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HPC AI500: A Benchmark Suite for HPC AI Systems


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HPC AI500 Ranking, Image Classification, Free Level, July 2, 2020



RankSource VPFLOPSTime (min)QualityAI accFramework
1Fujitsu [1]31.411.275.1%Tesla V100 * 2048MXNet
2Google [2] V3 * 1024TensorFlow
3Sony [3]10.023.775.0%Tesla V100 * 2176 NNL
4Tencent [4]6.446.676.0%Tesla P100 * 1024Chainer
5Preferred Network [5]2.411574.9%Tesla P40 * 2048TensorFlow
6Berkeley [6]1.952075.4%KNL * 2048Intel Caffe
7Intel [7]1.272874.6%KNL * 1536Intel Caffe
8IBM [8]0.755075.0%Tesla P100 * 256Caffe
9Facebook [9]0.706076.3%Tesla P100 * 1024Caffe2

The data (unverified) are collected from the original papers and technical reports.

Top 3 HPC AI Systems

Rank1: Fujitsu

Achieves 31.41 Valid PFLOPS, and finishes Image Classification in 1.2 minutes. The hardware consists of 2048 Nvidia Tesla V100 GPUs. They propose a novel communication algorithm by optimal scheduling group layers and implement a CUDA kernel that dedicated to calculating norms in parallel. They also leverage the TensorCore of Tesla V100 by mixed-precision training.

Rank2: Google

Achieves 20.10 Valid PFLOPS, and finishes Image Classification in 2.2 minutes. The hardware consists of 1024 TPU V3. They propose a 2D-mesh all-reduce for highly efficient communication and implement the batch normalization in a distributed manner. They leverage BFLOAT16, which is the unique precision representation in TPU, for mixed precision training.

Rank3: Sony

Achieves 10.02 VPFLOPS, and finishes Image Classification in 3.7 minutes. The hardware consists of 2176 Nvidia Tesla V100 GPUs. They propose a 2D-Turus all-reduce for highly efficient communication and eliminate the moving average in batch normalization. They also leverage the TensorCore of Tesla V100 by mixed-precision training.


HPC AI500 Benchmarks

Problem DomainDataset Target qualityEpochs
Image ClassificationImageNet Top1 Accuracy = 0.76390
Extreme Weather AnalyticsThe extreme weather dataset mAP@[IoU=0.5]=0.3550


The primary metric is Valid FLOPS, which is calculated by the following equation:

VFLOPS = FLOPS * (achieved quality/ target quality) ^n

Achieved 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. N is a positive integer, indicating the sensitivity to the model quality. In image Classification, the target quality is top1 accuracy=0.763 and the value of n is 5 as default. In extreme weather analytics, the target quality is mAP@[IoU=0.5]=0.35 and the value of n is 10 as default.


As shown in Figure 2, HPC AI500 benchmarking methodology provides three benchmarking levels, including hardware level, system level, and free level.

  • Hardware level, users can change layer 1 to layer 4. 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.
  • 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).
  • Free level, users can change any layers from Layer 1 to Layer 8 while keeping Layer 9 intact. The same data set, target quality, and training epochs are defined in Layer 9 while the other layers are open for optimizations.

Figure 2: HPC AI500 V2.0 Methodology.


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2. C. Ying, S. Kumar, D. Chen, T. Wang, and Y. Cheng, “Image classification at supercomputer scale,” arXiv preprint arXiv:1811.06992, 2018.
3. Y. Tanaka and Y. Kageyama, “Imagenet/resnet-50 training in 224 seconds”.
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7. V. Codreanu, D. Podareanu, and V. Saletore, “Scale out for large minibatch sgd: Residual net-work training on imagenet-1k with improved accuracy and reduced time to train,”arXiv preprintarXiv:1711.04291, 2017.
8. M. Cho, U. Finkler, S. Kumar, D. Kung, V. Saxena, and D. Sreedhar, “Powerai ddl,”arXiv preprintarXiv:1708.02188, 2017.
9. P. Goyal, P. Doll ́ar, R. Girshick, P. Noordhuis, L. Wesolowski, A. Kyrola, A. Tulloch, Y. Jia,and K. He, “Accurate, large minibatch sgd: Training imagenet in 1 hour,” arXiv preprintarXiv:1706.02677, 2017.