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swTVM: Towards Optimized Tensor Code Generation for Deep Learning on Sunway Many-Core Processor by Li, Mingzhen; Liu, Changxi; Liao, Jianjin; Zheng, Xuegui; Yang, Hailong; Sun, Rujun; Xu, Jun; Gan, Lin; Yang, Guangwen; Luan, Zhongzhi; Qian, Depei is a scholarly article available to read on EtoBox.

What is swTVM: Towards Optimized Tensor Code Generation for Deep Learning on Sunway Many-Core Processor about?

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application portability. Among the existing deep learning compilers, TVM is well known for its efficiency in code generation and optimization across diverse hardware devices. In the meanwhile, the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning workloads. This paper combines the trends in these two directions. Specifically, we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as Sunway. In addition, we leverage the architecture features during the compilation such as core group for massive parallelism, DMA for high bandwidth memory transfer and local device memory for data locality, in order to generate efficient codes for deep learning workloads on Sunway. The experiment results show that the codes generated by swTVM achieves 1.79x on average compared to the state-of-the-art deep learning framework on Sunway, acros

Author

Li, Mingzhen; Liu, Changxi; Liao, Jianjin; Zheng, Xuegui; Yang, Hailong; Sun, Rujun; Xu, Jun; Gan, Lin; Yang, Guangwen; Luan, Zhongzhi; Qian, Depei

Published

2019

Language

EN