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Implement dominant resource fairness for a cluster where jobs need different mixes of GPU, CPU and memory.

Allocate tasks by current dominant share and distinguish per-user fairness from cluster feasibility and physical placement.

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Allocate tasks by current dominant share and distinguish per-user fairness from cluster feasibility and physical placement.

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The concepts behind this question

Use these concept explanations to review the ideas behind this question.

Advanced
🗂️ Scheduling & Orchestration🔒 Premium
Multi-Tenancy, Quotas and Fair ShareFollow three teams through GPU borrowing and reclaim. Distinguish quotas, reservations and fair sharing; see why a returning team may leave another above its nominal share, and why failures or indivisible jobs change what can be promised.
Core
📐 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.
Advanced
⚡ Kernels & Compilers🔒 Premium
GEMM Work Partitioning and Wave QuantizationOutput tiles determine how much independent matrix work is available. Split-K and work-centric schedules can reduce an underfilled final wave, while adding partial-result reduction and workspace costs.
Core
🚀 Inference & ServingSign in
Continuous BatchingContinuous batching changes which requests run between model iterations. Learn how a waiting request joins, why an empty slot is not enough, and how to balance throughput against each user’s token latency.
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UP NEXT ON YOUR JOURNEY
Next in this trackWrite a tiled matrix multiply and explain the cache effect. Then say honestly what your benchmark actually measured.Next in this trackImplement a log-structured event store for fleet metrics with range queries and compaction.Next in this trackA multi-GPU training job hangs at step 400 with every GPU at 100 percent utilization. Debug it.
FEDITOR'S NOTE

Look for exact normalized shares, valid demand vectors, executable allocations and qualified theoretical guarantees.

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