Distributed Training Speedup Calculator

Work out distributed training speedup instantly with clear inputs, formula shown and shareable results.

About Distributed Training Speedup Calculator

Amdahl's law caps speedup at 1 / (s + p/N), so even a 92 percent parallel workload cannot exceed 12.5 times no matter how many devices you add. Real distributed training is worse still because gradient synchronisation grows with device count, and multiplying by that communication factor gives the efficiency figure that determines whether adding hardware is worth the money.

Formula

Amdahl with communication

S = 1 / (serial + parallel/N) divided by (1 + comm overhead x (N - 1)); efficiency = S / N

N: Devices
serial: Non-parallelisable share

Worked example

With the default values below, Distributed Training Speedup Calculator returns Realistic speedup of 4.641. Change any input above and every figure here is recalculated in your browser.

Inputs used

Devicesminimum 1: 8
Parallelisable share of the workaccepts 1–100 %: 92 %
Communication overhead per deviceaccepts 0–50 %: 1.5 %

Results

Realistic speedup: 4.641 x
Ideal linear speedup: 8 x
Scaling efficiency: 58.01%
Effective device count: 4.64

Step by step

1Values used
2Amdahl with communication
3Realistic speedup= 4.641 x
4Ideal linear speedup= 8 x
5Scaling efficiency= 58.01
6Effective device count= 4.64

Assumptions and limits

The result is derived only from the 3 inputs listed above. Anything not among them is not part of the calculation.
In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
Figures are rounded for display to 3 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

What efficiency should I expect?

Well-tuned data-parallel training holds 85 to 95 percent efficiency up to tens of GPUs on a fast interconnect. Below 70 percent, investigate the interconnect and the batch size per device.

How is communication overhead reduced?

Gradient bucketing overlapped with the backward pass, mixed-precision or compressed gradients, larger per-device batches, and topology-aware all-reduce.