Precimonious: Tuning assistant for floating-point precision

By Cindy Rubio-González, Cuong Nguyen, Hong Diep Nguyen, J. Demmel, W. Kahan, Koushik Sen, D. Bailey, Costin Iancu, David G. Hough. 2013 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2013

331 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/b1c78011b772dc4e95e6e9633e500747df2747d1

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

1. Koushik Sen
2. Zachary Tatlock
3. James R. Wilcox
4. George C. Necula
5. Wontae Choi
6. Pavel Panchekha
7. Xavier Leroy
8. Oleksandr Polozov

In Computational Theory and Mathematics

1. James R. Wilcox
2. Koushik Sen
3. George C. Necula
4. Wontae Choi
5. Zachary Tatlock
6. Pavel Panchekha
7. Oleksandr Polozov
8. Sumit Gulwani

In Hardware and Architecture

1. Koushik Sen

Citations in the corpus

• Guided GUI testing of android apps with minimal restart and approximate learning (2013, background): “It was proposed in the context of fault isolation and has been successfully applied to many problems [22, 84, 99, 123].”
• Automatically improving accuracy for floating point expressions (2015, background): “Precimonious [35] attempts to decrease the precision of intermediate results to improve run-time and memory use.”