Home   Publications     edited volumes   Awards   Research   Teaching   Miscellaneous   Full CV [pdf]   BLOG   bio
  
 
 
  
 
  
  Events
  
  
  
  
   
  
   Past Events
  
  
  
  
  
  
   
    | 
Publications of Torsten Hoefler  
Ingo Mueller, Andrea Arteaga, Torsten Hoefler, Gustavo Alonso:
 
  |  |   | Reproducible Floating-Point Aggregation in RDBMSs
   (Feb. 2018, In Proceedings of the 2018 IEEE 34th International Conference on Data Enineering ) 
 
 Abstract—Industry-grade database systems are expected to
    produce the same result if the same query is repeatedly run on the
    same input. However, the numerous sources of non-determinism
    in modern systems make reproducible results difficult to achieve.
    This is particularly true if floating-point numbers are involved,
    where the order of the operations affects the final result.
    As part of a larger effort to extend database engines with data
    representations more suitable for machine learning and scientific
    applications, in this paper we explore the problem of making
    relational GROUPBY over floating-point formats bit-reproducible,
    i.e., ensuring any execution of the operator produces the same
    result up to every single bit. To that aim, we first propose a
    numeric data type that can be used as drop-in replacement
    for other number formats and is—unlike standard floating-point
      formats—associative. We use this data type to make state-of-theart
      GROUPBY operators reproducible, but this approach incurs a
      slowdown between 4 × and 12 × compared to the same operator
      using conventional database number formats. We thus explore
      how to modify existing GROUPBY algorithms to make them bitreproducible
      and efficient. By using vectorized summation on
      batches and carefully balancing batch size, cache footprint, and
      preprocessing costs, we are able to reduce the slowdown due to
      reproducibility to a factor between 1.9 × and 2.4 × of aggregation
      in isolation and to a mere 2.7 % of end-to-end query performance
      even on aggregation-intensive queries in MonetDB. We thereby
      provide a solid basis for supporting more reproducible operations
directly in relational engines
 
 Documentsdownload article:  
  |  |   | BibTeX |  @inproceedings{,   author={Ingo Mueller and Andrea Arteaga and Torsten Hoefler and Gustavo Alonso},   title={{Reproducible Floating-Point Aggregation in RDBMSs}},   year={2018},   month={Feb.},   note={In Proceedings of the 2018 IEEE 34th International Conference on Data Enineering},   source={http://www.unixer.de/~htor/publications/}, } |  
  |  
  
 
 |