1. Energy efficient calculations of text similarity measure on FPGA-accelerated computing platforms Michał Karwatowski 1,2, Paweł Russek 1,2, Maciej Wielgosz 1,2, Sebastian Koryciak 1,2, Kazimierz Wiatr 12 1 AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków, 2 ACK Cyfronet AGH, ul. Nawojki 11, 30-950 Kraków PPAM 06-09.09.2015 Kraków

2. Agenda Energy consumption in data centers Text processing Low energy FPGA cluster Experiments Results Conclusions and future work 2

3. Energy consumption in data centers HUGE energy consumption Complex algorithms require computing power Text processing Use different hardware 3

4. Text similarity calculation VSM TD-IDF Cosine similarity 4

5. Vector Space Model 5

6. Term Frequency – Inverse Document Frequency weighting scheme 6

7. Cosine similarity measure 7

8. Text comparison 8

9. ZedBoard Dual-core ARM Cortex-A9 667 MHz 512 MB RAM connected to PS FPGA XC7Z020 85k logic cells 140 block RAMs 9

10. Cluster 10

11. Hadoop 11

12. VC707 Intel Core i7 950 3066 MHz 12 GB RAM FPGA VX485T 485k logic cells 1030 block RAMs PCIe Gen2x8 12

13. Experiment scheme 13

14. Runtime for 1 – 8 vectors 14

15. Runtime for 1 – 32 vectors 15

16. Zynq energy consumption 16 3.99 W4.35 W

17. Vitrex energy consumption 17 220 W180 W

18. Average energy consumption [uJ] 18

19. Resource utilization 19

20. Conclusions Speedup achieved; Zynq 11.7 times faster Virtex 10.5 times faster Energy consumption: Zynq 10.8 times lower Virtex 12.9 times lower 20

21. Work in progress 32 internal channels in Zynq 192 internal channels in Virtex Database in DDR3 memory 21

22. Questions 22