Hpc ai benchmark free#

Keutzer, “Imagenet training in minutes,” inProceedings of the 47th International Conference on Parallel Processing, ICPP 2018, (New York,NY, USA), Association for Computing Machinery, 2018.ħ. Fukuda, “Extremely large minibatch sgd: Training resnet-50 on imagenetin 15 minutes,” arXiv preprint arXiv:1711.04325, 2017.Ħ.

Highly Scal- able Deep Learning Training System with Mixed–Precision: Training ImageNet in Four Minutes. Kageyama, “Imagenet/resnet-50 training in 224 seconds”.Ĥ. Cheng, “Image classification at supercomputer scale,” arXiv preprint arXiv:1811.06992, 2018.ģ. Nakashima, “Yet another accelerated sgd: Resnet-50 training on imagenet in 74.7 seconds,” arXivpreprint arXiv:1903.12650, 2019.Ģ. The same data set, target quality, and training epochs are defined in Layer 9 while the other layers are open for optimizations.ġ.

Hpc ai benchmark free#

Free level, users can change any layers from Layer 1 to Layer 8 while keeping Layer 9 intact.System level, In addition to the changes allowed in the hardware level, the users areallowed to re-implement the algorithms on different or even customized AI framework (Layer 5).For the other layers, the benchmark users can only change parallel modes inLayer 6 or tune learning rate policies and batchsize settings in Layer 8. Hardware level, users can change layer 1 to layer 4.MethodologyĪs shown in Figure 2, HPC AI500 benchmarking methodology provides three benchmarking levels, including hardware level, system level, and free level. In extreme weather analytics, the target quality is and the value of n is 10 as default. In image Classification, the target quality is top1 accuracy=0.763 and the value of n is 5 as default. N is a positive integer, indicating the sensitivity to the model quality. VFLOPS = FLOPS * (achieved quality/ target quality) ^nĪchieved quality represents the actual model quality achieved in the evaluation target quality is the state-of-the-art model quality, predefined in HPC AI500 benchmark. The extreme weather primary metric is Valid FLOPS, which is calculated by the following equation: They also leverage the TensorCore of Tesla V100 by mixed-precision training. They propose a 2D-Turus all-reduce for highly efficient communication and eliminate the moving average in batch normalization. The hardware consists of 2176 Nvidia Tesla V100 GPUs. They leverage BFLOAT16, which is the unique precision representation in TPU, for mixed precision training.Īchieves 10.02 VPFLOPS, and finishes Image Classification in 3.7 minutes. They propose a 2D-mesh all-reduce for highly efficient communication and implement the batch normalization in a distributed manner.

They also leverage the TensorCore of Tesla V100 by mixed-precision training.Īchieves 20.10 Valid PFLOPS, and finishes Image Classification in 2.2 minutes. They propose a novel communication algorithm by optimal scheduling group layers and implement a CUDA kernel that dedicated to calculating norms in parallel. The hardware consists of 2048 Nvidia Tesla V100 GPUs. Top 3 HPC AI SystemsĪchieves 31.41 Valid PFLOPS, and finishes Image Classification in 1.2 minutes. Submission Contact: Please contact to submit a new benchmarking result. The data (unverified) are collected from the original papers and technical reports. HPC AI500 Ranking, Image Classification, Free Level, July 2, 2020