Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

I/O-signature-based feature analysis and classification of high-performance computing applications

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

The demand for high-performance computing (HPC) resources in computing fields such as machine learning has increased significantly in recent years. Computing power has been growing exponentially to keep up with this demand. However, these gains have not been able to translate to performance improvement in real-world applications. One of the biggest reasons for this is performance degradation in terms of input/output (I/O) due to the increased storage latency and complex parallel I/O architecture of accessing data in distributed storage systems. In this study, we analyze application-specific I/O patterns to gain a deeper understanding of I/O throughput and the interaction between applications and the I/O system. Specifically, we analyze the importance of each feature of I/O patterns through feature analysis based on the collected monitoring information. We also investigate the feasibility of identifying the application based on the analyzed key features. To this end, we present the analysis accuracy and confusion matrix of four algorithms, including random forest, which are widely used as classification algorithms in the experimental results. The experiment results confirm that we can distinguish applications with an accuracy of more than 90% by using application-specific I/O patterns.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Availability of data and materials

No additional data or materials available.

References

  1. (2023) Iozone. http://www.iozone.org

  2. Ahmad, A., Dey, L.: A k-mean clustering algorithm for mixed numeric and categorical data. Data Knowl. Eng. 63(2), 503–527 (2007)

    Article  Google Scholar 

  3. Bang, J., Kim, C., Wu K, et al.: Hpc workload characterization using feature selection and clustering. In: Proceedings of the 3rd International Workshop on Systems and Network Telemetry and Analytics, pp. 33–40 (2020)

  4. Behzad, B., Byna, S., Snir, M.: Optimizing I/O performance of HPC applications with autotuning. ACM Trans. Parallel Comput. (TOPC) 5(4), 1–27 (2019)

    Google Scholar 

  5. Ben-David, A.: Comparison of classification accuracy using Cohen’s weighted kappa. Expert Syst. Appl. 34(2), 825–832 (2008)

    Article  Google Scholar 

  6. Betke, E., Kunkel, J.: Footprinting parallel I/O–machine learning to classify application’s I/O behavior. In: High Performance Computing: ISC High Performance 2019 International Workshops, Frankfurt, Germany, June 16–20, 2019, Revised Selected Papers 34, Springer, pp 214–226 (2019)

  7. Busch, A., Noorshams, Q., Kounev, S., et al.: Automated workload characterization for I/O performance analysis in virtualized environments. In: Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering. Association for Computing Machinery, New York, NY, USA, ICPE ’15, pp 265–276. https://doi.org/10.1145/2668930.2688050 (2015)

  8. Carns, P., Harms, K., Allcock, W., et al.: Understanding and improving computational science storage access through continuous characterization. ACM Trans. Storage (TOS) 7(3), 1–26 (2011)

    Article  Google Scholar 

  9. Fernández, A., Garcia, S., Herrera, F., et al.: Smote for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. J. Artif. Intell. Res. 61, 863–905 (2018)

    Article  MathSciNet  Google Scholar 

  10. Gainaru, A., Aupy, G., Benoit, A., et al.: Scheduling the I/O of HPC applications under congestion. In: 2015 IEEE International Parallel and Distributed Processing Symposium, IEEE, pp. 1013–1022 (2015)

  11. Han, H., Guo, X., Yu, H.: Variable selection using mean decrease accuracy and mean decrease gini based on random forest. In: 2016 7th IEEE International Conference on Software Engineering and Service Science (icsess), IEEE, pp. 219–224 (2016)

  12. Karimi, A.M., Paul, A.K., Wang, F.: I/O performance analysis of machine learning workloads on leadership scale supercomputer. Perform. Eval. 157(102), 318 (2022)

    Google Scholar 

  13. Kim, S., Sim, A., Wu, K., et al.: Towards HPC I/O performance prediction through large-scale log analysis. In: Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing, pp. 77–88 (2020)

  14. Kougkas, A., Dorier, M., Latham, R., et al.: Leveraging burst buffer coordination to prevent I/O interference. In: 2016 IEEE 12th International Conference on e-Science (e-Science), IEEE, pp. 371–380 (2016)

  15. Kumar, S., Ramasree, R.: Dimensionality reduction in automated evaluation of descriptive answers through zero variance, near zero variance and non frequent words techniques-a comparison. In: 2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO), IEEE, pp. 1–6 (2015)

  16. Li, D., Wang, Y., Xu, B., et al.: Pipuls: Predicting I/O patterns using LSTM in storage systems. In: 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD &IS), IEEE, pp. 14–21 (2019)

