Sequence packing requires that the length distribution of the training set sequences be known in advance. As the training sequences have different lengths, it is possible to fit more than one sequence within a 512-token sample. In MLPerf Bidirectional Encoder Representations from Transformers (BERT), a training sample has been restricted to 512 tokens, but it often has fewer tokens than 512. To take advantage of CUDA graphs efficiently while minimizing the overhead of padding, NVIDIA used the concept of sequence packing this round. However, for small batch sizes, where CUDA graphs are used to reduce CPU overhead, dynamically sized buffers require many separate graphs for each possible size.

This is not an issue when we do not have to use CUDA graphs, such when large-batch sizes are used. Unpadding, however, results in dynamically sized buffers, as the total number of tokens is not fixed anymore. In previous rounds, the overhead related to padding required to fill up the batch was already optimized by introducing an unpadding optimization. Fusion of fully connected and GELU layers.The latest NVIDIA BERT submission took advantage of the following optimizations: For more information, see The following sections highlight some of the work done to achieve these improvements. (**) RetinaNet was not part of either MLPerf v0.7 or v1.1. 3D U-Net and RNN-T were not part of MLPerf v0.7. Per-Accelerator performance for A100 computed using 8xA100 server time-to-train and multiplying it by 8. Object Detection вЂ“ Lightweight (RetinaNet) V2.0 Per-Accelerator Time to Train (min) (vs. Through continued innovation across the entire stack, including system software, libraries, and algorithms, NVIDIA has yet again delivered performance improvements compared to prior submissions using the same A100 Tensor Core GPU.Д¬ompared to NVIDIA MLPerf v0.7 submissions, which marked the first A100 Tensor Core GPU submissions, results showed gains of up to 2.1x on a per-chip basis, and 5.7x for max-scale training (Table 1). Many partners also submitted results using the A100 Tensor Core GPU. NVIDIA MLPerf v2.0 submissions are based on the proven A100 Tensor Core GPU, the NVIDIA DGX A100 system, as well as the NVIDIA DGX SuperPODВ reference architecture. In this post, you will learn about the methods that NVIDIA deployed across the entire stack to deliver more performance in MLPerf v2.0. The NVIDIA platform, with full-stack innovation and a rich developer and application ecosystem, continues to be the only one to submit results on all eight MLPerf Training tests, as well as to submit on all MLPerf Inference and MLPerf high-performance computing (HPC) tests. The ability to train every model, as well as provide infrastructure fungibility to run all AI workloads from training to inference, is critical to allowing organizations to maximize return on their infrastructure investments. However, just as important as a platformвЂ™s performance is its versatility.

Lower training times are important to speed time to deployment, minimizing the total cost of ownership and maximizing return on investment.

MLPerf training focuses on measuring time to train a range of commonly used neural networks for the following tasks: MLPerf benchmarks are developed by a consortium of AI leaders across industry, academia, and research labs, with the aim of providing standardized, fair, and useful measures of deep learning performance.

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