TripleID-Q: RDF Query Processing Framework Using GPU

Chantrapornchai, Chantana; Choksuchat, Chidchanok

doi:10.1109/tpds.2018.2814567

Cited by 13 publications

(5 citation statements)

References 21 publications

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“…TripleID-Q [6] relies on the relational row-based format to represent the RDF data and converted the triple into integer IDs to compact the data. The sub-result triples are simultaneously marked by GPU threads.…”

Section: Sparql Optimization On Gpumentioning

confidence: 99%

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VEDAS: an efficient GPU alternative for store and query of large RDF data sets

Makpaisit

Chantrapornchai

2021

J Big Data

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Resource Description Framework (RDF) is commonly used as a standard for data interchange on the web. The collection of RDF data sets can form a large graph which consumes time to query. It is known that modern Graphic Processing Units (GPUs) can be employed to execute parallel programs in order to speedup the running time. In this paper, we propose a novel RDF data representation along with the query processing algorithm that is suitable for GPU processing. Since the main challenges of GPU architecture are the limited memory sizes, the memory transfer latency, and the vast number of GPU cores. Our system is designed to strengthen the use of GPU cores and reduce the effect of memory transfer. We propose a representation consists of indices and column-based RDF ID data that can reduce the GPU memory requirement. The indexing and pre-upload filtering techniques are then applied to reduce the data transfer between the host and GPU memory. We add the index swapping process to facilitate the sorting and joining data process based on the given variable and add the pre-upload step to reduce the size of results’ storage, and the data transfer time. The experimental results show that our representation is about 35% smaller than the traditional NT format and 40% less compared to that of gStore. The query processing time can be speedup ranging from 1.95 to 397.03 when compared with RDF3X and gStore processing time with WatDiv test suite. It achieves speedup 578.57 and 62.97 for LUBM benchmark when compared to RDF-3X and gStore. The analysis shows the query cases which can gain benefits from our approach.

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Section: Sparql Optimization On Gpumentioning

confidence: 99%

“…In this work, we propose a framework based on the TripleID representation [6]. To make it fit inside the GPU memory, we compress the representation by transforming them into a column format with column indices.…”

mentioning

confidence: 99%

VEDAS: an efficient GPU alternative for store and query of large RDF data sets

Makpaisit

Chantrapornchai

2021

J Big Data

Self Cite

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show abstract

“…Utilizing them at the same time can increase the processing speedup. In this work, we propose a framework which is based on the TripleID representation [6]. To make it fit inside the GPU memory, we compress the representation by transforming them into a column format with column indices which can save a lot of memory since the RDF data is usually sparse.…”

Section: Introductionmentioning

confidence: 99%

VEDAS: An Efficient GPU Alternative for Store and Query of Large RDF Data Sets

Makpaisit

Chantrapornchai

2021

Preprint

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Resource Description Framework (RDF) is commonly used as a standard for data interchange on the web. The collection of RDF data sets can form a large graph which consume time to query. It is known that modern Graphic Processing Units (GPUs) can be employed to execute parallel programs in order to speedup the running time. In this paper, we propose a novel RDF data representation along with the query processing algorithm that is suitable for GPU processing. Since the main challenges of GPU architecture are the limited memory sizes, the memory transfer latency, and the vast number of GPU cores. Our system is designed to strengthen the use of GPU cores and reduce the effect of memory transfer. We propose a representation consists of indices and column-based RDF ID data that can save GPU memory requirement. The indices and pre-upload filtering technique are then applied to reduce the data transfer between host and GPU memory. We add the index swapping process to facilitate the sort and join the data with the given variable and add the pre-upload step to reduce the size of results’ storage, and the data transfer time. The experimental results show that our representation is about 35% smaller that the traditional NT format and 40% less compared to that of gStore. The query processing time can be speedup ranging from 1.95 to 397.03 when compared with RDF3X and gStore processing time with WatDiv testsuite. It achieves speedup 578.57 and 62.97 for LUBM benchmark when compared to RDF-3X and gStore. The analysis shows the query cases which can gain benefits from our approach.

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“…There have been several attempts to implement reasoners in DL using parallel computation [8,9]. The most relevant effort here is Chantrapornchai and Choksuchat's [10] recently proposed GPU-accelerated framework for RDF query processing, TripleID-Q. The framework maintains a separate hash table for each of the three arguments (subject, predicate, object) of the RDF triples.…”

Section: Introductionmentioning

confidence: 99%

CONNER: A Concurrent ILP Learner in Description Logic

Algahtani

Kazakov

2020

Inductive Logic Programming

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Machine Learning (ML) approaches can achieve impressive results, but many lack transparency or have difficulties handling data of high structural complexity. The class of ML known as Inductive Logic Programming (ILP) draws on the expressivity and rigour of subsets of First Order Logic to represent both data and models. When Description Logics (DL) are used, the approach can be applied directly to knowledge represented as ontologies. ILP output is a prime candidate for explainable artificial intelligence; the expense being computational complexity. We have recently demonstrated how a critical component of ILP learners in DL, namely, cover set testing, can be speeded up through the use of concurrent processing. Here we describe the first prototype of an ILP learner in DL that benefits from this use of concurrency. The result is a fast, scalable tool that can be applied directly to large ontologies.

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TripleID-Q: RDF Query Processing Framework Using GPU

Cited by 13 publications

References 21 publications

VEDAS: an efficient GPU alternative for store and query of large RDF data sets

VEDAS: an efficient GPU alternative for store and query of large RDF data sets

VEDAS: An Efficient GPU Alternative for Store and Query of Large RDF Data Sets

CONNER: A Concurrent ILP Learner in Description Logic

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