In-Network Aggregation with Transport Layer Transparency for Distributed Training


Recent In-Network Aggregation (INA) solutions offload the all-reduce operation onto network switches to accelerate and scale distributed training (DT). On end hosts, these solutions build custom network stacks to replace the transport layer. The INA-oriented network stack cannot take advantage of the state-of-the-art performant transport layer implementation, and also causes complexity in system development and operation.

We design a transport-transparent INA primitive named NetReduce for modern multi-rack data centers. NetReduce runs beneath the transport layer. The switch performs aggregation operations but preserves data transmission connections. The host uses RoCE as its transport layer to deliver gradient messages and receive aggregation results. NetReduce achieves performance gains from both INA and RoCE: linear scalability, traffic reduction, and bandwidth freeing-up from INA — high throughput, low latency, and low CPU overhead from RoCE. For jobs spanning several multi-GPU machines, we also devise parallel all-reduce based on NetReduce to make use of intra-machine and inter-machine bandwidth efficiently. We prototype NetReduce on an FPGA board attached to an Ethernet switch. We compare NetReduce with existing programmable switch-based solutions and justify the FPGA-based design choice. We evaluate NetReduce’s performance by training typical Deep Neural Network models on single-GPU and multi-GPU testbeds. NetReduce inter-operates with the existing Ethernet transport layer, is training-framework friendly, accelerates network-intensive DT jobs effectively (e.g., 70% for AlexNet), reduces CPU overheads (e.g., only one core for transmission), and is cost-effective (e.g., only 2.40% more capital expense and 0.68% more power consumption making 12.3-57.9% more performance acceleration).

Proceedings of ASPLOS'23