%0 Conference Proceedings
%T Leveraging Subgraph Extraction for Performance Portable Programming Frameworks on DL Accelerators
%+ CAS Institute of Computing Technology (ICT)
%+ University of Chinese Academy of Sciences [Beijing] (UCAS)
%+ Cambricon Technologies
%A Zhang, Xiao
%A Lan, Huiying
%A Zhi, Tian
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 15th IFIP International Conference on Network and Parallel Computing (NPC)
%C Muroran, Japan
%Y Feng Zhang
%Y Jidong Zhai
%Y Marc Snir
%Y Hai Jin
%Y Hironori Kasahara
%Y Mateo Valero
%I Springer International Publishing
%3 Network and Parallel Computing
%V LNCS-11276
%P 179-184
%8 2018-11-29
%D 2018
%R 10.1007/978-3-030-05677-3_21
%Z Computer Science [cs]Conference papers
%X Deep learning framework plays an important role in connecting hardware platform and algorithm. In recent years, some domain-specific deep learning accelerators with better performance and energy efficiency were proposed by researchers. However, current frameworks lack enough considerations about how to better support the possible new features brought by accelerators. In this paper, we propose to build a performance portable programming framework with subgraph extraction. The intuition is that increasing ratio of optimizations are taken from the top-level framework to the low-level software stack of accelerator. In response to this development trend, framework needs to pay more attention to the splitting strategy of computation graph for the heterogeneous computation.
%G English
%Z TC 10
%Z WG 10.3
%2 https://inria.hal.science/hal-02279540/document
%2 https://inria.hal.science/hal-02279540/file/477597_1_En_21_Chapter.pdf
%L hal-02279540
%U https://inria.hal.science/hal-02279540
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC10
%~ IFIP-NPC
%~ IFIP-WG10-3
%~ IFIP-LNCS-11276