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, Kraków, 2 ACK Cyfronet AGH, ul. Nawojki 11, Kraków PPAM Kraków

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

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

Text similarity calculation VSM TD-IDF Cosine similarity 4

Vector Space Model 5

Term Frequency – Inverse Document Frequency weighting scheme 6

Cosine similarity measure 7

Text comparison 8

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

Cluster 10

Hadoop 11

VC707 Intel Core i MHz 12 GB RAM FPGA VX485T 485k logic cells 1030 block RAMs PCIe Gen2x8 12

Experiment scheme 13

Runtime for 1 – 8 vectors 14

Runtime for 1 – 32 vectors 15

Zynq energy consumption W4.35 W

Vitrex energy consumption W180 W

Average energy consumption [uJ] 18

Resource utilization 19

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

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

Questions 22