Modern computing systems feature a variety of distinct communication resources, such as the PCI-Express bus for cross-device data transfers, storage interfaces like solid-state drives, and network bandwidth for distributed operations. Because these communication channels are independent hardware components, they can be utilized in parallel to achieve peak system efficiency. By scheduling data movement simultaneously across these different pathways, backend frameworks can ensure that large-scale deep learning operations minimize idle time and maximize overall throughput.

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In distributed deep learning, transferring data between devices (such as moving gradients from a GPU to the CPU) utilizes the system bus (e.g., PCI-Express), which is a hardware resource distinct from the computational units. Because these resources are separate, frameworks can overlap computation and communication to significantly reduce total execution time. For example, during backpropagation on a minibatch, some parameter gradients become available earlier than others. A system can begin copying the computed gradients over the bus while the GPU concurrently processes the remaining gradients, since copying $$y[i-1]$$ does not conflict with computing $$y[i]$$.

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