What is Gradient Synchronization?

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Gradient synchronization is required when training on multiple GPUs or machines. Each worker computes gradients on its data subset, and synchronization ensures all workers update their model weights consistently. Without synchronization, workers diverge and the model fails to converge. You need synchronization for data-parallel training where you split batches across GPUs, and for model-parallel training where different model parts run on different GPUs. Single-GPU training doesn't need synchronization.

Use synchronous for most applications since it produces identical results to single-GPU training and is simpler to debug. Asynchronous allows workers to update independently, improving hardware utilization but potentially degrading convergence quality due to stale gradients. Synchronous training scales efficiently up to 8-16 GPUs for most models. Consider asynchronous only when scaling beyond 16 GPUs or when worker speeds vary significantly due to heterogeneous hardware. Most ML frameworks default to synchronous for good reason.

Use gradient compression to reduce communication volume by 10-100x. Overlap computation with communication by starting gradient transfer for completed layers while other layers are still computing. Use NCCL for multi-GPU communication on NVIDIA hardware since it's specifically optimized for GPU-to-GPU transfers. For multi-node training, ensure network bandwidth is sufficient with at least 25Gbps interconnect. Profile communication time versus computation time to identify whether synchronization is your actual bottleneck.

Gradient synchronization is required when training on multiple GPUs or machines. Each worker computes gradients on its data subset, and synchronization ensures all workers update their model weights consistently. Without synchronization, workers diverge and the model fails to converge. You need synchronization for data-parallel training where you split batches across GPUs, and for model-parallel training where different model parts run on different GPUs. Single-GPU training doesn't need synchronization.

Use synchronous for most applications since it produces identical results to single-GPU training and is simpler to debug. Asynchronous allows workers to update independently, improving hardware utilization but potentially degrading convergence quality due to stale gradients. Synchronous training scales efficiently up to 8-16 GPUs for most models. Consider asynchronous only when scaling beyond 16 GPUs or when worker speeds vary significantly due to heterogeneous hardware. Most ML frameworks default to synchronous for good reason.

Use gradient compression to reduce communication volume by 10-100x. Overlap computation with communication by starting gradient transfer for completed layers while other layers are still computing. Use NCCL for multi-GPU communication on NVIDIA hardware since it's specifically optimized for GPU-to-GPU transfers. For multi-node training, ensure network bandwidth is sufficient with at least 25Gbps interconnect. Profile communication time versus computation time to identify whether synchronization is your actual bottleneck.

Gradient synchronization is required when training on multiple GPUs or machines. Each worker computes gradients on its data subset, and synchronization ensures all workers update their model weights consistently. Without synchronization, workers diverge and the model fails to converge. You need synchronization for data-parallel training where you split batches across GPUs, and for model-parallel training where different model parts run on different GPUs. Single-GPU training doesn't need synchronization.

Use synchronous for most applications since it produces identical results to single-GPU training and is simpler to debug. Asynchronous allows workers to update independently, improving hardware utilization but potentially degrading convergence quality due to stale gradients. Synchronous training scales efficiently up to 8-16 GPUs for most models. Consider asynchronous only when scaling beyond 16 GPUs or when worker speeds vary significantly due to heterogeneous hardware. Most ML frameworks default to synchronous for good reason.

Use gradient compression to reduce communication volume by 10-100x. Overlap computation with communication by starting gradient transfer for completed layers while other layers are still computing. Use NCCL for multi-GPU communication on NVIDIA hardware since it's specifically optimized for GPU-to-GPU transfers. For multi-node training, ensure network bandwidth is sufficient with at least 25Gbps interconnect. Profile communication time versus computation time to identify whether synchronization is your actual bottleneck.