BaGuaLu: Targeting Brain Scale Pretrained Models with over 37 Million Cores

Zixuan Ma, Jiaao He, Jiezhong Qiu, Huanqi Cao, Yuanwei Wang, Zhenbo Sun, Liyan Zheng, Haojie Wang, Shizhi Tang, Tianyu Zheng, Junyang Lin, Guanyu Feng, Zeqiang Huang, Jie Gao, Aohan Zeng, Jianwei Zhang, Runxin Zhong, Tianhui Shi, Sha Liu, Weimin Zheng, Jie Tang, Hongxia Yang, Xin Liu, Jidong Zhai, Wenguang Chen

January, 2022

Abstract

Large-scale pretrained AI models have shown state-of-the-art accuracy in a series of important applications. As the size of pretrained AI models grows dramatically each year in an effort to achieve higher accuracy, training such models requires massive computing and memory capabilities, which accelerates the convergence of AI and HPC. However, there are still gaps in deploying AI applications on HPC systems, which need application and system co-design based on specific hardware features.To this end, this paper proposes BaGuaLu1, the first work targeting training brain scale models on an entire exascale supercomputer, the New Generation Sunway Supercomputer. By combining hardware-specific intra-node optimization and hybrid parallel strategies, BaGuaLu enables decent performance and scalability on unprecedentedly large models. The evaluation shows that BaGuaLu can train 14.5-trillion-parameter models with a performance of over 1 EFLOPS using mixed-precision and has the capability to train 174-trillion-parameter models, which rivals the number of synapses in a human brain.

Type

Conference paper

Publication

Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming