
    Figure 4: CUDA graphs optimization

With graphing, we see that the GPU kernels are tightly packed and GPU utilization remains high. The graphed portion now runs in 6 ms instead of 31ms, a speedup of 5x. We did not graph the entire model, mostly just the resnet backbone, which resulted in an overall speedup of ~1.7x.
In order to increase the scope of the graph, we made some changes in the software stack to eliminate some of the CPU-GPU synchronization points. In MLPerf v1.0, this work included changing the implementation of torch.randperm function to use CUB instead of Thrust because the latter is a synchronous C++ template library. These improvements are available in the latest NGC container.
BERT