Energy efficient calculationsof text similarity measureon FPGA-acceleratedcomputing platforms

Michał Karwatowski1,2, Paweł Russek1,2,Maciej Wielgosz1,2, Sebastian Koryciak1,2,Kazimierz Wiatr12

1AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków,2ACK Cyfronet AGH, ul. Nawojki 11, 30-950 Kraków

PPAM 06-09.09.2015 Kraków

2Agenda

Energy consumption in data centers

Text processing

Low energy FPGA cluster

Experiments

Results

Conclusions and future work

3Energy consumptionin data centers

HUGE energy consumption

Complex algorithms require computing power

Text processing

Use different hardware

4Text similarity calculation

VSM

TD-IDF

Cosine similarity

5Vector Space Model

6Term Frequency – Inverse Document Frequency weighting scheme

7Cosine similarity measure

𝑠𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦=cos (𝜃 )= 𝐴 ∙𝐵‖𝐴‖‖𝐵‖

8Text comparison

9ZedBoard

Dual-core ARM Cortex-A9 667 MHz

512 MB RAM connected to PS

FPGA XC7Z02085k logic cells

140 block RAMs

10Cluster

11Hadoop

12VC707

Intel Core i7 950 3066 MHz

12 GB RAM

FPGA VX485T485k logic cells

1030 block RAMs

PCIe Gen2x8

13Experiment scheme

14Runtime for 1 – 8 vectors

15Runtime for 1 – 32 vectors

16Zynq energy consumption

3.99 W 4.35 W

17Vitrex energy consumption

220 W 180 W

18Average energy consumption [uJ]

19Resource utilization

20Conclusions

Speedup achieved;Zynq 11.7 times faster

Virtex 10.5 times faster

Energy consumption:Zynq 10.8 times lower

Virtex 12.9 times lower

21Work in progress

32 internal channels in Zynq

192 internal channels in Virtex

Database in DDR3 memory

22Questions

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