Communication-Avoiding Parallel Sparse-Dense Matrix-Matrix Multiplication

By Penporn Koanantakool, Ariful Azad, Aydin Buluç, Dmitriy Morozov, Sang-Yun Oh, Leonid Oliker, K. Yelick. 2016

63 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Networks and Communications, Hardware and Architecture

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. K. Yelick
2. Jimeng Sun
3. R. Vuduc
4. Dmitriy Morozov
5. Aydin Buluç
6. Alnur Ali

In Computer Networks and Communications

1. R. Vuduc
2. Leonid Oliker
3. Aydin Buluç
4. K. Yelick
5. Jimeng Sun
6. Alnur Ali
7. Ariful Azad
8. Penporn Koanantakool

In Hardware and Architecture

1. R. Vuduc
2. Leonid Oliker
3. Aydin Buluç
4. K. Yelick
5. Jimeng Sun
6. Ariful Azad
7. Penporn Koanantakool
8. Jiajia Li

Citations in the corpus

• Integrated Model, Batch, and Domain Parallelism in Training Neural Networks (2017, unknown): “5D algorithms for matrix multiplication [14].”
• Communication-Avoiding Optimization Methods for Distributed Massive-Scale Sparse Inverse Covariance Estimation (2017, background): “5D algorithm [24] to support different replication factors for each matrix operand.”
• HiCOO: Hierarchical Storage of Sparse Tensors (2018, background): “There are many options for sparse matrices that tradeoff size, speed, and “fit” to the nonzero structure of a given input matrix and requirements of the application [1–17].”
• Integrated Model and Data Parallelism in Training Neural Networks (2017, unknown): “5D algorithms for matrix multiplication [11].”