Communication optimal parallel multiplication of sparse random matrices
By Grey Ballard, Aydin Buluç, J. Demmel, Laura Grigori, Benjamin Lipshitz, Oded Schwartz, Sivan Toledo. 2013
118 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, Hardware and Architecture, 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 Artificial Intelligence
- Ashish Vaswani
- Quoc V. Le
- Niki Parmar
- Noam Shazeer
- Jianfeng Gao
- Dustin Tran
- Irwan Bello
- Mohit Bansal
In Computer Networks and Communications
In Hardware and Architecture
- Leonid Oliker
- Aydin Buluç
- K. Yelick
- Noam Shazeer
- Ariful Azad
- Blake A. Hechtman
- Penporn Koanantakool
- HyoukJoong Lee
In Optimization & Training
- HyoukJoong Lee
- Ashish Vaswani
- Blake A. Hechtman
- K. Yelick
- Noam Shazeer
- Peter G. Hawkins
- Mingsheng Hong
- Aydin Buluç
Citations in the corpus
- Mesh-TensorFlow: Deep Learning for Supercomputers (2018, methodology): “For matrix multiplication, the most widely-used partitionings are grouped into three categories [11]: 1D, 2D, and 3D, based on the number of dimensions of the iteration space that are split.”
- Communication-Avoiding Parallel Sparse-Dense Matrix-Matrix Multiplication (2016, background): “Let M be the size in words of the fast memory each of our p processing elements has.”