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Equation 30 · Part 1 · What an AI Accelerator Actually Is: Silicon, Packaging, and the Memory It Can Reach

Symbol T_allreduce

Tallreduce≈2 (p−1) α+2 p−1p⋅Nβlink,T_{\mathrm{allreduce}} \approx 2\,(p-1)\,\alpha + 2\,\frac{p-1}{p}\cdot\frac{N}{\beta_{\mathrm{link}}},
TallreduceT_{\mathrm{allreduce}}

What this part means

TaT_allreduce is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

Its job in the formula

TaT_allreduce is a part of this expression. Its role is fixed by the surrounding article and by the operations shown in the formula.

The passage around this formula

Their cost has a shape worth internalising. For the standard ring formulation of an all-reduce over p devices and N bytes, the operation decomposes into a reduce-scatter followed by an all-gather, each of p - 1 steps in which every device sends N/p bytes. Total time is approximately Tallreduce≈2 (p−1) α+2 p−1p⋅NβlinkT_{\mathrm{allreduce}} \approx 2\,(p-1)\,\alpha + 2\,\frac{p-1}{p}\cdot\frac{N}{\beta_{\mathrm{link}}}. with α\alpha the per-step latency and βlink\beta_{\mathrm{link}} the per-link bandwidth. The bandwidth term approaches 2N/βlink\beta_{\mathrm{link}} and stops growing with p ; the latency term grows linearly in p . Small, frequent collectives are therefore latency bound and scale badly, while large ones are bandwidth bound and scale well — which is precisely why gradient bucketing and overlapping…

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