Practical parallel string matching framework for RDF entailments with GPUs

Choksuchat, Chidchanok; Chantrapornchai, Chantana

doi:10.1007/s10796-016-9692-4

Cited by 2 publications

(1 citation statement)

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“…This limitation is eliminated in the next version (demonstrated in the future version [49]) where the vector is used in placed of the hash table. Also, if the total triplesâ Ȃ Ź sizes are too large for the available GPU memory, it can also be scaled out to use multiple GPUs to hold several portions of TripleID data similarly as in [50]. Streaming process is another solution to overcome this limit.…”

Section: Discussionmentioning

confidence: 99%

TripleID-Q: RDF Query Processing Framework Using GPU

Chantrapornchai

Choksuchat

2018

IEEE Trans. Parallel Distrib. Syst.

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Resource Description Framework (RDF) data represents information linkage around the Internet. It uses Internationalized Resources Identifier (IRI) which can be referred to external information. Typically, an RDF data is serialized as a large text file which contains millions of relationships. In this work, we propose a framework based on TripleID-Q, for query processing of large RDF data in a GPU. The key elements of the framework are 1) a compact representation suitable for a Graphics Processing Unit (GPU) and 2) its simple representation conversion method which optimizes the preprocessing overhead. Together with the framework, we propose parallel algorithms which utilize thousands of GPU threads to look for specific data for a given query as well as to perform basic query operations such as union, join, and filter. The TripleID representation is smaller than the original representation 3-4 times. Querying from TripleID using a GPU is up to 108 times faster than using the traditional RDF tool. The speedup can be more than 1,000 times over the traditional RDF store when processing a complex query with union and join of many subqueries.

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Section: Discussionmentioning

confidence: 99%