Fast <it>k</it>-selection algorithms for graphics processing units
By T. Alabi, Jeffrey D. Blanchard, Bradley Gordon, Russell Steinbach. 2012
65 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computer Networks and Communications, Computer Vision and Pattern Recognition, Optimization & Training
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
In Computer Networks and Communications
- André F. T. Martins
- Herv'e J'egou
- Kenneth Heafield
- Jeff Johnson
- Matthijs Douze
- Ramón Fernandez Astudillo
In Computer Vision and Pattern Recognition
- Herv'e J'egou
- Luc Van Gool
- Andrew Zisserman
- Cordelia Schmid
- Matthijs Douze
- Dacheng Tao
- Trevor Darrell
- Yi Yang
In Optimization & Training
- Herv'e J'egou
- Matthijs Douze
- André F. T. Martins
- Minyi Guo
- Kenneth Heafield
- Jeff Johnson
- Ramón Fernandez Astudillo
- Alham Fikri Aji
Citations in the corpus
- Billion-Scale Similarity Search with GPUs (2017, background): “k -selection algorithms, often for arbitrarily large (cid:96) and k , can be translated to a GPU, including radix selection and bucket selection [1], probabilistic selection [33], quick-select [14], and truncated sorts [40].”
- Sparse Communication for Distributed Gradient Descent (2017, background): “Selection to obtain the threshold is expensive (Alabi et al., 2012).”
- From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification (2016, methodology): “There is, however, recent work providing efficient implementations of these algorithms on GPUs (Alabi et al., 2012).”
- Efficient Selection Algorithm for Fast k-NN Search on GPUs (2015, methodology): “Compared with the other partition-based algorithms like Quick Select [9], Bucket or Radix Select [12], Hierarchical Partition can finish searching within fixed steps.”