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Применение графических ускорителей для обработки запросов над сжатыми данными в параллельных системах баз данных

Применение графических ускорителей для обработки запросов над сжатыми данными в параллельных системах баз данных

Abstract

Работа посвящена вопросам применения графических процессоров для обработки запросов в параллельных системах баз данных. Целью данной работы является оценка эффективности выполнения запросов к сжатой базе данных без предварительной распаковки с использованием графических ускорителей, поддерживающих технологию CUDA. Объем внутренней памяти ГПУ на порядки меньше, чем объем оперативной памяти современных вычислительных систем. Это ограничивает размер базы данных, которую можно загрузить в память ГПУ и как следствие не позволяет раскрыть весь вычислительный потенциал графического процессора. Предлагается подход для обработки запросов над сжатыми данными на ГПУ. На основе предложенного подхода реализован эмулятор параллельной СУБД. Аналогичный эмулятор разработан для ЦПУ. Приведены результаты вычислительных экспериментов и произведена оценка эффективности данного подхода.

This article talks about using graphics processors for query processing in parallel database systems. The goal is to evaluate query execution efficiency over compressed database without decompression on multicore GPUs which support CUDA technology. GPU's memory size is significantly smaller than modern computer system's RAM size. This fact affects database's size can be loaded into GPU's internal memory, thus computing potential of GPU can not be used efficiently. The new approach presented in this article allows query processing over compressed data on GPU. An emulator of parallel DBMS is developed based on this approach. The similar emulator for a CPU is designed. Results of computational experiments are presented and analysis of efficiency of the proposed approaches is performed.

Keywords

ГРАФИЧЕСКИЕ ПРОЦЕССОРЫ, ПАРАЛЛЕЛЬНАЯ ОБРАБОТКА ЗАПРОСОВ

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold