Active storage networks: Using embedded computation in the network switch for cluster data processing

Singaraju, Janardhan; Thamarakuzhi, Ajithkumar; Chandy, John A.

doi:10.1016/j.future.2014.10.020

Cited by 3 publications

(1 citation statement)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of new hardware to speed up the process is also possible [7]. In recent years, many studies have chosen FPGA as an option for hardware accelerators [8,9], including the query [10]. Similarly, image processors (GPU) have been widely used in the field of query processing [11].…”

Section: Introductionmentioning

confidence: 99%

GPU-based efficient join algorithms on Hadoop

Wang

et al. 2020

J Supercomput

View full text Add to dashboard Cite

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.

show abstract