Limited throughput between CPU and GPU often results from data transfer bottlenecks or inefficient resource utilization. NVIDIA’s documentation on optimizing deep learning workflows (e.g., using CUDA and cuDNN) suggests the following:
Option B: Memory pooling techniques, such as pinned memory or unified memory, reduce data transfer overhead by optimizing how data is staged between CPU and GPU.
[References:, NVIDIA CUDA Documentation:https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html, NVIDIA GPU Product Documentation:https://www.nvidia.com/en-us/data-center/products/, ]
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