Advertisement

Evaluating Index Systems of High Energy Physics

  • Shaopeng DaiEmail author
  • Wanling Gao
  • Biwei Xie
  • Minghe Yu
  • Jia’nan Chen
  • Defei Kong
  • Rui Han
  • Jinheng Li
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 911)

Abstract

Nowadays, more and more scientific data has been produced through high-energy physics (HEP) facilities. Even in one particle physics experiment, the generated data reaches to petabytes scale. Retrieving data from massive data occupies a large proportion of data processing in HEP. Hence, the data query latency and throughput are the most important metrics for HEP data management. Inspired by the indexing technology of databases, the technology that improves the performance of data retrieval through the HEP data indexing, becomes the mainstream in the HEP data management. In this paper, focusing on two typical index systems–MySQL and HBase–for HEP data management, which are the typical SQL and NoSQL system respectively, we evaluate them from the perspectives of overall performance, system and micro-architecture behaviors. We find that HBase achieves higher performance than MySQL with the data scale increasing.

Keywords

High-energy physics Data management Event index HBase MySQL 

Notes

Acknowledgment

Our work in this paper is supported by NKRDPC, the National Key Research and Development Plan of China. (Grant No. 2016YFB1000600 and 2016YFB1000601).

References

  1. 1.
  2. 2.
  3. 3.
  4. 4.
  5. 5.
    Brun, R., Rademakers, F.: Root-an object oriented data analysis framework. Nucl. Instrum. Methods Phys. Res. Sect. A 389(1–2), 81–86 (1997)CrossRefGoogle Scholar
  6. 6.
    Cooper, B.F., Silberstein, A., Tam, E., Ramakrishnan, R., Sears, R.: Benchmarking cloud serving systems with YCSB. In: Proceedings of the 1st ACM Symposium on Cloud computing, pp. 143–154. ACM (2010)Google Scholar
  7. 7.
    Gao, W., et al.: Data motif-based proxy benchmarks for big data and AI workloads. In: IISWC 2018 (2018)Google Scholar
  8. 8.
    Gao, W., et al.: Data motifs: a lens towards fully understanding big data and AI workloads. In: 2018 27th International Conference on Parallel Architectures and Compilation Techniques (PACT) (2018)Google Scholar
  9. 9.
    Jia, Z., et al.: Characterizing and subsetting big data workloads. In: 2014 IEEE International Symposium on Workload Characterization (IISWC), pp. 191–201. IEEE (2014)Google Scholar
  10. 10.
    Jia, Z., et al.: Understanding big data analytics workloads on modern processors. IEEE Trans. Parallel Distrib. Syst. 28(6), 1797–1810 (2017)CrossRefGoogle Scholar
  11. 11.
    Karkhanis, T.S., Smith, J.E.: A first-order superscalar processor model. In: 31st Annual International Symposium on Computer Architecture, Proceedings, pp. 338–349. IEEE (2004)Google Scholar
  12. 12.
    Liu, B., et al.: High performance computing activities in hadron spectroscopy at BESIII. J. Phys. Conf. Ser. 523, 012008 (2014)CrossRefGoogle Scholar
  13. 13.
    Vora, M.N.: Hadoop-HBase for large-scale data. In: 2011 International Conference on Computer Science and Network Technology (ICCSNT), vol. 1, pp. 601–605. IEEE (2011)Google Scholar
  14. 14.
    Wang, L., et al.: Bigdatabench: a big data benchmark suite from internet services. In: 2014 IEEE 20th International Symposium on High Performance Computer Architecture (HPCA), pp. 488–499. IEEE (2014)Google Scholar
  15. 15.
    Yaodong, C., et al.: Data management challenges and event index technologies in high energy physics. J. Comput. Res. Dev. 54(2), 258–266 (2017)Google Scholar
  16. 16.
    Zheng, C., Zhan, J., Jia, Z., Zhang, L.: Characterizing OS behaviors of datacenter and big data workloads. In: 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp. 1079–1086. IEEE (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Shaopeng Dai
    • 1
    • 2
    Email author
  • Wanling Gao
    • 1
    • 2
  • Biwei Xie
    • 1
    • 2
  • Minghe Yu
    • 1
    • 2
  • Jia’nan Chen
    • 2
  • Defei Kong
    • 1
    • 2
  • Rui Han
    • 1
  • Jinheng Li
    • 3
  1. 1.Institute of Computing Technology, Chinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Sichuan UniversityChengduChina

Personalised recommendations