Collective communication provides standard operations for moving or combining data across many participants. Artificial intelligence workloads use collectives to distribute parameters, aggregate gradients, exchange activations, and keep parallel computation synchronized.
Ordering matters because all participants must enter compatible operations in a consistent sequence. Mismatches can deadlock or produce incorrect results. Performance depends on message size, topology, algorithms, overlap, and whether the selected collective matches the workload's partitioning.
