1. Faster File matching using GPGPU’s Deephan Mohan Professor: Dr
Faster File matching using GPGPU’s Deephan Mohan Professor: Dr.John Cavazos University of Delaware
Clarification about title:
Only NVIDIA GPUS. Extension to others in progress.
Inclusion of partial file matching support.
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2. Presentation Outline Introduction MD6 Algorithm CUDA MD6
Experiments and Results
Conclusion
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3. Introduction
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4. Introduction File matching Motivation
Indispensable in fields like forensics and Information security
Robustness of hashing algorithms used
Motivation
Advent of GPU computing
Faster file matching
Faster Hashing algorithms
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5. Faster file matching Hashing Algorithms
MD -4, -5, SHA -1, -2, -256, -512, Tiger, Whirlpool
Used in Integrity checking, checksum calculation, message authentication etc.
Existing file matching programs
SSDEEP
HASHDEEP
Tons of proprietary file matching programs
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6. The MD6 Algorithm
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7. Merkle Tree Computation proceeds from bottom up
Each leaf represents a data chunk
Each intermediate node represents a compression node
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8. The MD6 Algorithm MD6 Inputs: MD6 Compression :
M  the message to be hashed (mandatory).
d  message digest length desired, in bits (mandatory) .
K  key value (optional).
r  number of rounds (optional).
MD6 Compression :
MD6 word size  8 Bytes
MD6 Buffer size  64 words (512 Bytes)
Each Buffer is pre-processed
f : W64 W89  W16
W16 is post processed
The final hash is exactly d bits in length
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9. CUDA MD6
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10. CUDA MD6 Implementation
Step (i): Host buffers in the content of the source file
Step (ii): Allocates adequate memory on the device
Step (iii): Invoke kernels
md6_compress_block() – Preprocessing module
md6_compress() – Compression module
md6_rewrite() – MD6 hash aggregation module
Step (iv): Do step iii N+1 times to generate the final hash
Step (v): Perform hash comparison
Step (vi): Store hash in the hashdb
Step (ii): If file size larger, chunk it.
Step(iii): Sequential run of three kernels. Keep data in GPU memory during computational time without offloading it to the host. Improves performance.
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SAAHPC 2010

11. CUDA MD6 md6_compress_block() md6_compress() md6_rewrite()
Data preprocessing module
f : W64  W89
<<<Grid, Threads>>>  <<<Total number of buffers, 1>>>
md6_compress()
Performs MD6 compression
<<<Grid, Threads>>>  <<<Total number of buffers, 16>>>
md6_rewrite()
Performs MD6 Hash Aggregation
f : W89  W16
<<<Grid, Threads>>>  <<<(Total number of buffers/4), 1>>>
Sequential run of three kernels
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12. Preprocessing kernel Transforms each MD6 buffer
15 words – constant vector (Q)
8 words - Key (K)
U,V – unique control words
Last 64 words – Data chunk
A 15 word vector composed of constants (primes)
Header
Data chunk (64)
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13. CUDA MD6 Compression Kernel
For each block do
Set index to blockID
For each data[index] do
Set i to n + ThreadID: /* 16 steps */
x = Si-n xor Ai-n xor Ai-t0
x = x xor (Ai-t1 ^ Ai-t2) xor (Ai-t3 ^ Ai-t4)
x = x xor (x >> ri-n)
x = x xor (x << ri-n)
exit CUDA block
exit CUDA kernel call
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14. CUDA MD6 Compression Coalesced memory reads and writes
Use of constant and shared memory within kernel
Compression function loop unrolled
Compression rounds
Integrity
Sliding window thread access
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15. CUDA MD6 Execution Thread block STEP 2: Compress data
Call md6_compress ()
<<< Total buffers, Threads>>>  <<< 8, 16>>>
STEP 1:
Read in data
Call md6_compress_block ()
<<< Total buffers, Threads>>>  <<< 8, 1>>>
Grid
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16. CUDA MD6 Execution STEP 3: Write hash into appropriate node
Call md6_rewrite()
<<< Total buffers, Threads>>>  <<< 2, 1>>>
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17. CUDA MD6 Execution STEP 1: STEP 2: Read in data Compress data
Call md6_compress ()
<<< Total buffers, Threads>>>  <<< 2, 16>>>
STEP 1:
Read in data
Call md6_compress_block ()
<<< Total buffers, Threads>>>  <<< 2, 1>>>
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18. CUDA MD6 Execution Final step Write out the final hash
End of CUDA kernel
STEP 3:
Write hash into appropriate node
Call md6_rewrite()
<<< Total buffers, Threads>>>  <<< 2, 1>>>
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19. CUDA MD6 for File matching
Absolute file matching
Message digest is unique
User can input predetermined set of hashes
Comparison of Input hashes with GPU generated hashes
File matching can be done in two modes
Direct Hashing (Single files)
Recursive Hashing (Archive of files)
Hashing Larger Files
Larger files are broken down into data chunks
Each chunk is hashed and finally aggregated
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20. Experiments and Results
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21. Benchmarking platform
GPU
Nvidia GeForce 8800 GTX card (112 cores)
CUDA toolkit version 2.2
CPU
Quad-core Intel Xeon E5335 CPU
Sequential Iterative implementation of MD6
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22. Experiment 1: Executing CUDA MD6 on single files
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23. Experiment 2: Executing CUDA MD6 on archive of files
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24. Experiment 3: Executing CUDA MD6 with varying buffer sizes
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25. Number of compression rounds Vs Speedup
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26. Wall clock time Vs Kernel execution time
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27. Conclusion Speedup ranging from 2X to exceeding 250X
Performance degraders
Host to device data transfer, Device initialization, Idle threads
Faster hashing also depends on hash integrity
Speedup should scale with increased number of GPU cores
Point 2:
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28. Questions…
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29. Thank you!!!
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