Vectorized Highly Parallel Density-Based Clustering for Applications With Noise
Clustering 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 ex...
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10769413/ |
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| author | Joseph Arnold Xavier Juan Pedro Gutierrez Hermosillo Muriedas Stepan Nassyr Rocco Sedona Markus Gotz Achim Streit Morris Riedel Gabriele Cavallaro |
| author_facet | Joseph Arnold Xavier Juan Pedro Gutierrez Hermosillo Muriedas Stepan Nassyr Rocco Sedona Markus Gotz Achim Streit Morris Riedel Gabriele Cavallaro |
| author_sort | Joseph Arnold Xavier |
| collection | DOAJ |
| description | Clustering 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. |
| format | Article |
| id | doaj-art-89f509a67be546e0bbae4250b331d8ca |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-89f509a67be546e0bbae4250b331d8ca2025-08-20T02:38:35ZengIEEEIEEE Access2169-35362024-01-011218167918169210.1109/ACCESS.2024.350719310769413Vectorized Highly Parallel Density-Based Clustering for Applications With NoiseJoseph Arnold Xavier0https://orcid.org/0009-0007-5215-6022Juan Pedro Gutierrez Hermosillo Muriedas1https://orcid.org/0000-0001-8439-7145Stepan Nassyr2Rocco Sedona3https://orcid.org/0000-0003-4089-972XMarkus Gotz4https://orcid.org/0000-0002-2233-1041Achim Streit5https://orcid.org/0000-0002-5065-469XMorris Riedel6https://orcid.org/0000-0003-1810-9330Gabriele Cavallaro7https://orcid.org/0000-0002-3239-9904Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich, Jülich, GermanyScientific Computing Center (SCC), Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, GermanyJülich Supercomputing Centre (JSC), Forschungszentrum Jülich, Jülich, GermanyJülich Supercomputing Centre (JSC), Forschungszentrum Jülich, Jülich, GermanyScientific Computing Center (SCC), Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, GermanyScientific Computing Center (SCC), Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, GermanyJülich Supercomputing Centre (JSC), Forschungszentrum Jülich, Jülich, GermanyJülich Supercomputing Centre (JSC), Forschungszentrum Jülich, Jülich, GermanyClustering 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.https://ieeexplore.ieee.org/document/10769413/High-performance computingdensity-based clusteringvectorizationVHPDBSCAN |
| spellingShingle | Joseph Arnold Xavier Juan Pedro Gutierrez Hermosillo Muriedas Stepan Nassyr Rocco Sedona Markus Gotz Achim Streit Morris Riedel Gabriele Cavallaro Vectorized Highly Parallel Density-Based Clustering for Applications With Noise IEEE Access High-performance computing density-based clustering vectorization VHPDBSCAN |
| title | Vectorized Highly Parallel Density-Based Clustering for Applications With Noise |
| title_full | Vectorized Highly Parallel Density-Based Clustering for Applications With Noise |
| title_fullStr | Vectorized Highly Parallel Density-Based Clustering for Applications With Noise |
| title_full_unstemmed | Vectorized Highly Parallel Density-Based Clustering for Applications With Noise |
| title_short | Vectorized Highly Parallel Density-Based Clustering for Applications With Noise |
| title_sort | vectorized highly parallel density based clustering for applications with noise |
| topic | High-performance computing density-based clustering vectorization VHPDBSCAN |
| url | https://ieeexplore.ieee.org/document/10769413/ |
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