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iSSD-Based Collaborative Processing for Big Data Mining

효율적인 빅 데이터 마이닝을 위한 iSSD 기반 협업 처리 방안

  • Jo, Yong-Yoen (Hanyang University, Department of Computer and Software) ;
  • Kim, Sang-Wook (Hanyang University, Department of Computer and Software) ;
  • Bae, Duck-Ho (Samsung Electronics, Memory Business)
  • Received : 2017.01.09
  • Accepted : 2017.02.16
  • Published : 2017.02.28

Abstract

We address how to handle big data mining effectively using the intelligent SSD (iSSD). ISSD is a storage device equipped with computing power inside SSD for reducing the transferring cost and for processing data nearby SSD where the data is stored. We first introduce the structural characteristics of iSSD for efficient data processing. Then, we present how to process data mining algorithms by using iSSD. Finally, we discuss how to improve the performance of data mining algorithms significantly by exploiting heterogeneous computing environment where host CPUs and GPU coexist for maximizing the performance.

본 논문은 intelligent SSD (iSSD)를 통해 빅 데이터 마이닝을 효과적으로 처리하기 위한 방안에 대해서 소개한다. iSSD는 데이터 전송 비용을 줄이고 데이터가 저장된 장소와 가장 가까운 곳에서 데이터를 처리하기 위해, SSD 내부에 데이터 처리 능력을 부여한 장치이다. 본 논문에서는 먼저, iSSD의 등장 배경 및 효율적인 데이터 처리를 위한 iSSD 구조에 대해 소개한다. 더 나아가, iSSD를 이용하여 데이터 마이닝 알고리즘들을 빠르게 수행하는 방안을 소개한다. 끝으로, iSSD 뿐만 아니라 호스트 CPU, GPU 등 이질 (heterogeneous) 컴퓨팅 자원을 함께 활용하여 데이터 마이닝 알고리즘의 성능을 크게 향상시키는 협업 방안을 소개한다.

Keywords

References

  1. D. Bae et al., "Intelligent SSD: a turbo for big data mining," in Proc. ACM CIKM 2013, pp. 1553-1556, 2013.
  2. J. Dean and S. Ghemawat, "MapReduce: simplified data processing on large clusters," Commun. ACM, vol. 51, no. 1, pp. 107-113, 2008. https://doi.org/10.1145/1327452.1327492
  3. K. Shavachiko, et al., "The hadoop distributed file system," in Proc. IEEE MSST 2010, pp. 1-10, 2010.
  4. R. Greenwald, et al., Achieving extreme performance with Oracle Exadata, McGraw-Hill, 2011.
  5. P. Francisco, The netezza data appliance architecture: A platform for high performance data warehousing and analytics, IBM Redbooks 3, 2011.
  6. Y. Jo et, al., "On running data intensive algorithms with intelligent SSD and host CPU: a collaborative approach," in Proc. ACM SAC 2015, pp. 2060-2065, 2015.
  7. J. Do, et al., "Query processing on smart SSDs: opportunities and challenges," in Proc. ACM SIGMOD 2013, pp. 1221-1230, 2013.
  8. S. Kim, et al., "Fast, energy efficient scan inside flash memory SSDs," in Proc. ADMS 2011, Seattle, WA, Sept. 2011.
  9. E. Riedel, G. Gibson, and C. Faloutsos, "Active storage for large-scale data mining and multimedia," in Proc. VLDB 1998, pp. 62-73, 1998.
  10. D. Kim and S. Hwang, "An efficient wear-leveling algorithm for NAND flash SSD with multi-channel and multi-way architecture," J. KICS, vol. 39, no. 7, pp. 425-432, 2014.
  11. Y. Jo, et al., "Collaborative processing of data intensive algorithms with CPU, intelligent SSD, and GPU," in Proc. ACM SAC 2016, pp. 1865-1870, 2016.
  12. J. Zhang, M. Shihab, and M. Jung, "Power, energy and thermal considerations in SSD-Based I/O acceleration," in Proc. USENIX Workshop HotStorage, Philadelphia, PA, Jun. 2014.
  13. D. Shin, et al., "Malicious traffic detection using k-means," J. KICS, vol. 41 no. 02, pp. 277-284, 2015.
  14. L. Page, et al., The PageRank citation ranking: bringing order to the web, Technical Report, Stanford University, 1999.
  15. J. Kim and K. Park, "Personalized group recommendation using collaborative filtering and frequent pattern," J. KICS, vol. 41, no. 07, pp. 768-774, 2016. https://doi.org/10.7840/kics.2016.41.7.768
  16. N. Binkert, et al., "The Gem5 Simulator," ACM SIGARCH Computer Architecture News, vol. 39, no. 2, pp. 1-7, 2011.
  17. Y. Jo, et al., "Data mining in intelligent SSD: simulator-based evaluation," in Proc. BigComp 2016, pp. 123-128, 2016.
  18. V. Volkov and J. Demmel, "Benchmarking GPUs to tune dense linear algebra," in Proc. Int. Conf. Supercomputing(SC 2008), pp. 1-11, 2008.
  19. S. Ryoo, et al., "Optimization principles and application performance evaluation of a multi-threaded GPU using CUDA," in Proc. ACM SIGPLAN 2008, pp. 73-82, 2008.
  20. H. Oh and S. Ha, "A static scheduling heuristic for heterogeneous processors," in Euro-Par Parall. Process., pp. 573-577, 1996.
  21. E. Lee and D. Messerschmitt, "Synchronous data flow," in Proc. IEEE, vol. 75, no. 9, pp. 1235-1245, 1987.
  22. G. Jeh and J. Widom, "SimRank: a measure of structural-context similarity," in Proc. ACM SIGKDD 2002, pp. 538-543, 2002.