Abstract
Neural networks are extensively utilized for building performance prediction models for high-performance computing systems. It is challenging to construct the neural network architecture that provides accurate performance (runtime) predictions by exploring the number of layers and neurons in each layer. Automated machine learning (AutoML) using neural architecture search (NAS) frameworks such as Auto-Keras have been proposed to tune neural networks automatically. Researchers have utilized AutoML frameworks for many application domains to build neural networks automatically, providing accurate predictions. However, AutoML has not been explored for performance prediction domain based on our literature survey. Hence, our goal is to show the feasibility of AutoML-based algorithm for performance prediction of HPC systems. In this paper, we propose a novel AutoML based algorithm that builds a neural network model layer-by-layer for performance prediction. Our algorithm takes decisions based on prediction accuracy metric values to add a new layer to tune the network. We have performed extensive experiments utilizing applications from SPEC CPU 2006, SPEC CPU 2017 benchmark suites. Performance prediction accuracy results of RMSE (Root Mean Squared Error), MedAE (Median Absolute Error) and MedAPE (Median Absolute Percentage Error) from our experiments demonstrate superior performance of our algorithm compared to the state-of-the-art Auto-Keras framework.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agarwal, R., Chopra, S., Christophides, V., Georgantas, N., Issarny, V.: Detecting mobile crowdsensing context in the wild. In: Proceedings - IEEE International Conference on Mobile Data Management, June 2019, pp. 170–175. Institute of Electrical and Electronics Engineers Inc. (2019). https://doi.org/10.1109/MDM.2019.00-60
Chen, W., Dong, X., Chen, H., Wang, Q., Yu, X., Zhang, X.: Performance evaluation of convolutional neural network on tianhe-3 prototype. J. Supercomput. 77(11), 12647–12665 (2021). https://doi.org/10.1007/S11227-021-03759-8/FIGURES/10, https://link.springer.com/article/10.1007/s11227-021-03759-8
Chollet, F., et al.: Keras. https://keras.io (2015)
Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: a survey. J. Mach. Learn. Res. 20, 1–21 (2019). https://doi.org/10.5555/3322706.3361996, http://jmlr.org/papers/v20/18-598.html
Erickson, N., et al.: AutoGluon-tabular: robust and accurate AutoML for structured data (2020). https://arxiv.org/abs/2003.06505v1
Ferreira, L., Pilastri, A., Martins, C.M., Pires, P.M., Cortez, P.: A comparison of automl tools for machine learning, deep learning and XGBoost. In: Proceedings of the International Joint Conference on Neural Networks, July 2021. Institute of Electrical and Electronics Engineers Inc. (2021). https://doi.org/10.1109/IJCNN52387.2021.9534091
Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M., Hutter, F.: Auto-sklearn 2.0: hands-free AutoML via meta-learning (2020). https://arxiv.org/abs/2007.04074v2
García-Domínguez, M., Domínguez, C., Heras, J., Mata, E., Pascual, V.: UFOD: an AutoML framework for the construction, comparison, and combination of object detection models. Pattern Recogn. Lett. 145, 135–140 (2021). https://doi.org/10.1016/J.PATREC.2021.01.022
Gupta, G., Katarya, R.: EnPSO: an AutoML technique for generating ensemble recommender system. Arab. J. Sci. Eng. 46(9), 8677–8695 (2021). https://doi.org/10.1007/S13369-021-05670-Z/FIGURES/8, https://link.springer.com/article/10.1007/s13369-021-05670-z
Halvari, T., Nurminen, J.K., Mikkonen, T.: Robustness of AutoML for time series forecasting in sensor networks. In: 2021 IFIP Networking Conference, IFIP Networking 2021. Institute of Electrical and Electronics Engineers Inc. (2021). https://doi.org/10.23919/IFIPNETWORKING52078.2021.9472199
Jin, H., Song, Q., Hu, X.: Auto-Keras: an efficient neural architecture search system. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, New York, NY, USA (2019). https://doi.org/10.1145/3292500, https://doi.org/10.1145/3292500.3330648
Kulkarni, G.N., Ambesange, S., Vijayalaxmi, A., Sahoo, A.: Comparision of diabetic prediction AutoML model with customized model. In: Proceedings - International Conference on Artificial Intelligence and Smart Systems, ICAIS 2021, pp. 842–847. Institute of Electrical and Electronics Engineers Inc. (2021). https://doi.org/10.1109/ICAIS50930.2021.9395775
Kwok, T.Y., Yeung, D.Y.: Constructive algorithms for structure learning in feedforward neural networks for regression problems. IEEE Trans. Neural Netw. 8(3), 630–645 (1997). https://doi.org/10.1109/72.572102
Lee, M., Ahn, H., Hong, C.H., Nikolopoulos, D.S.: gShare: a centralized GPU memory management framework to enable GPU memory sharing for containers. Future Gener. Comput. Syst. 130, 181–192 (2022). https://doi.org/10.1016/J.FUTURE.2021.12.016, https://linkinghub.elsevier.com/retrieve/pii/S0167739X21004970
Liu, D., et al.: AutoGenome: an AutoML tool for genomic research. Artif. Intell. Life Sci. 1, 100017 (2021). https://doi.org/10.1016/J.AILSCI.2021.100017
