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BOOM: Use your Desktop to Accurately Predict the Performance of Large Deep Neural Networks

Published: 13 October 2024 Publication History

Abstract

The intensive computational requirements of training deep neural networks (DNNs) have significantly driven the adoption of DNN accelerators like Graph Processing Units (GPU). However, selecting the most suitable GPU from all candidates with drastically different specifications and prices is still a challenging problem. While directly measuring the performance of DNN training tasks on every candidate is prohibitive, and not always available due to hardware shortage, an accurate performance predictor can assist in the decision-making. However, most existing performance predictors cannot predict the GPU memory footprint in an accurate, generalizable, and interpretable manner, which is crucial to the feasibility and performance of running the DNN model on real GPUs. Moreover, many optimizations for DNN training, such as mixed precision training and checkpointing, can significantly impact performance. However, such hardware-dependent optimizations are not considered by existing performance predictors.
In this work, we propose a novel performance predictor containing (1) a memory footprint predictor with better generalizability and interoperability; (2) a runtime predictor supporting hardware-dependent optimizations. Experiments show that our memory footprint predictor achieves an average error of 2.7% on CNN models and 0.9% on transformers, and the runtime predictor achieves an average error of 10.5% on the CNN and transformer models.

References

[1]
2023. Amazon EC2 Instance Types. https://aws.amazon.com/ec2/instance-types/.
[2]
2023. AWS Inferentia. https://aws.amazon.com/machine-learning/inferentia/.
[3]
2023. AWS Trainium. https://aws.amazon.com/machine-learning/trainium/.
[4]
2023. cuBLAS. https://docs.nvidia.com/cuda/cublas/.
[5]
2023. NVIDIA Nsight Compute. https://developer.nvidia.com/nsight-compute.
[6]
2023. NVIDIA Nsight Systems. https://developer.nvidia.com/nsight-systems.
[7]
2023. PyTorch Memory Management. https://pytorch.org/docs/stable/notes/cuda.html#memory-management.
[8]
2023. SambaNova DataScale. https://sambanova.ai/products/datascale/.
[9]
2024. vast.ai. https://vast.ai/.
[10]
Andrew Adams, Karima Ma, Luke Anderson, Riyadh Baghdadi, Tzu-Mao Li, Michaël Gharbi, Benoit Steiner, Steven Johnson, Kayvon Fatahalian, Frédo Durand, 2019. Learning to optimize halide with tree search and random programs. ACM Transactions on Graphics (TOG) 38, 4 (2019), 1–12.
[11]
Muralidhar Andoorveedu, Zhanda Zhu, Bojian Zheng, and Gennady Pekhimenko. 2022. Tempo: Accelerating Transformer-Based Model Training through Memory Footprint Reduction. arXiv preprint arXiv:2210.10246 (2022).
[12]
Riyadh Baghdadi, Massinissa Merouani, Mohamed-Hicham Leghettas, Kamel Abdous, Taha Arbaoui, Karima Benatchba, 2021. A deep learning based cost model for automatic code optimization. Proceedings of Machine Learning and Systems 3 (2021), 181–193.
[13]
Iz Beltagy, Matthew E Peters, and Arman Cohan. 2020. Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150 (2020).
[14]
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020), 1877–1901.
[15]
Beidi Chen, Tri Dao, Kaizhao Liang, Jiaming Yang, Zhao Song, Atri Rudra, and Christopher Re. 2021. Pixelated butterfly: Simple and efficient sparse training for neural network models. arXiv preprint arXiv:2112.00029 (2021).
[16]
Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, 2018. { TVM} : An automated { End-to-End} optimizing compiler for deep learning. In 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI 18). 578–594.
[17]
Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin. 2016. Training deep nets with sublinear memory cost. arXiv preprint arXiv:1604.06174 (2016).
[18]
Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy. 2018. Learning to optimize tensor programs. Advances in Neural Information Processing Systems 31 (2018).
[19]
Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, and Evan Shelhamer. 2014. cudnn: Efficient primitives for deep learning. arXiv preprint arXiv:1410.0759 (2014).
[20]
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever. 2019. Generating long sequences with sparse transformers. arXiv preprint arXiv:1904.10509 (2019).
[21]
Torch Contributors. 2021. Torchvision. models. visited June 28 (2021).
[22]
Dipankar Das, Naveen Mellempudi, Dheevatsa Mudigere, Dhiraj D. Kalamkar, Sasikanth Avancha, Kunal Banerjee, Srinivas Sridharan, Karthik Vaidyanathan, Bharat Kaul, Evangelos Georganas, Alexander Heinecke, Pradeep Dubey, Jesús Corbal, Nikita Shustrov, Roman Dubtsov, Evarist Fomenko, and Vadim O. Pirogov. 2018. Mixed Precision Training of Convolutional Neural Networks using Integer Operations. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net. https://openreview.net/forum?id=H135uzZ0-
[23]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
[24]
Xuanyi Dong, Lu Liu, Katarzyna Musial, and Bogdan Gabrys. 2021. NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2021). https://doi.org/10.1109/TPAMI.2021.3054824
[25]
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).
