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@ARTICLE{Xavier:1034038,
      author       = {Xavier, Joseph Arnold and Pedro Gutiérrez Hermosillo
                      Muriedas, Juan and Nassyr, Stepan and Sedona, Rocco and
                      Götz, Markus and Streit, Achim and Riedel, Morris and
                      Cavallaro, Gabriele},
      title        = {{V}ectorized {H}ighly {P}arallel {D}ensity-{B}ased
                      {C}lustering for {A}pplications {W}ith {N}oise},
      journal      = {IEEE access},
      volume       = {12},
      issn         = {2169-3536},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2024-06868},
      pages        = {181679 - 181692},
      year         = {2024},
      abstract     = {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.},
      cin          = {JSC},
      ddc          = {621.3},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / 5122 - Future
                      Computing $\&$ Big Data Systems (POF4-512) / 5112 -
                      Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and
                      Research Groups (POF4-511) / EUPEX - EUROPEAN PILOT FOR
                      EXASCALE (101033975) / EUROCC-2 (DEA02266)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-HGF)POF4-5122 /
                      G:(DE-HGF)POF4-5112 / G:(EU-Grant)101033975 /
                      G:(DE-Juel-1)DEA02266},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001375797900029},
      doi          = {10.1109/ACCESS.2024.3507193},
      url          = {https://juser.fz-juelich.de/record/1034038},
}