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Compute per-tenant GPU-hours for a billing period from a log of allocation start and stop events.

The arithmetic is one multiplication and the difficulty is entirely in the events that do not pair cleanly. Clipping to the window, an allocation still running when the period ends, a stop with no start, and why every anomaly must be reported rather than dropped.

Updated Sep 2026 · Grounded in real AI infrastructure interview loops and written to a senior-engineer editorial bar, with every number worked and every diagram hand-built.

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The concepts behind this question

Ranked by how closely each one overlaps this question's topic, so the first card is the thing to read if the answer above moved too fast.

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Interval Merging and Utilization LogsGiven busy intervals per GPU, when was the whole cluster idle? What was the utilization per hour from a log of start and stop events? Which jobs overlapped? These are the interval problems of the infrastructure coding screen, and they share one tool: sort the endpoints and sweep. The sweep line turns every variant into a single pass with a counter, the sort is the only thing that costs more than linear time, and the edge cases (touching intervals, zero-length events, an unterminated start) are where candidates lose the round. This page works the standard problem and its relatives with code, tests and the complexity derivation.

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Batching Queues and BackpressureWrite a request batcher is the coding round's version of the serving engine's scheduler: requests arrive one at a time, the GPU wants them in groups, and the batcher decides when a group is full enough to send without holding anyone too long or accepting more than it can hold. The two knobs are the maximum batch size and the maximum wait, the invariant is a bounded queue, and the follow-ups (priorities, cost-aware batching, cancellation, bounded in-flight batches) are the ideas the real engines carry. This page implements the batcher in asyncio, derives what each knob buys, and walks the follow-ups.

Foundational

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The GPU Credit Scheduler PatternThe most widely reported coding problem in AI infrastructure loops is a small scheduler: accounts hold credits, jobs arrive with a cost and a priority, and you must decide which jobs run, in what order, without letting any account overspend, then extend it under follow-ups (refunds, reservations, concurrency limits, fairness). It is not a trick question; it is a test of whether you can model state cleanly, pick the right data structures, keep invariants under mutation, and talk about complexity while typing. This page works the problem from the first line to the fourth follow-up, with the code, the invariants, and the derivations.

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Retry, Backoff and IdempotencyA retry is a second request that the system did not budget for, and a thousand clients retrying at the same moment is a second outage that the first one caused. The craft is small and specific: retry only what is safe to retry, wait an exponentially growing random interval so the retries spread out, cap the total retries with a budget, and make every retried operation idempotent so a duplicate does not double-charge or double-train. This page derives why synchronized retries double the load, works the jitter arithmetic, implements the client correctly, and covers idempotency keys for the operations an AI platform exposes.

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Next in this trackImplement a circuit breaker in front of a model endpoint. What are the states, and what does the half-open state buy?Next in this trackGiven per-rank heartbeats from a training job, detect which rank has stopped and when. What produces false positives?Next in this trackWrite a thread-safe counter in C++ that many threads increment. What are the memory-ordering and layout choices?

FEDITOR'S NOTE

Scored on clipping intervals to the billing window rather than dropping them, on handling still-open allocations at the period end, and on reporting anomalies instead of silently skipping them.

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