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.

