Optimizing Data Transfer in Batched AI/ML Inference Workloads

GPU-to-CPU data transfer can be a significant bottleneck in batched AI/ML inference workloads, particularly when model outputs are large. Using a toy PyTorch image segmentation model and the NVIDIA Nsight Systems profiler, a performance issue is identified where the GPU idles while waiting for the CPU to process the previous batch's output. The initial analysis reveals that sequential execution of model inference and output processing causes this inefficiency. To resolve this, the first proposed optimization is implementing a multi-worker, producer-consumer pattern using PyTorch's multiprocessing to handle output processing in parallel, freeing the main process to feed the next batch to the GPU.