication-aware sharding strategies to maximize parallelism. We show that MARS can achieve 32.2% latency reduction on average for typical DNN workloads compared to the baseline, and 59.4% latency reduction on heterogeneous models compared to the corresponding state-of-the-art method.
• Score: 27.490645446510033
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Along with the fast evolution of deep neural networks, the hardware system is also developing rapidly. As a promising solution achieving high scalability and low manufacturing cost, multi-accelerator systems widely exist in data centers, cloud platforms, and SoCs. Thus, a challenging problem arises in multi-accelerator systems: selecting a proper combination of accelerators from available designs and searching for efficient DNN mapping strategies. To this end, we propose MARS, a novel mapping framework that can perform computation-aware accelerator selection, and apply communication-aware sharding strategies to maximize parallelism. Experimental results show that MARS can achieve 32.2% latency reduction on average for typical DNN workloads compared to the baseline, and 59.4% latency reduction on heterogeneous models compared to the corresponding state-of-the-art method.

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