Manycore GPU processing of repeated range queries over streams of moving objects observations

Lettich, Francesco; Orlando, Salvatore; Silvestri, Claudio; Jensen, Christian S.

doi:10.1002/cpe.3881

Cited by 2 publications

(2 citation statements)

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“…There are some studies that exploit the parallelism of GPU (Graphic Processing Unit) [9]- [11]. [11] proposed the repeated processing method for huge amounts of k nearest neighbours (k-NN) queries over massive sets of moving objects, where the spatial range of queries and the position of objects are continuously modified over time.…”

Section: Parallel Index Structures For Moving Objects On Single Machinementioning

confidence: 99%

“…[11] proposed the repeated processing method for huge amounts of k nearest neighbours (k-NN) queries over massive sets of moving objects, where the spatial range of queries and the position of objects are continuously modified over time. This method uses a hybrid CPU/GPU pipeline that significantly enhance k-NN query processing.…”

Section: Parallel Index Structures For Moving Objects On Single Machinementioning

confidence: 99%

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Distributed Indexing Methods for Moving Objects based on Spark Stream

Lee¹,

Song²

2015

International Journal of Contents

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Section: Parallel Index Structures For Moving Objects On Single Machinementioning

confidence: 99%

Section: Parallel Index Structures For Moving Objects On Single Machinementioning

confidence: 99%

Distributed Indexing Methods for Moving Objects based on Spark Stream

Lee¹,

Song²

2015

International Journal of Contents

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GPU-Accelerated Similarity Self-Join for Multi-Dimensional Data

Gowanlock

Karsin

2019

Proceedings of the 15th International Workshop on Data Management on New Hardware

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e self-join nds all objects in a dataset that are within a search distance, ϵ , of each other; therefore, the self-join is a building block of many algorithms. We advance a GPU-accelerated self-join algorithm targeted towards high dimensional data. e massive parallelism a orded by the GPU and high aggregate memory bandwidth makes the architecture well-suited for data-intensive workloads. We leverage a grid-based, GPU-tailored index to perform range queries. We propose the following optimizations: (i) a trade-o between candidate set ltering and index search overhead by exploiting properties of the index; (ii) reordering the data based on variance in each dimension to improve the ltering power of the index; and (iii) a pruning method for reducing the number of expensive distance calculations. Across most scenarios on real-world and synthetic datasets, our algorithm outperforms the parallel state-of-the-art approach. Exascale systems are converging on heterogeneous distributed-memory architectures. We show that an entity partitioning method can be utilized to achieve a balanced workload, and thus good scalability for multi-GPU or distributed-memory self-joins.us, many large-scale data analytics applications will rely on GPU-e cient algorithms, including the distance similarity self-join for high dimensional data -the subject of this work. is paper makes the following novel contributions:• Leveraging an e cient indexing scheme for the GPU, we exploit the trade-o between index ltering power and search cost to improve the overall performance of searching high dimensional feature spaces.• We improve the ltering power of the index by reordering the data in each dimension using statistical properties of the data distribution. We show that this is particularly important when exploiting the trade-o outlined above.• We mitigate the performance cost of reducing index ltering power by proposing a technique that prunes the candidate set by comparing points based on an un-indexed dimension.• We show that on the worst-case data distribution for our approach, we achieve signi cantly be er performance than the state-of-the-art on the same scenario. is suggests that the performance of the GPU-accelerated self-join is resilient to the data distribution, making the approach well-suited for many application scenarios.• We evaluate our approach on 5 real-world and 3 synthetic datasets and show that our GPU accelerated self-join outperforms the state-of-the-art parallel algorithm in the literature.• e self-join is an expensive operation. We show initial insights into the scalability of the self-join on multi-GPU and distributed-memory systems, and demonstrate that an entity partitioning strategy can be used to achieve good load balancing. e paper is outlined as follows: Section 2 provides background material, Section 3 formalizes the problem and discusses previous work that we employ, Section 4 presents the novel methods we use to improve high dimensional self-join performance, Section 5 illustrates our performance results, Section 6 dis...

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Manycore GPU processing of repeated range queries over streams of moving objects observations

Cited by 2 publications

References 37 publications

Distributed Indexing Methods for Moving Objects based on Spark Stream

Distributed Indexing Methods for Moving Objects based on Spark Stream

GPU-Accelerated Similarity Self-Join for Multi-Dimensional Data

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