@inproceedings{VinconBernhardtPetrovetal.2020,
  author    = {Vin{\c{c}}on, Tobias and Bernhardt, Arthur and Petrov, Ilia and Weber, Lukas and Koch, Andreas},
  title     = {nKV: near-data processing with KV-stores on native computational storage},
  series = {DaMoN '20 : proceedings of the 16th International Workshop on Data Management on New Hardware : Portland, Oregon, June, 2020},
  booktitle = {DaMoN '20 : proceedings of the 16th International Workshop on Data Management on New Hardware : Portland, Oregon, June, 2020},
  publisher = {Assoc. of Computing Machinery},
  address   = {New York, NY},
  isbn      = {978-1-4503-8024-9},
  doi       = {10.1145/3399666.3399934},
  pages     = {11},
  year      = {2020},
  abstract  = {Massive data transfers in modern key/value stores resulting from low data-locality and data-to-code system design hurt their performance and scalability. Near-data processing (NDP) designs represent a feasible solution, which although not new, have yet to see widespread use. In this paper we introduce nKV, which is a key/value store utilizing native computational storage and near-data processing. On the one hand, nKV can directly control the data and computation placement on the underlying storage hardware. On the other hand, nKV propagates the data formats and layouts to the storage device where, software and hardware parsers and accessors are implemented. Both allow NDP operations to execute in host-intervention-free manner, directly on physical addresses and thus better utilize the underlying hardware. Our performance evaluation is based on executing traditional KV operations (GET, SCAN) and on complex graph-processing algorithms (Betweenness Centrality) in-situ, with 1.4×-2.7× better performance on real hardware - the COSMOS+ platform.},
  language  = {en}
}