GPU-accelerated preconditioned iterative linear solvers

By Ruipeng Li, Y. Saad. Journal of Supercomputing, 2013

336 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Computational Theory and Mathematics, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/dbf7073cf4bab841c2b769e63768655bd285c033

How researchers see this paper

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

In Atomic and Molecular Physics, and Optics

1. Brian Curless
2. Sameer Agarwal
3. S. M. Seitz
4. Changchang Wu

In Computational Theory and Mathematics

1. Brian Curless
2. Sameer Agarwal
3. S. M. Seitz
4. Changchang Wu

In Hardware and Architecture

1. Brian Curless
2. Sameer Agarwal
3. S. M. Seitz
4. Brian Vinter
5. Changchang Wu
6. Weifeng Liu

Citations in the corpus

• CSR5: An Efficient Storage Format for Cross-Platform Sparse Matrix-Vector Multiplication (2015, background): “But to accelerate large-scale computation, parallel SpMV is still required to be hand-optimized with specific data storage formats and algorithms [1, 2, 3, 4, 6, 7, 10, 13, 16, 17, 19, 21, 22, 24, 29, 30, 31, 33, 34].”
• Multicore bundle adjustment (2011, methodology): “This includes general purpose libraries like nVidia’s Sparse Matrix-Vector Multiplication [3] and Li and Saad’s work on various GPU-based PCG algorithms and preconditioning techniques [10].”