AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
The growth of distributed training models, with parameters now reaching the billion-scale, has shifted the system bottleneck from computation to communication. While Remote Direct Memory Access (RDMA) is widely deployed to improve network performance by circumventing the kernel mechanism, the synchronization-computatio...
The extreme collective completion time (CCT) demands of AI workloads challenge existing packet spraying algorithms, which can have trouble efficiently load-balancing workloads that are sent at full line rates. We trace this to a structural cause: on a fat tree, once a packet picks its upward path, the downward path to...
This paper builds a system, REACT, that tunes the recurring pattern of data exchange between GPU nodes (known as communication collectives) in response to congestion, and improves communication performance by 13%-38% under network congestion.
In data centers, large-scale many-to-one traffic can rapidly exhaust switch buffers and trigger priority-based flow control (PFC) pause, resulting in increased flow completion time (FCT) for uncongested flows. To address this issue, we propose an innovative switch-side fast and accurate flow control (FAFC) scheme. By d...
AI clusters increasingly run large language model (LLM) inference and training on the same fabric. Prefill-decode (P-D) disaggregation creates key-value (KV) cache transfers between prefill and decode groups, whereas training collectives and all-to-all traffic benefit from near-uniform global connectivity. A static spa...
The digital transformation of education introduces high-concurrency events (e.g., course registration, online exams) that cause sudden traffic surges in campus optical networks, leading to severe packet loss and delay jitter. Traditional static bandwidth allocation or reactive dynamic bandwidth allocation (DBA) fails t...
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