Like MPI collective operations, NCCL collective operations have to be called for each rank (hence CUDA device) to form a complete collective operation. Failure to do so will result in other ranks waiting indefinitely.
The AllReduce operation is performing reductions on data (for example, sum, max) across devices and writing the result in the receive buffers of every rank.
The AllReduce operation is rank-agnostic. Any reordering of the ranks will not affect the outcome of the operations.
AllReduce starts with independent arrays Vk of N values on each of K ranks and ends with identical arrays S of N values, where S[i] = V0[i]+V1[i]+…+Vk-1[i], for each rank k.
The Broadcast operation copies an N-element buffer on the root rank to all ranks.
Important note: The root argument is one of the ranks, not a device number, and is therefore impacted by a different rank to device mapping.
The Reduce operation is performing the same operation as AllReduce, but writes the result only in the receive buffers of a specified root rank.
Important note : The root argument is one of the ranks (not a device number), and is therefore impacted by a different rank to device mapping.
Note: A Reduce, followed by a Broadcast, is equivalent to the AllReduce operation.
In the AllGather operation, each of the K processors aggregates N values from every processor into an output of dimension K*N. The output is ordered by rank index.
The AllGather operation is impacted by a different rank or device mapping since the ranks determine the data layout.
Note: Executing ReduceScatter, followed by AllGather, is equivalent to the AllReduce operation.
The ReduceScatter operation performs the same operation as the Reduce operation, except the result is scattered in equal blocks among ranks, each rank getting a chunk of data based on its rank index.
The ReduceScatter operation is impacted by a different rank or device mapping since the ranks determine the data layout.