Page 113 - 《软件学报》2025年第12期
P. 113

5494                                                      软件学报  2025  年第  36  卷第  12  期


                     Implementation. Renton: USENIX Association, 2022. 945–960.
                 [15]   TorchElastic. 2022. https://pytorch.org/elastic/0.2.0/index.html
                 [16]   Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L. ImageNet: A large-scale hierarchical image database. In: Proc. of the 2009 IEEE
                     Conf. on Computer Vision and Pattern Recognition. Miami: IEEE, 2009. 248–255. [doi: 10.1109/CVPR.2009.5206848]
                 [17]   He KM, Zhang XY, Ren SQ, Sun J. Deep residual learning for image recognition. In: Proc. of the 2016 IEEE Conf. on Computer Vision
                     and Pattern Recognition. Las Vegas: IEEE, 2016. 770–778. [doi: 10.1109/CVPR.2016.90]
                 [18]   Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556, 2015.
                 [19]   Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: Proc. of the 2016
                     IEEE Conf. on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016. 2818–2826. [doi: 10.1109/CVPR.2016.308]
                 [20]   Maas AL, Daly RE, Pham PT, Huang D, Ng AY, Potts C. Learning word vectors for sentiment analysis. In: Proc. of the 49th Annual
                     Meeting  of  the  Association  for  Computational  Linguistics:  Human  Language  Technologies.  Portland:  Association  for  Computational
                     Linguistics, 2011. 142–150.
                 [21]   Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I. Language models are unsupervised multitask learners. 2019. https://cdn.
                     openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
                 [22]   Panayotov V, Chen GG, Povey D, Khudanpur S. LibriSpeech: An ASR corpus based on public domain audio books. In: Proc. of the 2015
                     IEEE Int’l Conf. on Acoustics, Speech and Signal Processing. South Brisbane: IEEE, 2015. 5206–5210. [doi: 10.1109/ICASSP.2015.
                     7178964]
                 [23]   Amodei D, Ananthanarayanan S, Anubhai R, et al. Deep Speech 2: End-to-end speech recognition in English and Mandarin. In: Proc. of
                     the 33rd Int’l Conf. on Machine Learning. New York: JMLR.org, 2016. 173–182.
                 [24]   Zhao HY, Han ZH, Yang Z, Zhang QL, Yang F, Zhou LD, Yang M, Lau FCM, Wang YQ, Xiong YF, Wang B. HiveD: Sharing a GPU
                     cluster  for  deep  learning  with  guarantees.  In:  Proc.  of  the  14th  USENIX  Symp.  on  Operating  Systems  Design  and  Implementation.
                     USENIX Association, 2020. 515–532.
                 [25]   Microsoft Philly Trace. 2019. https://github.com/msr-fiddle/philly-traces
                 [26]   Amazon EC2 P4 Instance. 2024. https://aws.amazon.com/cn/ec2/instance-types/p4/
                 [27]   Koumoutzelis S, Giannoulakis I, Georgoulakis T, Avdikos G, Kafetzakis E. Security issues of GPUs and FPGAs for AI-powered near &
                     far edge services. In: Proc. of the 22nd European Conf. on Cyber Warfare and Security, 2023. 703–706. [doi: 10.34190/eccws.22.1.1160]
                 [28]   George L, Rivierre N, Spuri M. Preemptive and non-preemptive real-time uniprocessor scheduling. 1996. https://inria.hal.science/inria-
                     00073732/PDF/RR-2966.pdf
                 [29]   Malandrino  F,  Kirkpatrick  S,  Chiasserini  CF.  How  close  to  the  edge?  Delay/utilization  trends  in  MEC.  In:  Proc.  of  the  2016  ACM
                     Workshop on Cloud-assisted Networking. Irvine: ACM, 2016. 37–42. [doi: 10.1145/3010079.3010080]
                 [30]   Narayanan  D,  Santhanam  K,  Kazhamiaka  F,  Phanishayee  A,  Zaharia  M.  Heterogeneity-aware  cluster  scheduling  policies  for  deep
                     learning workloads. In: Proc. of the 14th USENIX Symp. on Operating Systems Design and Implementation. USENIX Association, 2020.
                     481–498.

                             谷典典(1998-), 女, 博士, 主要研究领域为机器                 刘譞哲(1980-), 男, 博士, 教授, 博士生导师,
                            学习系统, 集群调度, 分布式机器学习.                         CCF  杰出会员, 主要研究领域为服务计算, 系统
                                                                         软件.



                             金鑫(1989-), 男, 博士, 副教授, CCF  高级会员,
                            主要研究领域为系统软件, 计算机网络, 云计算.
   108   109   110   111   112   113   114   115   116   117   118