[26]
R David Evans, Lufei Liu, and Tor M Aamodt. 2020. Jpeg-act: accelerating deep learning via transform-based lossy compression. In 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA). IEEE, 860–873.
[27]
Siyuan Feng, Bohan Hou, Hongyi Jin, Wuwei Lin, Junru Shao, Ruihang Lai, Zihao Ye, Lianmin Zheng, Cody Hao Yu, Yong Yu, 2022. Tensorir: An abstraction for automatic tensorized program optimization. arXiv preprint arXiv:2207.04296 (2022).
[28]
Yanjie Gao, Xianyu Gu, Hongyu Zhang, Haoxiang Lin, and Mao Yang. 2021. Runtime Performance Prediction for Deep Learning Models with Graph Neural Network. Technical Report. Technical Report MSR-TR-2021-3. Microsoft.
[29]
Yanjie Gao, Yu Liu, Hongyu Zhang, Zhengxian Li, Yonghao Zhu, Haoxiang Lin, and Mao Yang. 2020. Estimating gpu memory consumption of deep learning models. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. 1342–1352.
[30]
X Yu Geoffrey, Yubo Gao, Pavel Golikov, and Gennady Pekhimenko. 2021. Habitat: A { Runtime-Based} Computational Performance Predictor for Deep Neural Network Training. In 2021 USENIX Annual Technical Conference (USENIX ATC 21). 503–521.
[31]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770–778.
[32]
Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, 2019. Searching for mobilenetv3. In Proceedings of the IEEE/CVF international conference on computer vision. 1314–1324.
[33]
Chien-Chin Huang, Gu Jin, and Jinyang Li. 2020. Swapadvisor: Pushing deep learning beyond the gpu memory limit via smart swapping. In Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems. 1341–1355.
[34]
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. 2017. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 4700–4708.
[35]
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. 2017. Quantized neural networks: Training neural networks with low precision weights and activations. The Journal of Machine Learning Research 18, 1 (2017), 6869–6898.
[36]
Animesh Jain, Amar Phanishayee, Jason Mars, Lingjia Tang, and Gennady Pekhimenko. 2018. Gist: Efficient data encoding for deep neural network training. In 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA). IEEE, 776–789.
[37]
Paras Jain, Ajay Jain, Aniruddha Nrusimha, Amir Gholami, Pieter Abbeel, Joseph Gonzalez, Kurt Keutzer, and Ion Stoica. 2020. Checkmate: Breaking the memory wall with optimal tensor rematerialization. Proceedings of Machine Learning and Systems 2 (2020), 497–511.
[38]
Zhihao Jia, Oded Padon, James Thomas, Todd Warszawski, Matei Zaharia, and Alex Aiken. 2019. TASO: optimizing deep learning computation with automatic generation of graph substitutions. In Proceedings of the 27th ACM Symposium on Operating Systems Principles. 47–62.
[39]
Zhe Jia, Blake Tillman, Marco Maggioni, and Daniele Paolo Scarpazza. 2019. Dissecting the graphcore ipu architecture via microbenchmarking. arXiv preprint arXiv:1912.03413 (2019).
[40]
Norman Jouppi, Cliff Young, Nishant Patil, and David Patterson. 2018. Motivation for and evaluation of the first tensor processing unit. ieee Micro 38, 3 (2018), 10–19.
[41]
Daniel Justus, John Brennan, Stephen Bonner, and Andrew Stephen McGough. 2018. Predicting the computational cost of deep learning models. In 2018 IEEE international conference on big data (Big Data). IEEE, 3873–3882.
[42]
Sam Kaufman, Phitchaya Phothilimthana, Yanqi Zhou, Charith Mendis, Sudip Roy, Amit Sabne, and Mike Burrows. 2021. A learned performance model for tensor processing units. Proceedings of Machine Learning and Systems 3 (2021), 387–400.
[43]
Stephen W Keckler, William J Dally, Brucek Khailany, Michael Garland, and David Glasco. 2011. GPUs and the future of parallel computing. IEEE micro 31, 5 (2011), 7–17.
