GPU-based Parallel R-tree Construction and Querying

Prasad, Sushil K.; McDermott, Michael; He, Xi; Puri, Satish

doi:10.1109/ipdpsw.2015.127

Cited by 20 publications

(2 citation statements)

References 33 publications

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“…Wang and others [6,16,45] proposed an approximate GPU-based join approach. There have also been GPU-based approaches for heatmap queries [47], creating and querying R-trees [34], and improving the filtering phase of spatial queries [1,39]. All the above approaches use CUDA (except [39] and [41]), and are hence restricted to work only on Nvidia GPUs.…”

Section: Related Workmentioning

confidence: 99%

GPU-Powered Spatial Database Engine for Commodity Hardware: Extended Version

Doraiswamy¹,

Freire²

2022

Preprint

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Given the massive growth in the volume of spatial data, there is a great need for systems that can efficiently evaluate spatial queries over large data sets. These queries are notoriously expensive using traditional database solutions. While faster response times can be attained through powerful clusters or servers with large main-memory, these options, due to cost and complexity, are out of reach to many data scientists and analysts making up the long tail.Graphics Processing Units (GPUs), which are now widely available even in commodity desktops and laptops, provide a cost-effective alternative to support high-performance computing, opening up new opportunities to the efficient evaluation of spatial queries. While GPU-based approaches proposed in the literature have shown great improvements in performance, they are tied to specific GPU hardware and only handle specific queries over fixed geometry types.In this paper we present SPADE, a GPU-powered spatial database engine that supports a rich set of spatial queries. We discuss the challenges involved in attaining efficient query evaluation over large datasets as well as portability across different GPU hardware, and how these are addressed in SPADE. We performed a detailed experimental evaluation to assess the effectiveness of the system for wide range of queries and datasets, and report results which show that SPADE is scalable and able to handle data larger than main-memory, and its performance on a laptop is on par with that other systems that require clusters or large-memory servers.

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Section: Related Workmentioning

confidence: 99%

GPU-Powered Spatial Database Engine for Commodity Hardware: Extended Version

Doraiswamy¹,

Freire²

2022

Preprint

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“…Graphics processing units (GPUs) have been increasingly used for general computational problems and particularly for improving similarity join performance [4,5], and with specific data indexing methods that are suited to the GPU's particular single instruction multiple threads (SIMT) architecture [10][11][12][13][14]. The proliferation of GPUs is particularly explained by their increased computational throughput and higher memory bandwidth compared to CPUs.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous CPU-GPU Epsilon Grid Joins: Static and Dynamic Work Partitioning Strategies

Gallet

Gowanlock

2020

Data Sci. Eng.

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Given two datasets (or tables) A and B and a search distance $$\epsilon$$ ϵ , the distance similarity join, denoted as $$A \ltimes _\epsilon B$$ A ⋉ ϵ B , finds the pairs of points ($$p_a$$ p a , $$p_b$$ p b ), where $$p_a \in A$$ p a ∈ A and $$p_b \in B$$ p b ∈ B , and such that the distance between $$p_a$$ p a and $$p_b$$ p b is $$\le \epsilon$$ ≤ ϵ . If $$A = B$$ A = B , then the similarity join is equivalent to a similarity self-join, denoted as $$A \bowtie _\epsilon A$$ A ⋈ ϵ A . We propose in this paper Heterogeneous Epsilon Grid Joins (HEGJoin), a heterogeneous CPU-GPU distance similarity join algorithm. Efficiently partitioning the work between the CPU and the GPU is a challenge. Indeed, the work partitioning strategy needs to consider the different characteristics and computational throughput of the processors (CPU and GPU), as well as the data-dependent nature of the similarity join that accounts in the overall execution time (e.g., the number of queries, their distribution, the dimensionality, etc.). In addition to HEGJoin, we design in this paper a dynamic and two static work partitioning strategies. We also propose a performance model for each static partitioning strategy to perform the distribution of the work between the processors. We evaluate the performance of all three partitioning methods by considering the execution time and the load imbalance between the CPU and GPU as performance metrics. HEGJoin achieves a speedup of up to $$5.46\times$$ 5.46 × ($$3.97\times$$ 3.97 × ) over the GPU-only (CPU-only) algorithms on our first test platform and up to $$1.97\times$$ 1.97 × ($$12.07\times$$ 12.07 × ) on our second test platform over the GPU-only (CPU-only) algorithms.

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HEGJoin: Heterogeneous CPU-GPU Epsilon Grids for Accelerated Distance Similarity Join

Gallet

Gowanlock

2020

Database Systems for Advanced Applications

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GPU-based Parallel R-tree Construction and Querying

Cited by 20 publications

References 33 publications

GPU-Powered Spatial Database Engine for Commodity Hardware: Extended Version

GPU-Powered Spatial Database Engine for Commodity Hardware: Extended Version

Heterogeneous CPU-GPU Epsilon Grid Joins: Static and Dynamic Work Partitioning Strategies

HEGJoin: Heterogeneous CPU-GPU Epsilon Grids for Accelerated Distance Similarity Join

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