MPFR: A multiple-precision binary floating-point library with correct rounding

By Laurent Fousse, G. Hanrot, V. Lefèvre, P. Pélissier, P. Zimmermann. TOMS, 2007

1,076 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Hardware and Architecture, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b5445b99cf553ba1dbef8fa2943f9eb70a253a38

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. James R. Wilcox
2. Felipe Serrano
3. Dieter Weninger
4. Janak Raj Sharma
5. Marie E. Rognes
6. Stefan Vigerske
7. Ambros M. Gleixner
8. Zachary Tatlock

In Hardware and Architecture

1. Alexandros Stamatakis
2. Marie E. Rognes
3. Garth N. Wells
4. Anders Logg
5. Christoph Weniger
6. G. Feiden
7. Matei A. Zaharia
8. Shoumik Palkar

In Signal Processing

1. Fredrik Johansson
2. Fabian Pedregosa
3. Marc E. Pfetsch

Citations in the corpus

• ias15: a fast, adaptive, high-order integrator for gravitational dynamics, accurate to machine precision over a billion orbits (2014, methodology): “All hard coded constants have been precalculated in extended floating-point precision using the GNU MP library (Fousse et al. 2007).”
• SweeD: Likelihood-Based Detection of Selective Sweeps in Thousands of Genomes (2013, methodology): “An efficient use of scarce computing and memory resources is also required. Furthermore, efficient parallel implementations are needed to analyze large data sets within reasonable times on state-of-the-art multiand many-core processors. At present, only a handful of tools that scale to thousands of ”
• The SCIP Optimization Suite 7.0 (2020, methodology): “For this purpose, SoPlex uses the Boost multiprecision library [14], which wraps either MPFR [25] (if available), or uses its own implementation of multi-precision floating-point numbers.”
• SymPy: Symbolic computing in Python (2017, background): “This is a primary advantage of mpmath over other multiple precision libraries such as GNU MPFR (Fousse et al., 2007), which is faster.”
• Efficient Jarratt-like methods for solving systems of nonlinear equations (2014, methodology): “To claim this estimation, we express the cost of the evaluation of the elementary functions in terms of products, which depends on the computer, the software and the arithmetics used [10,18].”
• The FEniCS Project Version 1.5 (2015, methodology): “…backends, built automatically by mshr build system [The CGAL Project, 2015, Si, 2015] • GMP, MPFR; arbitrary-precision and multiple-precision arithmetic libraries, required by mshr [Granlund and the GMP development team, 2014, Fousse et al., 2007] • Scipy; needed by UFL for evaluation of error…”
• Automatically improving accuracy for floating point expressions (2015, methodology): “Recent work on this problem allowed the creation of MPFR, an arbitrary-precision floating point library with correct rounding [16].”