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arXiv · 2110.03214

MAPA: Multi-Accelerator Pattern Allocation Policy for Multi-Tenant GPU Servers

Kiran Ranganath·Joshua D. Suetterlein·Joseph B. Manzano·Shuaiwen Leon Song·Daniel Wong
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Abstract

Multi-accelerator servers are increasingly being deployed in shared multi-tenant environments (such as in cloud data centers) in order to meet the demands of large-scale compute-intensive workloads. In addition, these accelerators are increasingly being inter-connected in complex topologies and workloads are exhibiting a wider variety of inter-accelerator communication patterns. However, existing allocation policies are ill-suited for these emerging use-cases. Specifically, this work identifies that multi-accelerator workloads are commonly fragmented leading to reduced bandwidth and increased latency for inter-accelerator communication. We propose Multi-Accelerator Pattern Allocation (MAPA), a graph pattern mining approach towards providing generalized allocation support for allocating multi-accelerator workloads on multi-accelerator servers. We demonstrate that MAPA is able to improve the execution time of multi-accelerator workloads and that MAPA is able to provide generalized benefits across various accelerator topologies. Finally, we demonstrate a speedup of 12.4% for 75th percentile of jobs with the worst case execution time reduced by up to 35% against baseline policy using MAPA.

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Kiran Ranganath, Joshua D. Suetterlein, Joseph B. Manzano, Shuaiwen Leon Song, Daniel Wong. 2021-10-07. MAPA: Multi-Accelerator Pattern Allocation Policy for Multi-Tenant GPU Servers. https://doi.org/10.1145/3458817.3480853

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