Skip to main content
SpringerLink
Log in

Accelerating time series motif discovery in the Intel Xeon Phi KNL processor

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Time series analysis is an important research topic of great interest in many fields. Recently, the Matrix Profile method, and particularly one of its implementations—the SCRIMP algorithm—has become a state-of-the-art approach in this field. This is a technique that brings the possibility of obtaining exact motifs from a time series, which can be used to infer events, predict outcomes, detect anomalies and more. However, the memory-bound nature of the SCRIMP algorithm limits the execution performance in some processor architectures. In this paper, we analyze the SCRIMP algorithm from the performance viewpoint in the context of the Intel Xeon Phi Knights Landing architecture (KNL), which integrates high-bandwidth memory (HBM) modules, and we combine several techniques aimed at exploiting the potential of this architecture. On the one hand, we exploit the multi-threading and vector capabilities of the architecture. On the other hand, we explore how to allocate data in order to take advantage of the available hybrid memory architecture that conjugates both the high-bandwidth 3D-stacked HBM and the DDR4 memory modules. The experimental evaluation shows a performance improvement up to \(190\,\times \) with respect to the sequential execution and that the use of the HBM memory improves performance in a factor up to \(5\,\times \) with respect to the DDR4 memory.

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

Access this article

Log in via an institution

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
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Notes

  1. Our implementation uses the functions test_and_set() and clear()from std::atomics library available since C++11. We declare an array of type std::atomic_flag, corresponding to each position of the Matrix Profile.

References

  1. Yu ZG, Anh V (2001) Time series model based on global structure of complete genome. Chaos Solitons Fractals 12(10):1827–1834

    Article  Google Scholar 

  2. Yoon CE, O’Reilly O, Bergen KJ, Beroza GC (2015) Earthquake detection through computationally efficient similarity search. Sci Adv 1(11):e1501057

    Article  Google Scholar 

  3. Li L, Su X, Zhang Y, Lin Y, Li Z (2015) Trend modeling for traffic time series analysis: an integrated study. IEEE Trans Intell Transp Syst 16(6):3430–3439

    Article  Google Scholar 

  4. Refit: smart homes and energy demand reduction. www.refitsmarthomes.org/index.php/data. Accessed 17 Jan 2018

  5. Yeh CCM, Zhu Y, Ulanova L, Begum N, Ding Y, Dau HA, Silva DF, Mueen A, Keogh E (2016) Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In: 16th IEEE International Conference on Data Mining (ICDM), pp 1317–1322

  6. Torkamani S, Lohweg V (2017) Survey on time series motif discovery. Wiley Interdiscip Rev Data Min Knowl Discov 7(2):e1199

    Article  Google Scholar 

  7. Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv 41(3):15

    Article  Google Scholar 

  8. Kanarachos S, Christopoulos SRG, Chroneos A, Fitzpatrick ME (2017) Detecting anomalies in time series data via a deep learning algorithm combining wavelets, neural networks and Hilbert transforms. Expert Syst Appl 85:292–304

    Article  Google Scholar 

  9. Lee TJ, Gottschlich J, Tatbul N, Metcalf E, Zdonik S (2018) Greenhouse: a zero-positive machine learning system for time-series anomaly detection. arXiv preprint arXiv:1801.03168

  10. Wu J, Zeng W, Yan F (2018) Hierarchical temporal memory method for time-series-based anomaly detection. Neurocomputing 273:535–546

    Article  Google Scholar 

  11. Chiu B, Keogh E, Lonardi S (2003) Probabilistic discovery of time series motifs. In: 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 493–498

  12. McGovern A, Rosendahl DH, Brown RA, Droegemeier KK (2011) Identifying predictive multi-dimensional time series motifs: an application to severe weather prediction. Data Min Knowl Discov 22(1–2):232–258

    Article  Google Scholar 

  13. Yi BK, Faloutsos C (2000) Fast time sequence indexing for arbitrary Lp norms. In: 26th International Conference on Very Large Data Bases (VLDB), pp 385–394

