1. Data Classification Hardware Acceleration
Ivan Manuylov
Senior Software Developer, DLP Research
Kaspersky Lab
Security Analyst Summit 2013, Puerto Rico

2. Speed
PAGE 2 Security Analyst Summit 2013, Puerto Rico

3. Speed
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4. Speed?
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5. Speed?
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6. Speed?
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7. Speed?
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8. Hardware

9. Cores
Who has access to 1000+ cores?
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10. Purpose of GPU
General Purpose computation on GPU
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11. How to use GPGPU
Direct Compute
 Cross platform  Cross platform  Windows
 Multi vendor  NVIDIA  Multi vendor
 C kernel  C/C++ kernel  HLSL kernel
 Languages:  Languages:  Languages:
 C/C++  C/C++  C++
 .NET  .NET  C++ AMP
 Java  Java  .NET
 Ruby  Fortran
 Python  Perl
 Python
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12. Kernel code
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13. Using of Hardware Acceleration

14. Using of Hardware Acceleration
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15. Research

16. Hardware
CPU GPU
Intel Core i5 – 750 NVIDIA GeForce GTS 450
2.66 GHz 1.6 GHz
4 cores 192 cores
DDR3: GDDR5:
667 MHz 1.8 GHz
12 Gb/s 58 Gb/s
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17. Fingerprinting

18. Fingerprinting
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19. Fingerprinting
100 000 hashes
100 Kb
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20. Fingerprinting
2x faster
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21. Fingerprinting
1800
µs
1600
1400
CPU GPU
is is
1200
2x
1000 better better
800 CPU
GPU
600
400
200
0
0 5 10 15 20 25
Kb
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22. Keywords

23. Keywords
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24. Keywords
What is the performance boost?
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25. Keywords
What is the performance boost?
10+
times
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26. Keywords
60000
µs 1000 keywords
50000
GPU
40000
is
30000
better
10X CPU
GPU
20000
10000
0
0 10 20 30 40 50 60 70
Kb
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27. Epilogue

28. Summary
Fingerprints search 2x
Keywords search 10x
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29. We can do it!
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30. Data Classification Hardware Acceleration
Thank you!
Ivan Manuylov
Senior Software Developer, DLP Research,
Kaspersky Lab
Ivan.Manuylov@kaspersky.com
Security Analyst Summit 2013, Puerto Rico