🤖 AI Summary
As AI training scales from a single GPU to hundreds or thousands, the dominant engineering problem shifts from compute to communication: networks become the bottleneck. Distributed training produces petabytes of gradient traffic and relies on collective operations (all-reduce, all-gather, broadcast) thousands of times per run, so fluctuating link bandwidth, transient failures, time-varying workloads and topology-induced hotspots can throttle otherwise well-optimized kernels. Multiple parallelism schemes (data, tensor, expert, pipeline) create distinct, often conflicting, communication patterns, and common topologies (ring, tree, fat‑tree) each trade off latency, bandwidth distribution and failure modes—meaning performance is no longer deterministic but a function of continual adaptation and co-dependence between algorithms and infrastructure.
COSMIC—which the class highlighted—advocates full-stack co-design: jointly optimize workload mapping, parallelism strategy and network topology rather than treating them as independent layers. By searching across choices (e.g., how much data vs. model parallelism, optimal collective algorithms, and network layout), COSMIC exposes cross-layer optimizations that hardware- or software-only approaches miss, yielding measurable efficiency and cost gains at warehouse scale. The takeaway for ML engineers: at cluster scale, algorithm, placement and network must be co-optimized (though costly), and production systems require continuous, learned adaptation to dynamic network conditions rather than one-time tuning.
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