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Stochastic Rounding 2.0, with a View towards Complexity Analysis
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Stochastic Rounding 2.0, with a View towards Complexity Analysis
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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.
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Cited by 1 Pith paper
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What is New in Stochastic Rounding: a Survey on Theory, Hardware, and Applications
An update survey of stochastic rounding (2022–2026) that centers limited-precision SR, commercial hardware, probabilistic error bounds, and applications in ML and scientific computing.
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