---
res:
  bibo_abstract:
  - Applying machine learning techniques to the quickly growing data in science and
    industry requires highly-scalable algorithms. Large datasets are most commonly
    processed "data parallel" distributed across many nodes. Each node's contribution
    to the overall gradient is summed using a global allreduce. This allreduce is
    the single communication and thus scalability bottleneck for most machine learning
    workloads. We observe that frequently, many gradient values are (close to) zero,
    leading to sparse of sparsifyable communications. To exploit this insight, we
    analyze, design, and implement a set of communication-efficient protocols for
    sparse input data, in conjunction with efficient machine learning algorithms which
    can leverage these primitives. Our communication protocols generalize standard
    collective operations, by allowing processes to contribute arbitrary sparse input
    data vectors. Our generic communication library, SparCML1, extends MPI to support
    additional features, such as non-blocking (asynchronous) operations and low-precision
    data representations. As such, SparCML and its techniques will form the basis
    of future highly-scalable machine learning frameworks.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Cedric
      foaf_name: Renggli, Cedric
      foaf_surname: Renggli
  - foaf_Person:
      foaf_givenName: Saleh
      foaf_name: Ashkboos, Saleh
      foaf_surname: Ashkboos
      foaf_workInfoHomepage: http://www.librecat.org/personId=0D0A9058-257B-11EA-A937-9341C3D8BC8A
  - foaf_Person:
      foaf_givenName: Mehdi
      foaf_name: Aghagolzadeh, Mehdi
      foaf_surname: Aghagolzadeh
  - foaf_Person:
      foaf_givenName: Dan-Adrian
      foaf_name: Alistarh, Dan-Adrian
      foaf_surname: Alistarh
      foaf_workInfoHomepage: http://www.librecat.org/personId=4A899BFC-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0003-3650-940X
  - foaf_Person:
      foaf_givenName: Torsten
      foaf_name: Hoefler, Torsten
      foaf_surname: Hoefler
  bibo_doi: 10.1145/3295500.3356222
  dct_date: 2019^xs_gYear
  dct_identifier:
  - UT:000545976800011
  dct_isPartOf:
  - http://id.crossref.org/issn/2167-4329
  - http://id.crossref.org/issn/2167-4337
  - http://id.crossref.org/issn/9781450362290
  dct_language: eng
  dct_publisher: ACM@
  dct_title: 'SparCML: High-performance sparse communication for machine learning@'
...