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@MISC{Hoppe:1034769,
      author       = {Hoppe, Fabian and Osterfeld, Fynn and Gutiérrez Hermosillo
                      Muriedas, Juan Pedro and Vaithinathan Aravindan, Ashwath and
                      Comito, Claudia and Krajsek, Kai and Nguyen Xuan, Tu and
                      Tarnawa, Michael and Coquelin, Daniel and Debus, Charlotte
                      and Götz, Markus and Hagemeier, Björn and Knechtges,
                      Philipp and Rüttgers, Alexander},
      title        = {{H}eat (v1.5.0); 1.5.0},
      reportid     = {FZJ-2024-07524},
      year         = {2024},
      abstract     = {Heat 1.5 Release Notes Overview Highlights Performance
                      Improvements Sparse Signal Processing RNG Statistics
                      Manipulations I/O Machine Learning Deep Learning Other
                      Updates Contributors Overview With Heat 1.5 we release the
                      first set of features developed within the ESAPCA project
                      co-funded by the European Space Agency (ESA). The main focus
                      of this release is on distributed linear algebra operations,
                      such as tall-skinny SVD, batch matrix multiplication, and
                      triangular solver. We also introduce vectorization via vmap
                      across MPI processes, and batch-parallel random number
                      generation as default for distributed operations. This
                      release also includes a new class for distributed Compressed
                      Sparse Column matrices, paving the way for future
                      implementation of distributed sparse matrix multiplication.
                      On the performance side, our new array redistribution via
                      MPI Custom Datatypes provides significant speed-up in
                      operations that require it, such as FFTs. We are grateful to
                      our community of users, students, open-source contributors,
                      the European Space Agency and the Helmholtz Association for
                      their support and feedback. Highlights [ESAPCA] Distributed
                      tall-skinny SVD: ht.linalg.svd (by @mrfh92) Distributed
                      batch matrix multiplication: ht.linalg.matmul (by
                      @FOsterfeld) Distributed solver for triangular systems:
                      $ht.linalg.solve_triangular$ (by @FOsterfeld) Vectorization
                      across MPI processes: ht.vmap (by @mrfh92) Other Changes
                      Performance Improvements #1493 Redistribution speed-up via
                      MPI Custom Datatypes available by default in ht.resplit (by
                      @JuanPedroGHM) Sparse #1377 New class: Distributed
                      Compressed Sparse Column Matrix $ht.sparse.DCSC_matrix()$
                      (by @Mystic-Slice) Signal Processing #1515 Support batch 1-d
                      convolution in ht.signal.convolve (by @ClaudiaComito) RNG
                      #1508 Introduce batch-parallel RNG as default for
                      distributed operations (by @mrfh92) Statistics #1420 Support
                      sketched percentile/median for large datasets with
                      ht.percentile(sketched=True) (and ht.median) (by @mrhf92)
                      #1510 Support multiple axes for distributed ht.percentile
                      and ht.median (by @ClaudiaComito) Manipulations #1419
                      Implement distributed unfold operation (by @FOsterfeld) I/O
                      #1602 Improve load balancing when loading .npy files from
                      path (by @Reisii) #1551 Improve load balancing when loading
                      .csv files from path (by @Reisii) Machine Learning #1593
                      Improved batch-parallel clustering
                      ht.cluster.BatchParallelKMeans and
                      ht.cluster.BatchParallelKMedians (by @mrfh92) Deep Learning
                      #1529 Make dataset.ishuffle optional. (by @krajsek) Other
                      Updates #1618 Support mpi4py 4.x.x (by @JuanPedroGHM)
                      Contributors @mrfh92, @FOsterfeld, @JuanPedroGHM,
                      @Mystic-Slice, @ClaudiaComito, @Reisii, @mtar and @krajsek},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / 5111 - Domain-Specific
                      Simulation $\&$ Data Life Cycle Labs (SDLs) and Research
                      Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)33},
      doi          = {10.5281/ZENODO.14001852},
      url          = {https://juser.fz-juelich.de/record/1034769},
}