Efficient parallel lists intersection and index compression algorithms using graphics processing units

Ao, Naiyong; Zhang, Fan; Wu, Di; Stones, Douglas S.; Wang, Gang; Liu, Xiaoguang; Liu, Jing; Lin, Sheng‐Xiang

doi:10.14778/2002974.2002975

Cited by 74 publications

(42 citation statements)

References 30 publications

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“…Spatial Query Processing on GPUs. Over the past decade, several research efforts have leveraged programmable GPUs to boost the performance of general, data-intensive operations [5,18,22,26,32]. Earlier techniques (e.g., [22]) employed the programmable rendering pipeline to execute these queries.…”

Section: Related Workmentioning

confidence: 99%

GPU rasterization for real-time spatial aggregation over arbitrary polygons

et al. 2017

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Visual exploration of spatial data relies heavily on spatial aggregation queries that slice and summarize the data over different regions. These queries comprise computationally-intensive point-inpolygon tests that associate data points to polygonal regions, challenging the responsiveness of visualization tools. This challenge is compounded by the sheer amounts of data, requiring a large number of such tests to be performed. Traditional pre-aggregation approaches are unsuitable in this setting since they fix the query constraints and support only rectangular regions. On the other hand, query constraints are defined interactively in visual analytics systems, and polygons can be of arbitrary shapes. In this paper, we convert a spatial aggregation query into a set of drawing operations on a canvas and leverage the rendering pipeline of the graphics hardware (GPU) to enable interactive response times. Our technique trades-off accuracy for response time by adjusting the canvas resolution, and can even provide accurate results when combined with a polygon index. We evaluate our technique on two large real-world data sets, exhibiting superior performance compared to index-based approaches.

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Section: Related Workmentioning

confidence: 99%

GPU rasterization for real-time spatial aggregation over arbitrary polygons

et al. 2017

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“…Some set intersection algorithms exploit other characteristics of the modern hardware architectures beyond their branch performance, such as the large amount of cache memory and multiple cores [16][17][18]. For example, Tsirogiannis et al [16] improve the performance of a search-based intersection algorithm by using a small index called a micro-index, which can fit into the processor's cache memory.…”

Section: Related Workmentioning

confidence: 99%

Faster set intersection with SIMD instructions by reducing branch mispredictions

2014

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Set intersection is one of the most important operations for many applications such as Web search engines or database management systems. This paper describes our new algorithm to efficiently find set intersections with sorted arrays on modern processors with SIMD instructions and high branch misprediction penalties. Our algorithm efficiently exploits SIMD instructions and can drastically reduce branch mispredictions. Our algorithm extends a merge-based algorithm by reading multiple elements, instead of just one element, from each of two input arrays and compares all of the pairs of elements from the two arrays to find the elements with the same values. The key insight for our improvement is that we can reduce the number of costly hard-to-predict conditional branches by advancing a pointer by more than one element at a time. Although this algorithm increases the total number of comparisons, we can execute these comparisons more efficiently using the SIMD instructions and gain the benefits of the reduced branch misprediction overhead. Our algorithm is suitable to replace existing standard library functions, such as std::set_intersection in C++, thus accelerating many applications, because the algorithm is simple and requires no preprocessing to generate additional data structures. We implemented our algorithm on Xeon and POWER7+. The experimental results show our algorithm outperforms the std::set_intersection implementation delivered with gcc by up to 5.2x using SIMD instructions and by up to 2.1x even without using SIMD instructions for 32-bit and 64-bit integer datasets. Our SIMD algorithm also outperformed an existing algorithm that can leverage SIMD instructions.

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“…There are already a set of research papers on optimizing various database operations on GPUs [12,18,20,28,17,29,16,30,21,19,23,32,10,33,25]. Our unique contribution in this paper is presenting a comprehensive study for complex data warehousing queries with different software optimizations and hardware configurations.…”

Section: Other Related Workmentioning

confidence: 99%

“…Because of GPU's high computational power, how to accelerate various workloads on GPUs has been a major research topic in both the high performance computing area and the database area [12,18,20,28,17,29,16,30,21,19,23,10,33,25,32]. In high performance computing area, GPUs as accelerators have already been widely deployed to process performance-critical tasks.…”

Section: Introductionmentioning

confidence: 99%

The Yin and Yang of processing data warehousing queries on GPU devices

2013

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Database community has made significant research efforts to optimize query processing on GPUs in the past few years. However, we can hardly find that GPUs have been truly adopted in major warehousing production systems. Preparing to merge GPUs to the warehousing systems, we have identified and addressed several critical issues in a threedimensional study of warehousing queries on GPUs by varying query characteristics, software techniques, and GPU hardware configurations. We also propose an analytical model to understand and predict the query performance on GPUs. Based on our study, we present our performance insights for warehousing query execution on GPUs. The objective of our work is to provide a comprehensive guidance for GPU architects, software system designers, and database practitioners to narrow the speed gap between the GPU kernel execution (the fast mode) and data transfer to prepare GPU execution (the slow mode) for high performance in processing data warehousing queries. The GPU query engine developed in this work is open source to the public.

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Efficient parallel lists intersection and index compression algorithms using graphics processing units

Cited by 74 publications

References 30 publications

GPU rasterization for real-time spatial aggregation over arbitrary polygons

GPU rasterization for real-time spatial aggregation over arbitrary polygons

Faster set intersection with SIMD instructions by reducing branch mispredictions

The Yin and Yang of processing data warehousing queries on GPU devices

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