Lopez, L., Guynn, M., Lu, M.: Predicting computer performance based on hardware configuration using multiple neural networks. In: Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, pp. 824–827. Institute of Electrical and Electronics Engineers Inc. (2019). https://doi.org/10.1109/ICMLA.2018.00132
Luo, X., Liu, D., Huai, S., Kong, H., Chen, H., Liu, W.: Designing efficient DNNs via hardware-aware neural architecture search and beyond. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. (2021). https://doi.org/10.1109/TCAD.2021.3100249
Malakar, P., Balaprakash, P., Vishwanath, V., Morozov, V., Kumaran, K.: Benchmarking machine learning methods for performance modeling of scientific applications. In: Proceedings of PMBS 2018: Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems, Held in conjunction with SC 2018: The International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 33–44. Institute of Electrical and Electronics Engineers Inc., Dallas, Texas, USA (2019). https://doi.org/10.1109/PMBS.2018.8641686
Mankodi, A., Bhatt, A., Chaudhury, B.: Evaluation of neural network models for performance prediction of scientific applications. In: IEEE Region 10th Annual International Conference, Proceedings/TENCON. November 2020, pp. 426–431. Institute of Electrical and Electronics Engineers Inc. (2020). https://doi.org/10.1109/TENCON50793.2020.9293788
Mariani, G., Anghel, A., Jongerius, R., Dittmann, G.: Predicting cloud performance for HPC applications before deployment. Futur. Gener. Comput. Syst. 87, 618–628 (2018). https://doi.org/10.1016/j.future.2017.10.048
Nikitin, N., et al.: Automated evolutionary approach for the design of composite machine learning pipelines. Futur. Gener. Comput. Syst. 127, 109–125 (2022). https://doi.org/10.1016/J.FUTURE.2021.08.022
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011). http://scikit-learn.sourceforge.net
Rakhshani, H., et al.: Automated machine learning for information retrieval in scientific articles. In: 2020 IEEE Congress on Evolutionary Computation, CEC 2020 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc. (2020). https://doi.org/10.1109/CEC48606.2020.9185893
Ren, P., et al.: A comprehensive survey of neural architecture search. ACM Comput. Surv. (CSUR), 54(4), 76 (2021). https://doi.org/10.1145/3447582
Srivastava, A., Zhang, N., Kannan, R., Prasanna, V.K.: Towards high performance, portability, and productivity: lightweight augmented neural networks for performance prediction. In: Proceedings - 2020 IEEE 27th International Conference on High Performance Computing, Data, and Analytics, HiPC 2020, pp. 21–30. Institute of Electrical and Electronics Engineers Inc. (2020). https://doi.org/10.1109/HIPC50609.2020.00016
Sun, J., Sun, G., Zhan, S., Zhang, J., Chen, Y.: Automated performance modeling of HPC applications using machine learning. IEEE Trans. Comput. 69(5), 749–763 (2020). https://doi.org/10.1109/TC.2020.2964767
Wang, K., Guo, P.: A robust automated machine learning system with pseudoinverse learning. Cogn. Comput. 13(3), 724–735 (2021). https://doi.org/10.1007/S12559-021-09853-6, https://link.springer.com/article/10.1007/s12559-021-09853-6
Wang, K., Liu, Z., Lin, Y., Lin, J., Han, S.: Hardware-centric AutoML for mixed-precision quantization. Int. J. Comput. Vis. 128(8–9), 2035–2048 (2020). https://doi.org/10.1007/S11263-020-01339-6/FIGURES/11, https://link.springer.com/article/10.1007/s11263-020-01339-6
Wang, C.C., Liao, Y.C., Kao, M.C., Liang, W.Y., Hung, S.H.: Toward accurate platform-aware performance modeling for deep neural networks. ACM SIGAPP Appl. Comput. Rev. 21(1), 50–61 (2021). https://doi.org/10.1145/3477133.3477137, https://dl.acm.org/doi/abs/10.1145/3477133.3477137
Yang, J., Shi, R., Ni, B.: MedMNIST classification decathlon: a lightweight automl benchmark for medical image analysis. In: Proceedings - International Symposium on Biomedical Imaging, April 2021, pp. 191–195. IEEE Computer Society (2021). https://doi.org/10.1109/ISBI48211.2021.9434062
Zhang, L., et al.: AutoGGN: a gene graph network AutoML tool for multi-omics research. Artif. Intell. Life Sci. 1, 100019 (2021). https://doi.org/10.1016/J.AILSCI.2021.100019
Zimmer, L., Lindauer, M., Hutter, F.: Auto-pytorch tabular: multi-fidelity metalearning for efficient and robust AutoDL. IEEE Trans. Pattern Anal. Mach. Intell. 43(9), 3079–3090 (2020). https://doi.org/10.1109/TPAMI.2021.3067763, https://arxiv.org/abs/2006.13799v3
Zöller, M.A., Huber, M.F.: Benchmark and survey of automated machine learning frameworks. J. Artif. Intell. Res. 70, 409–472 (2021). https://doi.org/10.1613/JAIR.1.11854, https://dl.acm.org/doi/abs/10.1613/jair.1.11854
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mankodi, A., Bhatt, A., Chaudhury, B. (2023). An AutoML Based Algorithm for Performance Prediction in HPC Systems. In: Takizawa, H., Shen, H., Hanawa, T., Hyuk Park, J., Tian, H., Egawa, R. (eds) Parallel and Distributed Computing, Applications and Technologies. PDCAT 2022. Lecture Notes in Computer Science, vol 13798. Springer, Cham. https://doi.org/10.1007/978-3-031-29927-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-29927-8_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-29926-1
Online ISBN: 978-3-031-29927-8
eBook Packages: Computer ScienceComputer Science (R0)