Application of CUDA Kernel in the Processing of Information in the Database Using BLAZINGSQL

Purpose of research a way to increase the efficiency of data processing processes using hardware video accelerators that have CUDA cores in their composition.

Methods. The method of software interaction with the hardware computing means of a video accelerator with CUDA cores using the BlazingDB solution based on RAPIDS AI was used. PostgresSQL database and BlazingSQL technology are used to study the performance and speed up data processing processes.

Results. The use of BlazingDB speeds up the data processing process several times relative to PostgresSQL. The performance of BlazingDB technology, which uses a video accelerator instead of a central processor, surpassed PostgresSQL by 10 and 35 times for two different massive data sampling and processing tasks.

Conclusion. The possibilities of hardware video accelerators when using them for information processing tasks in a database are investigated; the performance of the BlazingDB solution for interacting with CUDA cores and standard PostgresSQL solutions is compared; the considered solution demonstrates a multiple increase in data processing performance using a large number of CUDA video accelerator cores relative to the CPU, which has a significantly smaller number of cores, but a large the frequency of work.

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