@inproceedings{chi-gop-rak-sol-14-aa-maxfper,
  author = {Chiang, Wei-Fan and Gopalakrishnan, Ganesh and Rakamaric, Zvonimir and Solovyev, Alexey},
  title = {Efficient Search for Inputs Causing High Floating-Point Errors},
  booktitle = {Proceedings of the 19th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP)},
  location = {Orlando, US},
  pages = {43-52},
  doi = {10.1145/2555243.2555265},
  year = 2014,
  month = feb,
  comment = {Claims that AA bounds are too pessimistic so they develop their own method},
  abstract = {Tools for floating-point error estimation are fundamental to program understanding and optimization. In this paper, we focus on tools for determining the input settings to a floating point routine that maximizes its result error. Such tools can help support activities such as precision allocation, performance optimization, and auto-tuning. We benchmark current abstraction-based precision analysis methods, and show that they often do not work at scale, or generate highly pessimistic error estimates, often caused by non-linear operators or complex input constraints that define the set of legal inputs. We show that while concrete-testing-based error estimation methods based on maintaining shadow values at higher precision can search out higher error-inducing inputs, suit able heuristic search guidance is key to finding higher errors. We develop a heuristic search algorithm called Binary Guided Random Testing (BGRT). In 45 of the 48 total benchmarks, including many real-world routines, BGRT returns higher guaranteed errors. We also evaluate BGRT against two other heuristic search methods called ILS and PSO, obtaining better results.}
}