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2020•Unpublished venueRequires access

GPU in applications with non-relational DBs

Stefan Jovanov, Vladimir Zdraveski, Marjan Gušev

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Abstract

Rapid speeds have been acquired throughout the past decade using GPUs and NoSQL-databases. This paper presents a concept of a GPU-extended non-relational database management system. The research focuses on implementing kernels and performing the basic aggregation functions over a JSON file. Different comparisons with the Numpy library, a CPU-counterpart and MongoDb show the importance of the concept. The hypothesis is to check if GPU can speed up NoSQL-database queries which we prove using our methods.

About this research paper

What this paper is about

Rapid speeds have been acquired throughout the past decade using GPUs and NoSQL-databases. This paper presents a concept of a GPU-extended non-relational database management system. The research focuses on implementing kernels and performing the basic aggregation functions over a JSON file. Different comparisons with the Numpy library, a CPU-counterpart and MongoDb show the importance of the concept. The hypothesis is to check if GPU can speed up NoSQL-database queries which we prove using our methods.

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Available abstract

Rapid speeds have been acquired throughout the past decade using GPUs and NoSQL-databases. This paper presents a concept of a GPU-extended non-relational database management system. The research focuses on implementing kernels and performing the basic aggregation functions over a JSON file. Different comparisons with the Numpy library, a CPU-counterpart and MongoDb show the importance of the concept. The hypothesis is to check if GPU can speed up NoSQL-database queries which we prove using our methods.

Key concepts: NoSQL, JSON, Computer science, Relational database, Relational database management system, Database, SQL, Relational model

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