[44]
Andrew Kerr, Haicheng Wu, Manish Gupta, Dustyn Blasig, Pradeep Ramini, Duane Merrill, Aniket Shivam, Piotr Majcher, Paul Springer, Markus Hohnerbach, Jin Wang, and Matt Nicely. 2022. CUTLASS. https://github.com/NVIDIA/cutlass
[45]
Marisa Kirisame, Steven Lyubomirsky, Altan Haan, Jennifer Brennan, Mike He, Jared Roesch, Tianqi Chen, and Zachary Tatlock. 2020. Dynamic tensor rematerialization. arXiv preprint arXiv:2006.09616 (2020).
[46]
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019. Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942 (2019).
[47]
Rebecca Lewington. 2021. An AI Chip With Unprecedented Performance To Do the Unimaginable. (2021).
[48]
Xiaoyao Liang. 2019. Ascend AI Processor architecture and programming.
[49]
Heng Liao, Jiajin Tu, Jing Xia, and Xiping Zhou. 2019. DaVinci: A Scalable Architecture for Neural Network Computing. In Hot Chips Symposium. 1–44.
[50]
Ying-Chiao Liao, Chuan-Chi Wang, Chia-Heng Tu, Ming-Chang Kao, Wen-Yew Liang, and Shih-Hao Hung. 2020. PerfNetRT: Platform-Aware Performance Modeling for Optimized Deep Neural Networks. In 2020 International Computer Symposium (ICS). IEEE, 153–158.
[51]
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2015. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971 (2015).
[52]
Zhongyi Lin, Louis Feng, Ehsan K Ardestani, Jaewon Lee, John Lundell, Changkyu Kim, Arun Kejariwal, and John D Owens. 2022. Building a Performance Model for Deep Learning Recommendation Model Training on GPUs. arXiv preprint arXiv:2201.07821 (2022).
[53]
Guodong Liu, Sa Wang, and Yungang Bao. 2021. SEER: A Time Prediction Model for CNNs from GPU Kernel’s View. In 2021 30th International Conference on Parallel Architectures and Compilation Techniques (PACT). IEEE, 173–185.
[54]
Shaoli Liu, Zidong Du, Jinhua Tao, Dong Han, Tao Luo, Yuan Xie, Yunji Chen, and Tianshi Chen. 2016. Cambricon: An instruction set architecture for neural networks. In 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA). IEEE, 393–405.
[55]
Yao Lu, Song Bian, Lequn Chen, Yongjun He, Yulong Hui, Matthew Lentz, Beibin Li, Fei Liu, Jialin Li, Qi Liu, Rui Liu, Xiaoxuan Liu, Lin Ma, Kexin Rong, Jianguo Wang, Yingjun Wu, Yongji Wu, Huanchen Zhang, Minjia Zhang, Qizhen Zhang, Tianyi Zhou, and Danyang Zhuo. 2024. Computing in the Era of Large Generative Models: From Cloud-Native to AI-Native. arxiv:2401.12230 [cs.DC]
[56]
Peter Mattson, Christine Cheng, Gregory Diamos, Cody Coleman, Paulius Micikevicius, David Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, 2020. Mlperf training benchmark. Proceedings of Machine Learning and Systems 2 (2020), 336–349.
[57]
Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, Amar Phanishayee, and Matei Zaharia. 2020. Analysis and exploitation of dynamic pricing in the public cloud for ml training. In VLDB DISPA Workshop 2020.
[58]
Deepak Narayanan, Mohammad Shoeybi, Jared Casper, Patrick LeGresley, Mostofa Patwary, Vijay Korthikanti, Dmitri Vainbrand, Prethvi Kashinkunti, Julie Bernauer, Bryan Catanzaro, 2021. Efficient large-scale language model training on gpu clusters using megatron-lm. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 1–15.
[59]
Wei Niu, Jiexiong Guan, Yanzhi Wang, Gagan Agrawal, and Bin Ren. 2021. DNNFusion: accelerating deep neural networks execution with advanced operator fusion. In Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation. 883–898.
[60]
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32 (2019).
[61]
Xuan Peng, Xuanhua Shi, Hulin Dai, Hai Jin, Weiliang Ma, Qian Xiong, Fan Yang, and Xuehai Qian. 2020. Capuchin: Tensor-based gpu memory management for deep learning. In Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems. 891–905.
[62]
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, 2019. Language models are unsupervised multitask learners. OpenAI blog 1, 8 (2019), 9.
[63]
Jonathan Ragan-Kelley, Connelly Barnes, Andrew Adams, Sylvain Paris, Frédo Durand, and Saman Amarasinghe. 2013. Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines. Acm Sigplan Notices 48, 6 (2013), 519–530.
[64]
Aditya Rajagopal and Christos-Savvas Bouganis. 2021. perf4sight: A toolflow to model CNN training performance on Edge GPUs. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 963–971.
[65]
Samyam Rajbhandari, Olatunji Ruwase, Jeff Rasley, Shaden Smith, and Yuxiong He. 2021. Zero-infinity: Breaking the gpu memory wall for extreme scale deep learning. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 1–14.
