Chainer-XP: A Flexible Framework for ANNs Run on the Intel® Xeon PhiTM Coprocessor

Thanh-Dang Diep (), Minh-Tri Nguyen (), Nhu-Y Nguyen-Huynh (), Minh Thanh Chung (), Manh-Thin Nguyen (), Nguyen Quang-Hung () and Nam Thoai ()
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Thanh-Dang Diep: Ho Chi Minh City University of Technology, VNUHCM, High Performance Computing Laboratory, Faculty of Computer Science and Engineering
Minh-Tri Nguyen: Ho Chi Minh City University of Technology, VNUHCM, High Performance Computing Laboratory, Faculty of Computer Science and Engineering
Nhu-Y Nguyen-Huynh: Ho Chi Minh City University of Technology, VNUHCM, High Performance Computing Laboratory, Faculty of Computer Science and Engineering
Minh Thanh Chung: Ho Chi Minh City University of Technology, VNUHCM, High Performance Computing Laboratory, Faculty of Computer Science and Engineering
Manh-Thin Nguyen: Ho Chi Minh City University of Technology, VNUHCM, High Performance Computing Laboratory, Faculty of Computer Science and Engineering
Nguyen Quang-Hung: Ho Chi Minh City University of Technology, VNUHCM, High Performance Computing Laboratory, Faculty of Computer Science and Engineering
Nam Thoai: Ho Chi Minh City University of Technology, VNUHCM, High Performance Computing Laboratory, Faculty of Computer Science and Engineering

A chapter in Modeling, Simulation and Optimization of Complex Processes HPSC 2018, 2021, pp 133-147 from Springer

Abstract: Abstract Chainer is a well-known deep learning framework facilitating the quick and efficient establishment of Artificial Neural Networks. Chainer can be deployed on systems consisting of Central Processing Units and Graphics Processing Units efficiently. In addition, it is possible to run Chainer on systems containing Intel Xeon Phi coprocessors. Nonetheless, Chainer can only be deployed on Intel Xeon Phi Knights Landing, not Knights Corner. There are many existing systems, such as Tiane2 (MilkyWay-2), Thunder, Cascade, SuperMUC, and so on, including Knights Corner only. For that reason, Chainer cannot fully exploit the computing power of such systems, which leads to the demand for supporting Chainer run on them. It becomes more challenging in the situation where deep learning applications are written in Python while the Xeon Phi processor is only capable of interpreting C/C $$++$$ + + or Fortran. Fortunately, there is an offloading module called pyMIC which helps port Python applications into the Intel Xeon Phi Knights Corner coprocessor. In this paper, we present Chainer-XP as a deep learning framework assisting applications to run on the systems containing the Intel Xeon Phi Knights Corner coprocessor. Chainer-XP is an extension of Chainer by integrating pyMIC into Chainer. The experimental findings show that Chainer-XP can help to move the core computation (matrix multiplication) to the Intel Xeon Phi Knights Corner coprocessor with acceptable performance in comparison with Chainer.

Keywords: Deep learning framework; Xeon Phi; Knights Corner; Offloading; Neural network (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/978-3-030-55240-4_7

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