Gradient buckets: overlap follows a dependency schedule

A bucket groups gradient storage into a communication unit. Its reduction becomes eligible when the required gradients are ready according to the reducer’s bookkeeping. An earlier layer in the forward graph often has a later gradient in backward, so parameter layout and runtime dependency order affect when communication can begin. Smaller buckets may expose earlier opportunities but pay more per-collective overhead. Larger buckets amortize overhead but can wait for a late gradient that delays useful bytes already produced. There is no universally optimal size independent of graph and interconnect.

Ranks cannot independently reduce buckets in whichever local order looks ready. The DDP reducer maintains a compatible bucket-index collective order so that peers match the same logical operations, even if local autograd readiness timing differs. Parameter registration, shapes, and relevant graph behavior must be compatible across ranks. Unused-parameter handling has its own configuration assumptions. A rank-dependent branch that changes which reductions occur is a correctness concern before it is an overlap concern; a fast local trace does not rescue a mismatched global protocol.

Launching a collective asynchronously permits the host or another stream to continue suitable work. It does not make its result available immediately or permit a gradient buffer to be reused while communication still reads it. The optimizer needs completion dependencies before consuming the reduced gradients. Gradient-as-bucket-view configurations introduce storage aliasing that also affects safe mutation and clearing. Inspect the actual framework contract rather than infer lifetime from one Python function returning. Timing only the issue call can measure enqueue overhead while excluding the communication tail.

An ideal ring all-reduce model separates latency rounds from transferred payload, but it does not model every implementation or topology. Its bandwidth term depends on the full bucket size per rank and the participant count. Small buckets can become latency dominated; large ones may become bandwidth dominated while reducing overlap opportunities. Measure exposed step time and the end of backward, not merely an isolated collective bandwidth number. A schedule can communicate efficiently in isolation and still leave a large unhidden final bucket on the training critical path.

Record gradient-ready events, collective issue and completion, backward completion, and optimizer start for matched iterations after warmup. Identify which rank defines global readiness and whether data loading or earlier collectives are delaying it. Compare a controlled bucket-size change while preserving the objective, parameters, input batch, and optimizer behavior. An overlap visualization can look busier while increasing total step time through contention or extra startup costs. Check correctness first and interpret the final exposed tail with the same schedule assumptions used in the arithmetic.

The queue lab provides only an average readiness-pressure analogy. Reset, explicitly set arrival two and service three, predict drainage from four initial items, then Play or Step. Compare an independent Reset with both rates set to four and three before predicting growth. Control edits retain elapsed time and recompute the initial-value formula. There are no bucket identities, autograd dependencies, collective ordering, communication streams, or tensor lifetimes in this fluid model; the actual per-bucket trace is required to validate overlap.