Question

In a distributed training environment, which NCCL primitive is required to combine gradient updates from all GPUs into a single synchronized result across the cluster?

Accepted Answer

The NCCL primitive required to combine gradient updates from all GPUs into a single synchronized result is AllReduce. In distributed training, each GPU calculates a portion of the total gradient based on its specific batch of data. Because every GPU needs the complete gradient to update its model parameters identically, the AllReduce operation is performed. This operation functions in two steps: it first performs a reduction, such as summation, to aggregate all the individual gradient values from every GPU across the cluster, and then it broadcasts that final summed result back to every participating GPU. By the end of the AllReduce operation, every GPU possesses the exact same aggregated gradient, ensuring the model weights stay synchronized across the entire distributed system.

In a distributed training environment, which NCCL primitive is required to combine gradient updates from all GPUs into a single synchronized result across the cluster?

The NCCL primitive required to combine gradient updates from all GPUs into a single synchronized result is AllReduce. In distributed training, each GPU calculates a portion of the total gradient based on its specific batch of data. Becau....

Community Answers

Home → All Courses → Engineering and Technology Courses → High-Performance GPU Programming for AI Workloads → Flashcard

In a distributed training environment, which NCCL primitive is required to combine gradient updates from all GPUs into a single synchronized result across the cluster?

The NCCL primitive required to combine gradient updates from all GPUs into a single synchronized result is AllReduce. In distributed training, each GPU calculates a portion of the total gradient based on its specific batch of data. Becau....

Community Answers