GPU-Accelerated Method of Query Selectivity Estimation for Non Equi-Join Conditions Based on Discrete Fourier Transform

Warchał, Łukasz

doi:10.1007/978-3-319-10518-5_17

Cited by 5 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other com- munities already use co-processors as accelerators for their optimization problems successfully [12,25,32,34,37]. This is a strong indicator that DBMSs can also benefit from parallel optimization on co-processors, as it was already shown for the selectivity estimation problem [2][3][4]21].…”

Section: Introductionmentioning

confidence: 91%

Toward GPU-accelerated Database Optimization

2015

View full text Add to dashboard Cite

For over three decades, research investigates optimization options in DBMSs. Nowadays, the hardware used in DBMSs become more and more heterogeneous, because processors are bound by a fixed energy budget leading to increased parallelism. Existing optimization approaches in DBMSs do not exploit parallelism for a single optimization task and, hence, can only benefit from the parallelism offered by current hardware by batch-processing multiple optimization tasks.Since a large optimization space often allows us to process sub-spaces in parallel, we expect large gains in result quality for optimization approaches in DBMSs and, hence, performance for query processing on modern (co-)processors. However, parallel optimization on CPUs is likely to slow down query processing, because DBMSs can fully exploit the CPUs computing resources due to high data parallelism. In contrast, the communication overhead of co-processors such as GPUs typically lead to plenty of compute resources unused.In this paper, we motivate the use of parallel co-processors for optimization in DBMSs, identify optimization problems benefiting from parallelization, and show how we can design parallel optimization approaches on the example of the operator placement problem.A. Meister ( ) · G. Saake

show abstract

Section: Introductionmentioning

confidence: 91%

Toward GPU-accelerated Database Optimization

2015

View full text Add to dashboard Cite

show abstract

“…For GPU, we use an NVIDIA GTX1080@1733 MHz, 2560 Cuda Cores, 2MB shared L2 Cache, 8GB Graphic Memory, and 180Watt TDP. The SELECTION algorithm is introduced from [11], the SORT algorithm from [14], Equijoin from [15], and inequality join from [6]. For FPGA, we use the architecture and algorithms from [16] for SELECTION, SORT, and Equi-Join queries.…”

Section: E Compared With Other Platformsmentioning

confidence: 99%

ReSQM: Accelerating Database Operations Using ReRAM-Based Content Addressable Memory

Li¹,

Jin²,

Zheng³

2020

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

View full text Add to dashboard Cite

The huge amount of data enforces great pressure on the processing efficiency of database systems. By leveraging the in-situ computing ability of emerging non-volatile memory, Processing-In-Memory (PIM) technology shows great potential in accelerating database operations against traditional architectures without data movement overheads. In this paper, we introduce ReSQM, a novel ReCAM-based accelerator, which can dramatically reduce the response time of database systems. The key novelty of ReSQM is that some commonly used database queries that would be otherwise processed inefficiently in previous studies can be in-situ accomplished with massively-high parallelism by exploiting the PIM-enabled ReCAM array. ReSQM supports some typical database queries (such as SELECTION, SORT, and JOIN) effectively based on the limited computational mode of the ReCAM array. ReSQM is also equipped with a series of hardware-algorithm co-designs to maximize efficiency. We present a new data mapping mechanism that allows enjoying insitu in-memory computations for SELECTION operating upon intermediate results. We also develop a count-based ReCAMspecific algorithm to enable the in-memory sorting without any row swapping. The relational comparisons are integrated for accelerating inequality join by making a few modifications to the ReCAM cells with negligible hardware overhead. Experimental results show that ReSQM can improve the (energy-)efficiency by 611× (193×), 19× (17×), 59× (43×), and 307× (181×) in comparison to a 10-core Intel Xeon E5-2630v4 processor for SELECTION, SORT, equi-join, and inequality join, respectively. In contrast to state-of-the-art CMOS-based CAM, GPU, FPGA, NDP, and PIM solutions, ReSQM can also offer 2.2× ∼ 39× speedups.

show abstract

“…Koumarelas [29], Okcan [30] and Penar [31] achieved theta join operations of two tables on Map-Reduce, meanwhile Yan [32], Zhang [33] and Changchun [34,35] achieved a multi-table theta join. Augustyn [36] used the GPU to estimate the join selectivity for theta join of two tables.…”

Section: Gpu-based Theta Join With Hadoopmentioning

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

GPU-Accelerated Method of Query Selectivity Estimation for Non Equi-Join Conditions Based on Discrete Fourier Transform

Cited by 5 publications

References 9 publications

Toward GPU-accelerated Database Optimization

Toward GPU-accelerated Database Optimization

ReSQM: Accelerating Database Operations Using ReRAM-Based Content Addressable Memory

GPU-based efficient join algorithms on Hadoop

Contact Info

Product

Resources

About