Fetching the paper…
Reading the bibliography…
High-Performance Computing (HPC) centers and cloud providers support an increasingly diverse set of applications on heterogenous hardware.
1901
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, pp. 1735–80, 12 1997
1997
Earlier work this paper cites.
Y. Lecun, L. Bottou, Y. Bengio et al. , “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
S. Fernández, A. Graves, and J. Schmidhuber, “An application of recurrent neural networks to discriminative keyword spotting,” in Proceedings of the 17th International Conference on Artificial Neural Networks , ser. ICANN’07. Berlin, Heidelberg: Springer-Verlag, 2007, p. 220–229
2007
Earlier work this paper cites.
A. Graves and J. Schmidhuber, “Offline handwriting recognition with multidimensional recurrent neural networks,” in Advances in Neural Information Processing Systems , D. Koller, D. Schuurmans, Y. Bengio et al. , Eds., vol. 21. Curran Associates, Inc., 2008. [Online]. Available: https://proceedings.neurips.cc/paper/2008/file/66368270ffd51418ec58bd793f2d9b1b-Paper.pdf
2008
Earlier work this paper cites.
J. Wilkes, “More Google cluster data.” [Online]. Available: https://ai.googleblog.com/2011/11/more-google-cluster-data.html
2011
Earlier work this paper cites.
J. Wilkes and C. Reiss, “Clusterdata 2011 traces.” [Online]. Available: https://github.com/google/cluster-data/blob/master/ClusterData2011_2.md
2011
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort et al. , “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research , vol. 12, pp. 2825–2830, 2011
2011
Earlier work this paper cites.
A. L. Maas, A. Y. Hannun, A. Y. Ng et al. , “Rectifier nonlinearities improve neural network acoustic models,” in Proc. icml , vol. 30, no. 1. Citeseer, 2013, p. 3
2013
Earlier work this paper cites.
D. G. Feitelson, D. Tsafrir, and D. Krakov, “Experience with using the parallel workloads archive,” Journal of Parallel and Distributed Computing , vol. 74, no. 10, pp. 2967–2982, 2014. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0743731514001154
2014
Earlier work this paper cites.
A. Wildani and I. F. Adams, “A case for rigorous workload classification,” in 2015 IEEE 23rd International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems . IEEE, 2015, pp. 146–149
2015
Cited alongside, same era.
J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural Networks , vol. 61, pp. 85–117, 2015. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0893608014002135
2015
Cited alongside, same era.
X. Shi, Z. Chen, H. Wang et al. , “Convolutional lstm network: A machine learning approach for precipitation nowcasting,” in Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1 , ser. NIPS’15. Cambridge, MA, USA: MIT Press, 2015, p. 802–810
2015
Cited alongside, same era.
W. Yoo, A. Sim, and K. Wu, “Machine learning based job status prediction in scientific clusters,” in 2016 SAI Computing Conference (SAI) . IEEE, 2016, pp. 44–53
2016
M. Prabhat, K. Kashinath, T. Kurth et al. , “Lessons learnt from applying Deep Learning to Scientific problems at NERSC,” in AGU Fall Meeting Abstracts , vol. 2018, Dec. 2018, pp. IN12A–07
2018
Later among the works it cites.
J. Wilkes, “Clusterdata 2019 traces.” [Online]. Available: https://github.com/google/cluster-data/blob/master/ClusterData2019.md
2019
Later among the works it cites.
J. Bang, C. Kim, K. Wu et al. , HPC Workload Characterization Using Feature Selection and Clustering . New York, NY, USA: Association for Computing Machinery, 2020, p. 33–40
2020
Later among the works it cites.
V. J. Reddi, C. Cheng, D. Kanter et al. , “Mlperf inference benchmark,” in 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA) , 2020, pp. 446–459
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , ser. KDD 16. New York, NY, USA: Association for Computing Machinery, 2016, p. 785–794. [Online]. Available: https://doi.org/10.1145/2939672.2939785
2016
Cited alongside, same era.
B. H. Park, S. Hukerikar, R. Adamson et al. , “Big data meets hpc log analytics: Scalable approach to understanding systems at extreme scale,” in 2017 IEEE International Conference on Cluster Computing (CLUSTER) . IEEE, 2017, pp. 758–765
2017
Cited alongside, same era.
J. Klinkenberg, C. Terboven, S. Lankes et al. , “Data mining-based analysis of hpc center operations,” in 2017 IEEE International Conference on Cluster Computing (CLUSTER) . IEEE, 2017, pp. 766–773
2017
Cited alongside, same era.
L. N. Smith, “Cyclical learning rates for training neural networks,” in 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) , 2017, pp. 464–472
2017
Cited alongside, same era.
A. Banjongkan, W. Pongsena, R. Chanklan et al. , “Multi-label classification of high performance computing workload with variable transformation,” International Journal of Machine Learning and Computing IJOMLAC8 (2018) , vol. 536, 2018
2018
Cited alongside, same era.
G. Amvrosiadis, M. Kuchnik, J. W. Park et al. , “The atlas cluster trace repository,” Usenix Mag. , vol. 43, no. 4, 2018
2018
Cited alongside, same era.
D. Feitelson. Parallel workloads archive. [Online]. Available: https://www.cs.huji.ac.il/labs/parallel/workload/
Cited in the paper.
S. Anoep, C. Dumitrescu, D. Epema et al. Grid workloads archive. [Online]. Available: http://gwa.ewi.tudelft.nl/
Cited in the paper.
2021
Later among the works it cites.
S. Samsi, M. L. Weiss, D. Bestor et al. , “The MIT Supercloud Dataset,” in 2021 IEEE High Performance Extreme Computing Conference (HPEC) , 2021, pp. 1–8
2021
Later among the works it cites.
S. Köhler, L. Wenzel, M. Plauth et al. , “Recognizing hpc workloads based on power draw signatures,” in 2021 Ninth International Symposium on Computing and Networking Workshops (CANDARW) , 2021, pp. 278–284
2021
Later among the works it cites.
C. Bentéjac, A. Csörgő, and G. Martínez-Muñoz, “A comparative analysis of gradient boosting algorithms,” Artificial Intelligence Review , vol. 54, no. 3, pp. 1937–1967, 2021
2021
Later among the works it cites.
B. Li, R. Arora, S. Samsi et al. , “AI-Enabling Workloads on Large-Scale GPU-Accelerated System: Characterization, Opportunities, and Implications,” in 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA) , In press
2022
Closest in time.
“MLaaS in the wild: Workload analysis and scheduling in Large-Scale heterogeneous GPU clusters,” in 19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22) . Renton, WA: USENIX Association, Apr. 2022. [Online]. Available: https://www.usenix.org/conference/nsdi22/presentation/weng
2022
Closest in time.