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@INPROCEEDINGS{Lattanzio:1047360,
      author       = {Lattanzio, Emily and Ranjan, Sansriti and Underwood, Robert
                      and Baumann, Thomas and Speck, Robert and Calhoun, Jon C.},
      title        = {{P}erformance {A}nalysis of {I}nline {C}ompression in
                      py{SDC}},
      publisher    = {IEEE},
      reportid     = {FZJ-2025-04254},
      pages        = {1-8},
      year         = {2025},
      comment      = {2025 IEEE High Performance Extreme Computing Conference
                      (HPEC) : [Proceedings] - IEEE, 2025. - ISBN
                      979-8-3315-7844-2 - doi:10.1109/HPEC67600.2025.11196695},
      booktitle     = {2025 IEEE High Performance Extreme
                       Computing Conference (HPEC) :
                       [Proceedings] - IEEE, 2025. - ISBN
                       979-8-3315-7844-2 -
                       doi:10.1109/HPEC67600.2025.11196695},
      abstract     = {The volume of data required for High Performance Computing
                      (HPC) jobs is growing faster than the memory storage
                      available to store the required data, leading to performance
                      bottlenecks. Hence the need for inline data compression,
                      which reduces the amount of allocated memory needed by
                      storing all data in its compressed format and
                      decompressing/recompressing single variables as needed. We
                      apply inline compression to the HPC application pySDC, a
                      framework for parallel-in-time integration of partial
                      differential equations. We introduce a new version of pySDC
                      that has a compression manager to add inline compression
                      functionality, along with a software cache that stores the
                      decompressed state of the most frequently used variables. We
                      use the ZFP lossy compressor to test our model with varying
                      software cache sizes. Results show that having no cache has
                      the best compression ratio (CR) at size 5.8, but having a
                      cache size of 16 reduces total execution time by 2.6× while
                      also slightly improving the memory footprint with a CR of
                      1.5. Our framework overall provides user versatility in the
                      trade-off between execution time and memory savings.},
      month         = {Sep},
      date          = {2025-09-15},
      organization  = {2025 IEEE High Performance Extreme
                       Computing Conference (HPEC), Wakefield
                       (MA), 15 Sep 2025 - 19 Sep 2025},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / RGRSE - RG Research
                      Software Engineering for HPC (RG RSE) (RG-RSE)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(DE-Juel-1)RG-RSE},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.1109/HPEC67600.2025.11196695},
      url          = {https://juser.fz-juelich.de/record/1047360},
}