placement
AI infra interview questions tagged placement, across every topic.
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Concepts behind "placement"
The curriculum that explains the ideas these questions test.
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Topology-Aware CommunicationThe same collective can run at 900 GB/s or at 50 GB/s depending on which links it is laid across, so the mapping of parallel groups onto hardware is a performance decision, not a deployment detail. The rule: tensor-parallel groups inside the NVLink domain, data-parallel rings along rails, pipeline stages across the fabric, and every rank placed so its partner is one hop away. NCCL discovers the topology and does most of this when the job lets it; the failures come from placements that do not.🔌 Networking & Storage🔒 Premium
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Topology-Aware SchedulingTwo placements of the same 64-GPU job can differ by 2x in step time: one keeps every tensor-parallel group on a single NVSwitch node and every data-parallel ring on a single rail, the other scatters ranks across racks and pushes per-layer traffic through the spine. The scheduler is the only thing that can prevent the second placement, because the framework maps ranks to whatever GPUs it is handed. Topology-aware scheduling means the scheduler knows the hierarchy (NVLink domain, rail, rack, spine block) and places gangs to keep traffic low in it.🗂️ Scheduling & Orchestration🔒 Premium