1 research outputs found
GPU-based Efficient Join Algorithms on Hadoop
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.Comment: 39 page