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Rethinking Selective In-Network Aggregation for Multi-Tenant Learning
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DOI：10.1109/TON.2025.3617097.png)
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Abstract
En 中文
In-network aggregation accelerates distributed training by offloading gradient aggregation to the programmable switches. However, in multi-tenant learning environments, contention for limited switch memory can cause memory overflows that significantly degrade aggregation throughput. To mitigate memory overflow, prior work has proposed selective in-network aggregation, which allocates switch memory to a subset of jobs based on memory availability. This approach classifies jobs into INA jobs (which perform in-network aggregation) and PS jobs (which perform in-server aggregation). Despite these efforts, network congestion occurs and slows INA jobs, resulting in inefficient memory utilization and reduced aggregation throughput. In this paper, we present FlexINA, which rethinks selective in-network aggregation to deliver high aggregation throughput. The first challenge is how to avoid excessive memory under-utilization by INA jobs during network congestion. FlexINA introduces INA-aware congestion control, prioritizing reducing the sending window of PS jobs during network congestion. The second challenge is how to allow PS jobs to utilize under-utilized aggregators without affecting the aggregation of INA jobs. FlexINA implements an adaptive head-tail aggregation, optimizing memory usage by combining static head mapping for INA jobs (to use allocated head memory) and dynamic tail mapping for PS jobs (to use under-utilized tail memory). We implement a FlexINA prototype and evaluate it on both a small-scale testbed and in large-scale simulation experiments. Our evaluation shows that FlexINA improves aggregation throughput by up to $1.9\times $ and $1.4\times $ compared to selective in-network aggregation NetPack (ATP) and NetPack (A2TP), respectively.
Keywords:
In-network aggregation
congestion control
memory management