gssjoin: a gpu-based set similarity join...

Mestrado emCiência daComputação

Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

WellingtonS. Martins

Introduction

Background

The gSSJoinAlgorithm

Experiments

RelatedWork

Conclusionand FutureWork

gSSJoin: a GPU-based Set Similarity Join

Algorithm

Sidney R. Junior, Rafael D. Quirino, Leonardo A. Ribeiro,Wellington S. Martins

www.inf.ufg.br

October 7, 2016

1 / 35

Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Agenda

1 Introduction

2 Background

3 The gSSJoin Algorithm

4 Experiments

5 Related Work

6 Conclusion and Future Work

2 / 35

Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Introduction

Set similarity join returns all pairs of similar sets from a dataset.Sets are considered similar if the value returned by a setsimilarity function for them is not less than a given threshold.

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Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Applications

Integration

Cleaning

Plagiarism identi�cation

Data Mining

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Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Current Algorithms

Most state-of-the-art algorithms uses a technique called pre�x

�ltering to prune dissimilar sets by inspecting only a fraction ofthem.

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Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Limitations

Pre�x �ltering is only e�ective at high threshold values. Whenthe threshold value is low, the fraction needed to be inspectedis bigger and the remaining sets needed to be veri�ed after thepruning step is much higher.

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Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Contributions

A �ne-grained parallel algorithm for both indexing the dataand performing the set similarity joins.

A highly threaded GPU implementation that takesadvantage of intensive occupation, hierarchical memory,and coalesced memory access.

A scalable multi-GPU implementation that exploits bothdata parallelism and task parallelism.

Extensive experimental work with standard real-worldtextual datasets.

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Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Data Representation

We map the strings to sets of tokens called q-grams. q-gramsare sub-strings of length q obtained by sliding a window overthe characters of the input string.

3-grams of "gSSJoin": {"gSS", "SSJ", "SJo", "Joi", "oin"}

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Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Formal De�nition

Given two set collections R and S, a set similarity function sim,and a threshold τ , the set similarity join between R and Sreturns all scored set pairs 〈(r , s), τ ′〉 s.t. (r , s) ∈ R× S andsim (r , s) = τ ′ ≥ τ .

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Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Similarity Function

A set similarity function sim (r , s) returns a value in [0, 1] torepresent their similarity. A greater value indicates that r and s

have higher similarity.

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Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Similarity Function

Jaccard Similarity : J (r , s) = |r∩s||r∪s|

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Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Overlap Constraint

A predicate involving the Jaccard similarity and a threshold τcan be equivalently rewritten into a set overlap constraint:J (r , s) ≥ τ ⇐⇒ |r ∩ s| ≥ τ

1+τ (|r |+ |s|).

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Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Pre�x

For a threshold τ , we can identify all candidate matches of agiven set r using a pre�x of length |r | − d|r | · τe+ 1.

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Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Current Algorithms

14 / 35

Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Limitations of Current Algorithms

15 / 35

Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

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Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

17 / 35

Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

The gSSJoin Algorithm

Exploits massive parallelism instead of �ltering

Builds an inverted index on the pre-processing phase

Performs a set similarity search for each set

18 / 35

Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Pre-processing

19 / 35

Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Set Similarity Search

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Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Multi-GPU

The inverted index is created in all GPUs

The search sets are divided between the GPUs

21 / 35

Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Multi-GPU

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Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Experimental Setup

We implemented gSSJoin using the CUDA Toolkit version 7.5.The experiments were conducted on a machine running CentOs7.2.1511 64-bits, with 24 Intel Xeon E5-2620, 16GB of ECCRAM, and four GeForce Zotac Nvidia GTX Titan Black, with6GB of RAM and 2,880 CUDA cores each.

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Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Databases

Table : Dataset statistics.

Database Number of sets Max Mean Standard deviation

DBLP 100k 205 70 23.9

Net�ix 200k 54 30.54 8.2

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Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Performance Results

Figure : DBLP dataset using 3-grams.

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Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Performance Results

Figure : Net�ix dataset using 3-grams.

26 / 35

Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Performance Results

Figure : DBLP dataset using 2-grams.

27 / 35

Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Performance Results

Figure : Net�ix dataset using 2-grams.

28 / 35

Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Performance Results

Table : Execution Time (secs) Using Multiple GPUs, Threshold = 0.5

Number of GPUs: 1 2 3 4

DBLP 2gram 27.56 14.37 10.4 7.2

DBLP 3gram 16.73 8.77 6 4.47

Net�ix 2gram 41.10 21.63 14.37 11.2

Net�ix 3gram 23.52 12.69 8.5 6.5

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Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Related Work

The state-of-the-art algorithms are intrinsically sequential[Sarawagi and Kirpal, Chaudhuri et al. 2006, Bayardo et al.2007, Xiao et al. 2011, Ribeiro and Härder 2011, Wang etal. 2012].

A distributed memory MapReduce framework to performsimilarity joins was proposed by [Vernica et at. 2010].

Parallel solutions such as [Cruz et al. 2015] performapproximate set similarity joins using MinHash and LocalitySensitive Hashing.

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Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Conclusion

The gSSJoin algorithm has a much better performance than thestate-of-the-art CPU-based algorithms when the threshold islow and the database is more uniform.

In future work, we plan to incorporate candidate �lteringtechniques to obtain even greater speed-ups.

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Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

Questions?

email: srjsoftware@gmail.com

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Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

References

[Cruz et al. 2015]

Cruz, M. S. H., Kozawa, Y., Amagasa, T., and Kitagawa, H. (2015).GPU Acceleration of Set Similarity Joins. In DEXA, pages 384�398.

[Bayardo et al. 2007]

Bayardo, R. J., Ma, Y., and Srikant, R. (2007). Scaling up All PairsSimilarity Search. In WWW, pages 131�140.

[Chaudhuri et al. 2006]

Chaudhuri, S., Ganti, V., and Kaushik, R. (2006). A primitiveoperator for similarity joins in data cleaning. In ICDE, page 5.

[Ribeiro and Härder 2011]

Ribeiro, L. A. and Härder, T. (2011). Generalizing Pre�x Filtering toImprove Set Similarity Joins. Information Systems, 36(1):62�78.

[Sarawagi and Kirpal 2004]

Sarawagi, S. and Kirpal, A. E�cient Set Joins on SimilarityPredicates. In SIGMOD.

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Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

References

[Vernica et al. 2010]

Vernica, R., Carey, M. J., and Li, C. (2010). E�cient Parallel SetSimilarity Joins using MapReduce. In SIGMOD, pages 495�506.

[Xiao et al. 2011]

Xiao, C., Wang, W., Lin, X., Yu, J. X., and Wang, G. (2011). E�cientSimilarity Joins for Near-duplicate Detection. TODS, 36(3):15.

[Wang et al. 2012]

Wang, J., Li, G., and Feng, J. (2012). Can We Beat the Pre�xFiltering?: An Adaptive Framework for Similarity Join and Search. InSIGMOD, pages 85�96.

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Sidney R.Junior,

Rafael D.Quirino,

Leonardo A.Ribeiro,

Introduction

Background

Experiments

RelatedWork

GPU Architecture

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gssjoin: a gpu-based set similarity join...

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