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

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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}}},

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This equation gives an approximation: it relates the quantities while allowing an approximation. Read the equation part by part below; each part has a contextual explanation and a link to its mathematical background.

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TallreduceT_{\mathrm{allreduce}}

Symbol T_allreduce

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

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pp

Symbol p

the latency term grows linearly in.

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α\alpha

Symbol α

the per-step latency and βlink\beta_{\mathrm{link}} the per-link bandwidth.

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NN

Symbol N

the number of bytes.

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βlink\beta_{\mathrm{link}}

Symbol beta_link

the per-link bandwidth.

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fraction

fraction

Divide the expression above the line by the one below it.

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≈

≈

Approximately equal to; the equality is not exact.

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multiplication

multiplication

Multiply the quantities on either side.

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addition

addition

Add the term after the plus sign to the term or group before it.

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subtraction

subtraction

Subtract the following term or group from the preceding one. A leading minus marks a negative quantity.

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subscript

subscript

The lower label selects a particular version, component, or indexed member of the quantity. For example, x₀ and xₜ can be values at different positions.

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p−1p-1

Numerator: p-1

The complete quantity above the fraction bar.

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How to interpret it

With a fixed numerator, increasing a nonzero denominator reduces the fraction. Its accuracy depends on the assumptions and range of use described in the article.

What the article says around this equation

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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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 communication with backward computation are standard practice rather than optimisations.

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