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Place GPU jobs onto nodes. Compare first fit, best fit and worst fit, and say which one a training cluster wants.

Compare three GPU placement heuristics with executable traces. Show how admission and free-space shape change, then add workload and topology constraints.

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Compare three GPU placement heuristics with executable traces. Show how admission and free-space shape change, then add workload and topology constraints.

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The concepts behind this question

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📐 AI Systems DesignSign in

GPU Job Scheduler DesignA GPU scheduler decides which jobs run, where they run and when resources can be reclaimed. Its placement rules must account for whole jobs, topology and tenant policy.

🗂️ Scheduling & Orchestration🔒 Premium

Topology-Aware SchedulingTopology-aware scheduling places jobs with the communication network in mind. The right GPU count is not enough when ranks share slow or congested paths.

🔌 Networking & Storage🔒 Premium

Topology-Aware CommunicationMap communication groups onto real GPU and network paths. A four-rank example shows how two tensor-parallel streams share one adapter, why per-rank bandwidth can mislead, and when locality is worth waiting for.

🚀 Inference & Serving🔒 Premium

PagedAttentionFollow a token position through a block table, watch a cache grow, and trace a shared tail through copy-on-write. Separate memory saved from throughput gained.

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Next in this trackTokens arrive as byte fragments and a character can span several. Write the decoder that streams text correctly.Next in this trackSimulate a ring all-reduce on arrays. Verify both the result and the bytes each rank sends.Next in this trackImplement the block allocator behind a paged KV cache, including prefix sharing and copy-on-write.

FEDITOR'S NOTE

Look for correct node selection, valid inputs, reproducible traces and placement metrics matched to the requested job shape.

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