  14. Lin J, Keogh E, Wei L, Lonardi S (2007) Experiencing SAX: a novel symbolic representation of time series. Data Min Knowl Discov 15(2):107–144

    Article  MathSciNet  Google Scholar 

  15. Miniakhmetov R, Movchan A, Zymbler M (2015) Accelerating time series subsequence matching on the Intel Xeon Phi many-core coprocessor. In: 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp 1399–1404

  16. Chrysos G (2012) Intel Xeon Phi coprocessor (codename Knights Corner). In: IEEE Hot Chips 24 Symposium (HCS), pp 1–31

  17. Zhu Y, Yeh CCM, Zimmerman Z, Kamgar K, Keogh E (2018) Matrix profile XI: SCRIMP++: time series motif discovery at interactive speeds. In: 18th IEEE International Conference on Data Mining (ICDM)

  18. Zhu Y, Zimmerman Z, Senobari NS, Yeh CCM, Funning G, Mueen A, Brisk P, Keogh E (2016) Matrix profile II: exploiting a novel algorithm and GPUs to break the one hundred million barrier for time series motifs and joins. In: 16th IEEE International Conference on Data Mining (ICDM), pp 739–748

  19. Jeffers J, Reinders J, Sodani A (2016) Intel Xeon Phi processor high performance programming: knights, Landing edn. Morgan Kaufmann, Burlington

    Google Scholar 

  20. Loh GH (2008) 3D-stacked memory architectures for multi-core processors. ACM SIGARCH Comput Archit News 36(3):453–464

    Article  Google Scholar 

  21. Rakthanmanon T, Campana B, Mueen A, Batista G, Westover B, Zhu Q, Zakaria J, Keogh E (2012) Searching and mining trillions of time series subsequences under dynamic time warping. In: 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 262–270

  22. Superserver 5038k-i specs. www.supermicro.com/products/system/tower/5038/SYS-5038K-I.cfm. Accessed 17 Jan 2018

  23. Intel Xeon Phi 7210 specs. https://ark.intel.com/products/94033/Intel-Xeon-Phi-Processor-7210-16GB-1-30-GHz-64-core-. Accessed 17 Jan 2018

  24. Sodani A, Gramunt R, Corbal J, Kim HS, Vinod K, Chinthamani S, Hutsell S, Agarwal R, Liu YC (2016) Knights landing: second-generation Intel Xeon Phi product. IEEE Micro 36(2):34–46

    Article  Google Scholar 

  25. Khaldi D, Chapman B (2016) Towards automatic HBM allocation using LLVM: a case study with knights landing. In: Third Workshop on the LLVM Compiler Infrastructure in HPC (LLVM-HPC). IEEE, pp 12–20

  26. Feautrier P (1988) Array expansion. In: 2nd ACM International Conference on Supercomputing (ICS), pp 429–441

  27. Gutiérrez E, Plata O, Zapata EL (2000) A compiler method for the parallel execution of irregular reductions in scalable shared memory multiprocessors. In: 14th International Conference on Supercomputing (ICS), pp 78–87

  28. Intel VTune website. https://software.intel.com/en-us/vtune. Accessed 17 Jan 2018

  29. Intel Advisor website. https://software.intel.com/en-us/advisor. Accessed 17 Jan 2018

  30. Ghose S, Hsieh K, Boroumand A, Ausavarungnirun R, Mutlu O (2018) Enabling the adoption of processing-in-memory: challenges, mechanisms, future research directions. arXiv preprint arXiv:1802.00320

Download references

Author information

Authors and Affiliations

  1. Department of Computer Architecture, University of Málaga, 29071, Málaga, Spain

    Ivan Fernandez, Alejandro Villegas, Eladio Gutierrez & Oscar Plata

Authors
  1. Ivan Fernandez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alejandro Villegas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eladio Gutierrez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Oscar Plata
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ivan Fernandez.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fernandez, I., Villegas, A., Gutierrez, E. et al. Accelerating time series motif discovery in the Intel Xeon Phi KNL processor. J Supercomput 75, 7053–7075 (2019). https://doi.org/10.1007/s11227-019-02923-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-019-02923-5

Keywords

Search

Navigation