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001034038 1001_ $$00009-0007-5215-6022$$aXavier, Joseph Arnold$$b0$$eCorresponding author
001034038 245__ $$aVectorized Highly Parallel Density-Based Clustering for Applications With Noise
001034038 260__ $$aNew York, NY$$bIEEE$$c2024
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001034038 520__ $$aClustering in data mining involves grouping similar objects into categories based on their characteristics. As the volume of data continues to grow and advancements in high-performance computing evolve, a critical need has emerged for algorithms that can efficiently process these computations and exploit the various levels of parallelism offered by modern supercomputing systems. Exploiting Single Instruction Multiple Data (SIMD) instructions enhances parallelism at the instruction level and minimizes data movement within the memory hierarchy. To fully harness a processor’s SIMD capabilities and achieve optimal performance, adapting algorithms for better compatibility with vector operations is necessary. In this paper, we introduce a vectorized implementation of the Density-based Clustering for Applications with Noise (DBSCAN) algorithm suitable for the execution on both shared and distributed memory systems. By leveraging SIMD, we enhance the performance of distance computations. Our proposed Vectorized HPDBSCAN (VHPDBSCAN) demonstrates a performance improvement of up to two times over the state-of-the-art parallel version, Highly Parallel DBSCAN (HPDBSCAN), on the ARM-based A64FX processor on two different datasets with varying dimensions. We have parallelized computations which are essential for the efficient workload distribution. This has significantly enhanced the performance on higher dimensional datasets. Additionally, we evaluate VHPDBSCAN’s energy consumption on the A64FX and Intel Xeon processors. The results show that in both processors, due to the reduced runtime, the total energy consumption of the application is reduced by 50% on the A64FX Central Processing Unit (CPU) and by approximately 19% on the Intel Xeon 8368 CPU compared to HPDBSCAN.
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001034038 7001_ $$00000-0001-8439-7145$$aPedro Gutiérrez Hermosillo Muriedas, Juan$$b1
001034038 7001_ $$0P:(DE-Juel1)172888$$aNassyr, Stepan$$b2$$ufzj
001034038 7001_ $$0P:(DE-Juel1)178695$$aSedona, Rocco$$b3
001034038 7001_ $$0P:(DE-Juel1)162390$$aGötz, Markus$$b4
001034038 7001_ $$00000-0002-5065-469X$$aStreit, Achim$$b5
001034038 7001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b6
001034038 7001_ $$0P:(DE-Juel1)171343$$aCavallaro, Gabriele$$b7
001034038 773__ $$0PERI:(DE-600)2687964-5$$a10.1109/ACCESS.2024.3507193$$gVol. 12, p. 181679 - 181692$$p181679 - 181692$$tIEEE access$$v12$$x2169-3536$$y2024
001034038 8564_ $$uhttps://juser.fz-juelich.de/record/1034038/files/Vectorized_Highly_Parallel_Density-Based_Clustering_for_Applications_With_Noise.pdf$$yOpenAccess
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