This document discusses parallel frequent pattern mining on GPUs. It presents several key papers on using GPUs to accelerate FP-growth algorithms for frequent itemset mining. It then describes the proposed GPU-based FP-growth algorithm, which parallelizes mining iterations by assigning each transaction to a thread. Header tables are stored in coalesced memory to improve access efficiency. Experimental results show the GPU approach outperforms CPU FP-growth by 1.2-8x and is faster than the best existing GPU method by 1.8-42x on several datasets.

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4. [KW ‘02] K. Wang, L. Tang, J. Han, and J. Liu, “Top down fp-growth for association rule mining,”
in Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data
Mining, ser. PAKDD ’02.
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7. [F.Z. ‘16] F. Zhang, P. Di, H. Zhou, X. Liao, and J. Xue, “Regtt: Accelerating tree traversals on gpus by exploiting regularities,”
in 2016 ICPP
[M.G. ‘13] M. Goldfarb, Y. Jo, and M. Kulkarni, “General transformations for gpu execution of tree traversals,” in Proceedings
of the International Conference on High Performance Computing, Networking, Storage and Analysis, ser. SC ’13.
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8. Index 0
item
(parent item, the index of parent node, support)
coalesced access
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9. (a)
(b)
(a)
(b)
53x
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[XH ’10] X. Huang, C. I. Rodrigues, S. Jones, I. Buck and W. Hwu,
"XMalloc: A Scalable Lock-free Dynamic Memory Allocator for Many-core Machines,"
2010 10th IEEE International Conference on Computer and Information Technology
[MS ’12] M. Steinberger, M. Kenzel, B. Kainz and D. Schmalstieg,
"ScatterAlloc: Massively parallel dynamic memory allocation for the GPU,"
2012 Innovative Parallel Computing (InPar)

11. Input table set
Output table
set
Mining Iteration 0
Input table set
Output table
set
Mining Iteration 1
Input table set
Output table
set
Mining
Iteration 2
Header
table 0
Header
table 1
Header
table k
Info of an item : node, support, etc.
Header table XY: the header table of pattern XY
Info of
item 0
Info of
item 1
Info of
item k-1
Thread
blocks
Out of order
Header
table 1k
Header
table 2k
Header
table (k-1)k
Header
table 13
Header
table 59
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2 0 1Remap
Size 0 Size 1 Size 2
Size 1 Size 2 Size 0
exclusive prefix-sum
0 Size 1 Size 1+2
Write offset
Calculating the write offsets

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2 0 1
0 Size 1 Size 1+2
Table Table Table
Write offset
Remap
Using the write offsets

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I I
I
I
Idx:0 Idx:1
Idx:2
Idx:3
0 2 3 1 4
Thread 0, Thread 1, Thread 2, Thread 3
I
Idx:4
Thread block size: 4

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[CB ’05] C. Borgelt, “An implementation of the fp-growth algorithm,” OSDM ’05.(workshop)
[FW ’14] F. Wang and B. Yuan, “Parallel frequent pattern mining without candidate generation on gpus,”
2014 IEEE ICDMW
[HJ ‘17]H. Jiang and H. Meng, “A parallel fp-growth algorithm based on gpu,” 2017 IEEE ICEBE
[WF ’09] W. Fang, M. Lu, X. Xiao, B. He, and Q. Luo, “Frequent itemset mining on graphics processors,” DaMoN ’09
[Chon ’18] K.-W. Chon, S.-H. Hwang, and M.-S. Kim, “Gminer: A fast gpu-based frequent itemset mining
method for large-scale data,” InformationSciences, vol. 439-440, pp. 19 – 38, 2018.
Not open source,
and the normalized results are too bad

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Dataset #items #trans Size Threshold
(%)
v.s. CPU
FP-
growth
v.s. the
best GPU
Apriori
chess 75 3196 335KB 35~60 1.2x~0.7x 1.8x~3.3x
retail 16470 88163 4MB 0.07~0.1 2x ~ 1.8x 9.8x ~ 8.6x
accident 468 340184 34MB 20~40 8x~6x 16x ~ 42x
kosarak 41270 990002 30MB 0.3 ~ 0.6 6x~7x 12x ~ 40x
Webdoc 5267656 1692082 1.48GB 20 ~ 25 12x~7x 12x ~ 86x
Fewer patternsPerformance criteria :execution time
Operations can be processed offline are excluded.

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21. Generated frequent patterns
04 14 24 34 4
Header table of pattern 24
0:5
2:2
3:2
4:2
1:3
2:1
3:1
4:1
4:2
2
The length of index array
Depend on hash function
0 1
3 1
1 1
Idx:0
Idx:1
0
3
1
2
The position is decided by hash value
# node
# support
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Assume the support threshold is 3
A new frequent pattern 024:3 will be generated