# GPU-based Efficient Join Algorithms on Hadoop

## TL;DR

This paper presents a GPU-accelerated join algorithm integrated with Hadoop that improves efficiency and scalability for big data query processing, achieving significant speedups over traditional methods.

## Contribution

It introduces a novel GPU-based join algorithm combined with Hadoop's Map-Reduce and HDFS, including the first GPU acceleration of theta join for big data.

## Key findings

- Achieves 1.5 to 2 times speedup over traditional GPU join algorithms.
- Handles larger data tables effectively using Hadoop's data pre-filtering.
- GPU version outperforms CPU version by 1.3 to 2 times in synthetic datasets.

## Abstract

The growing data has brought tremendous pressure for query processing and storage, so there are many studies that focus on using GPU to accelerate join operation, which is one of the most important operations in modern database systems. However, existing GPU acceleration join operation researches are not very suitable for the join operation on big data. Based on this, this paper speeds up nested loop join, hash join and theta join, combining Hadoop with GPU, which is also the first to use GPU to accelerate theta join. At the same time, after the data pre-filtering and pre-processing, using Map-Reduce and HDFS in Hadoop proposed in this paper, the larger data table can be handled, compared to existing GPU acceleration methods. Also with Map-Reduce in Hadoop, the algorithm proposed in this paper can estimate the number of results more accurately and allocate the appropriate storage space without unnecessary costs, making it more efficient. The rigorous experiments show that the proposed method can obtain 1.5 to 2 times the speedup, compared to the traditional GPU acceleration equi join algorithm. And in the synthetic data set, the GPU version of the proposed method can get 1.3 to 2 times the speedup, compared to CPU version.