Campo: Cost-Aware Performance Optimization for Mixed-Precision Neural Network Training


Xin He, CSEE, Hunan University & Xidian University; Jianhua Sun and Hao Chen, CSEE, Hunan University; Dong Li, University of California, Merced


Mixed precision training uses a mixture of full and lower precisions for neural network (NN) training. Applying mixed precision must cast tensors in NN from float32 (FP32) to float16 (FP16) or vice versa. The existing strategy greedily applies FP16 to performance-critical operations without quantifying and considering the casting cost. However, we reveal that the casting cost can take more than 21% of NN operation execution time, and in some cases surpasses the performance benefit of using low precision. In this paper, we introduce Campo, a tool that improves performance of mixed-precision NN training with the awareness of casting costs. Campo is built upon performance modeling that predicts the casting cost and operation performance with low precision, and introduces a cost-aware graph rewriting strategy. Campo is user-transparent, and enables high performance NN training using mixed precision without training accuracy loss. Evaluating Campo with six NN models, we show that compared to TensorFlow using TF_AMP (a state-of-the-art performance optimizer for mixed precision training from Nvidia), Campo improves training throughput by 20.8% on average (up to 24.5%) on RTX 2080 Ti GPU and by 20.9% on average (up to 23.4%) on V100 GPU, without training accuracy loss. Because of using the cost-aware mixed precision training, Campo also improves energy efficiency by 21.4% on average (up to 24.2%), compared to TensorFlow using TF_AMP.

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@inproceedings {280706,
author = {Xin He and Jianhua Sun and Hao Chen and Dong Li},
title = {Campo: {Cost-Aware} Performance Optimization for {Mixed-Precision} Neural Network Training},
booktitle = {2022 USENIX Annual Technical Conference (USENIX ATC 22)},
year = {2022},
isbn = {978-1-939133-29-22},
address = {Carlsbad, CA},
pages = {505--518},
url = {},
publisher = {USENIX Association},
month = jul

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