NCCL All-Reduce
Definition
All-Reduce aggregates distributed values and delivers the result to all participants.
In NCCL, a reduction operation combines the data of all participating GPU processes, for example as a sum. Afterwards, each participating process has the total result.
Simply explained
All teams report numbers, the common sum is returned to each team.
Why it matters in practice
During training, gradients can be synchronized between devices this way.
Example from business
Hypothetical example: Four GPUs sum their computed gradients before the next joint step.
Limits and pitfalls
- All participants must use matching data types, quantities, and calls. Communication costs can dominate.
Origin & history
All-Reduce originates from parallel data processing; NCCL optimizes the operation for GPUs.
Use in mid-sized companies
- SMEs mainly encounter it in distributed training and its performance analysis.
Frequently asked questions
Does All-Reduce always compute an average?
No, the reduction operation is fixed; averaging may require additional steps.
Sources
- Collective Operations — · NVIDIA: Collective Operations —
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