Deep learning performance depends heavily on the seamless movement of data from durable storage and RAM to the processors (CPUs or GPUs). If data cannot be loaded quickly enough, or if matrices cannot be moved rapidly to the accelerators, the processing elements will starve, creating a major system bottleneck. To achieve optimal performance, systems must efficiently shuffle data and often interleave communication with computation.

Hardware Data Movement Bottlenecks

In a standard computer architecture for deep learning, most high-performance components—such as the network interface, graphics processing unit (GPU) accelerators, and durable storage—are attached to the central processing unit (CPU) across the PCIe bus. In contrast, the main memory (RAM) is directly attached to the CPU, offering massive total bandwidth (e.g., up to 100 GB/s) to ensure data can flow seamlessly into the processor during intensive computations.

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