Omnia vincit amor
Home -> Publications
Home
  Publications
    
edited volumes
  Awards
  Research
  Teaching
  Miscellaneous
  Full CV [pdf]
  BLOG






  Events








  Past Events





Publications of Torsten Hoefler
Cedric Renggli, Dan Alistarh, Mehdi Aghagolzadeh, Torsten Hoefler:

 SparCML: High-Performance Sparse Communication for Machine Learning

(In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC19), Nov. 2019)

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, SparCML, 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.

Documents

download article:
 

BibTeX

@inproceedings{,
  author={Cedric Renggli and Dan Alistarh and Mehdi Aghagolzadeh and Torsten Hoefler},
  title={{SparCML: High-Performance Sparse Communication for Machine Learning}},
  year={2019},
  month={Nov.},
  booktitle={Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC19)},
  source={http://www.unixer.de/~htor/publications/},
}


serving: 44.220.184.63:54022© Torsten Hoefler