Stochastic Rounding is a probabilistic rounding mode that is surprisingly effective in large-scale computations and low-precision arithmetic. Its random nature promotes error cancellation rather than error accumulation, resulting in slower growth of roundoff errors as the problem size increases, especially when compared to traditional deterministic rounding methods, such as rounding-to-nearest. We advocate for SR as a foundational tool in the complexity analysis of algorithms, and suggest several research directions.

翻译：随机舍入是一种概率性舍入模式，在大规模计算和低精度算术中表现出惊人的有效性。其随机特性促进了误差抵消而非误差累积，导致随着问题规模增大，舍入误差的增长速度较慢，尤其是在与传统确定性舍入方法（如最近舍入）相比时。我们主张将随机舍入作为算法复杂度分析的基础工具，并提出了若干研究方向。