  17. Liu, Z., Lewis, R., Kettimuthu, R., et al.: Characterization and identification of HPC applications at leadership computing facility. In: Proceedings of the 34th ACM International Conference on Supercomputing, pp. 1–12 (2020)

  18. McKenna, R., Herbein, S., Moody, A., et al.: Machine learning predictions of runtime and io traffic on high-end clusters. In: 2016 IEEE International Conference on Cluster Computing (CLUSTER), IEEE, pp. 255–258 (2016)

  19. Neuwirth, S., Paul, A.K.: Parallel I/O evaluation techniques and emerging HPC workloads: a perspective. In: 2021 IEEE International Conference on Cluster Computing (CLUSTER), IEEE, pp. 671–679 (2021)

  20. Neuwirth, S.M.: Accelerating network communication and I/O in scientific high performance computing environments. PhD thesis (2019)

  21. Nitzberg, B., Schopf, J.M., Jones, J.P.: Pbs pro: Grid computing and scheduling attributes. Grid resource management: state of the art and future trends, pp. 183–190 (2004)

  22. Scheiner, S.M.: Manova: multiple response variables and multispecies interactions. In: Design and Analysis of Ecological Experiments. Chapman and Hall/CRC, Boca Raton, pp. 94–112 (2020)

  23. Schmidt, J.F., Kunkel, J.M.: Predicting I/O performance in HPC using artificial neural networks. Supercomput. Front. Innov. 3(3), 19–33 (2016)

    Google Scholar 

  24. Seo, B., Kang, S., Choi, J., et al.: Io workload characterization revisited: a data-mining approach. IEEE Trans. Comput. 63(12), 3026–3038 (2013)

    Article  MathSciNet  Google Scholar 

  25. Shende, S.S., Malony, A.D.: The tau parallel performance system. Int. J. High Perform. Comput. Appl. 20(2), 287–311 (2006)

    Article  Google Scholar 

  26. Sun, J., Sun, G., Zhan, S., et al.: Automated performance modeling of HPC applications using machine learning. IEEE Trans. Comput. 69(5), 749–763 (2020)

    Article  Google Scholar 

  27. Warrens, M.J.: Five ways to look at Cohen’s kappa. J. Psychol. Psychother. 5(4), 1 (2015)

    Article  Google Scholar 

  28. Xie, B., Tan, Z., Carns, P., et al.: Interpreting write performance of supercomputer I/O systems with regression models. In: 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS), IEEE, pp. 557–566 (2021)

  29. Yates, F., Grundy, P.M.: Selection without replacement from within strata with probability proportional to size. J. R. Stat. Soc.: Ser. B (Methodol.) 15(2), 253–261 (1953)

    Article  Google Scholar 

Download references

Funding

This work was supported by the Korea Institute of Science and Technology Information (Grant No. K-23-L02-C01-S01)

Author information

Authors and Affiliations

  1. Korea Institute of Science and Technology Information, 245 Daehak-ro, Daejeon, 34141, Republic of Korea

    Ju-Won Park, Jae-Kook Lee & Taeyoung Hong

  2. Department of Computer Science, Texas State University, San Marcos, TX, 78666, USA

    Xin Huang

Authors
  1. Ju-Won Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xin Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jae-Kook Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Taeyoung Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.P. and X.H. wrote the main manuscript and conducted statistical analysis of the I/O behavior. J.L. and T.H. implemented and applied the log module to collect the I/O logs. All authors reviewed the manuscript.

Corresponding author

Correspondence to Ju-Won Park.

Ethics declarations

Competing interest

The authors declare no competing interests.

Ethical approval

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix A

Appendix A

We below provide in Table 10 detailed description of applications. ANSYS is a widely used computer program for the solution of structural and heat transfer engineering analyses. GRIMs, CESM and WARF are computer systems that are widely used in the meteorological field to research and forecast the weather. GROMACS is a program package to perform molecular dynamics. It is designed for biochemical molecules like proteins, lipids and nucleic acids that have a lot of complicated bonded interactions. LAMMPS and NAMD are molecular dynamics simulation software designed to run efficiently on large-scale parallel computers. Quantum Espresso (QE) is an integrated suite of computer codes for electronic-structure calculations and materials modeling at the nanoscale. VASP is the Vienna Ab initio Simulation Package for atomic scale materials modeling.

Table 10 Application
Full size table

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Park, JW., Huang, X., Lee, JK. et al. I/O-signature-based feature analysis and classification of high-performance computing applications. Cluster Comput 27, 3219–3231 (2024). https://doi.org/10.1007/s10586-023-04139-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-023-04139-y

Keywords

Search

Navigation