[66]
Jie Ren, Jiaolin Luo, Kai Wu, Minjia Zhang, Hyeran Jeon, and Dong Li. 2021. Sentinel: Efficient tensor migration and allocation on heterogeneous memory systems for deep learning. In 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA). IEEE, 598–611.
[67]
Minsoo Rhu, Natalia Gimelshein, Jason Clemons, Arslan Zulfiqar, and Stephen W Keckler. 2016. vDNN: Virtualized deep neural networks for scalable, memory-efficient neural network design. In 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO). IEEE, 1–13.
[68]
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, 2016. Mastering the game of Go with deep neural networks and tree search. nature 529, 7587 (2016), 484–489.
[69]
Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).
[70]
Shikhar Singh, James Hegarty, Hugh Leather, and Benoit Steiner. 2022. A graph neural network-based performance model for deep learning applications. In Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming. 11–20.
[71]
Ajitesh Srivastava, Naifeng Zhang, Rajgopal Kannan, and Viktor K Prasanna. 2020. Towards high performance, portability, and productivity: Lightweight augmented neural networks for performance prediction. In 2020 IEEE 27th International Conference on High Performance Computing, Data, and Analytics (HiPC). IEEE, 21–30.
[72]
Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2020. Energy and policy considerations for modern deep learning research. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 13693–13696.
[73]
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2818–2826.
[74]
Mingxing Tan and Quoc Le. 2019. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning. PMLR, 6105–6114.
[75]
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023).
[76]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017).
[77]
Haojie Wang, Jidong Zhai, Mingyu Gao, Zixuan Ma, Shizhi Tang, Liyan Zheng, Yuanzhi Li, Kaiyuan Rong, Yuanyong Chen, and Zhihao Jia. 2021. { PET} : Optimizing Tensor Programs with Partially Equivalent Transformations and Automated Corrections. In 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21). 37–54.
[78]
Shang Wang, Peiming Yang, Yuxuan Zheng, Xin Li, and Gennady Pekhimenko. 2021. Horizontally Fused Training Array: An Effective Hardware Utilization Squeezer for Training Novel Deep Learning Models. Proceedings of Machine Learning and Systems 3 (2021), 599–623.
[79]
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020. Transformers: State-of-the-Art Natural Language Processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. Association for Computational Linguistics, Online, 38–45. https://www.aclweb.org/anthology/2020.emnlp-demos.6
[80]
Geoffrey X Yu, Tovi Grossman, and Gennady Pekhimenko. 2020. Skyline: Interactive In-Editor Computational Performance Profiling for Deep Neural Network Training. In Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology. 126–139.
[81]
Yi Zhai, Yu Zhang, Shuo Liu, Xiaomeng Chu, Jie Peng, Jianmin Ji, and Yanyong Zhang. 2023. Tlp: A deep learning-based cost model for tensor program tuning. In Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2. 833–845.
[82]
Quanlu Zhang, Zhenhua Han, Fan Yang, Yuge Zhang, Zhe Liu, Mao Yang, and Lidong Zhou. 2020. Retiarii: A Deep Learning { Exploratory-Training} Framework. In 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI 20). 919–936.
[83]
Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys (CSUR) 52, 1 (2019), 1–38.
[84]
Bojian Zheng, Nandita Vijaykumar, and Gennady Pekhimenko. 2020. Echo: Compiler-based GPU memory footprint reduction for LSTM RNN training. In 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA). IEEE, 1089–1102.
[85]
Lianmin Zheng, Chengfan Jia, Minmin Sun, Zhao Wu, Cody Hao Yu, Ameer Haj-Ali, Yida Wang, Jun Yang, Danyang Zhuo, Koushik Sen, 2020. Ansor: Generating { High-Performance} Tensor Programs for Deep Learning. In 14th USENIX symposium on operating systems design and implementation (OSDI 20). 863–879.
[86]
Hongyu Zhu, Mohamed Akrout, Bojian Zheng, Andrew Pelegris, Anand Jayarajan, Amar Phanishayee, Bianca Schroeder, and Gennady Pekhimenko. [n. d.]. Benchmarking and analyzing deep neural network training. In 2018 IEEE International Symposium on Workload Characterization (IISWC). IEEE, 88–100.
[87]
Hongyu Zhu, Amar Phanishayee, and Gennady Pekhimenko. 2020. Daydream: Accurately Estimating the Efficacy of Optimizations for { DNN} Training. In 2020 USENIX Annual Technical Conference (USENIX ATC 20). 337–352.

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    PACT '24: Proceedings of the 2024 International Conference on Parallel Architectures and Compilation Techniques
    October 2024
    375 pages
    ISBN:9798400706318
    DOI:10.1145/3656019
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