2004/10/18 by Mario Ruetti, Matthias Troyer, Ruetti, Mario +3 · 1 citation
Computer Science · Physics and Astronomy · #Algorithms and Data Compression #Computational Physics (physics.comp-ph) #Computational Physics and Python Applications #FOS: Mathematics #FOS: Physical sciences #High Energy Physics - Lattice (hep-lat) #Scientific Research and Discoveries #Statistical Mechanics (cond-mat.stat-mech) #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.math/0410385
openalex publication_date 2004/10/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The heart of every Monte Carlo simulation is a source of high quality random numbers and the generator has to be picked carefully. Since the ``Ferrenberg affair'' it is known to a broad community that statistical tests alone do not suffice to determine the quality of a generator, but also application-based tests are needed. With the inclusion of an extensible random number library and the definition of a generic interface into the revised C++ standard it will be important to have access to an extensive C++ random number test suite. Most currently available test suites are limited to a subset of tests are written in Fortran or C and cannot easily be used with the C++ random number generator library.