We present a pair arithmetic for the four basic operations and square root. It can be regarded as a simplified, more-efficient double-double arithmetic. The central assumption on the underlying arithmetic is the first standard model for error analysis for operations on a discrete set of real numbers. Neither do we require a floating-point grid nor a rounding to nearest property. Based on that, we define a relative rounding error unit u and prove rigorous error bounds for the computed result of an arbitrary arithmetic expression depending on u, the size of the expression, and possibly a condition measure. In the second part of this note, we extend the error analysis by examining requirements to ensure faithfully rounded outputs and apply our results to IEEE 754 standard conform floating-point systems. For a class of mathematical expressions, using an IEEE 754 standard conform arithmetic with base β , the result is proved to be faithfully rounded for up to 1 / √ β u - 2 operations. Our findings cover a number of previously published algorithms to compute faithfully rounded results, among them Horner’s scheme, products, sums, dot products, or Euclidean norm. Beyond that, several other problems can be analyzed, such as polynomial interpolation, orientation problems, Householder transformations, or the smallest singular value of Hilbert matrices of large size.
More than 45 years ago, Dekker proved that it is possible to evaluate the exact error of a floating-point sum with only two additional floating-point operations, provided certain conditions are met. Today the respective algorithm for transforming a sum into its floating-point approximation and the corresponding error is widely referred to as $${{\,\mathrm{FastTwoSum}\,}}$$ FastTwoSum . Besides some assumptions on the floating-point system itself—all of which are satisfied by any binary IEEE $$754$$ 754 standard conform arithmetic, the main practical limitation of $${{\,\mathrm{FastTwoSum}\,}}$$ FastTwoSum is that the summands have to be ordered according to their exponents. In most preceding applications of $${{\,\mathrm{FastTwoSum}\,}}$$ FastTwoSum , however, a more stringent condition is used, namely that the summands have to be sorted according to their absolute value. In remembrance of Dekker’s work, this note reminds the original assumptions for an error-free transformation via$${{\,\mathrm{FastTwoSum}\,}}$$ FastTwoSum . Moreover, we generalize the conditions for arbitrary bases and discuss a possible modification of the $${{\,\mathrm{FastTwoSum}\,}}$$ FastTwoSum algorithm to extend its applicability even further. Subsequently, a range of programs exploiting the wider applicability is presented. This comprises the OnlineExactSum algorithm by Zhu and Hayes, an error-free transformation from a product of three floating-point numbers to a sum of the same number of addends, and an algorithm for accurate summation proposed by Demmel and Hida.
We introduce a new accurate summation algorithm based on the error-free summation into floating-point buckets. Our algorithm exploits ideas from Zhu and Hayes’ OnlineExactSum , but it uses a significantly smaller number of accumulators and has a better instruction-level parallelism. In the default setting, our implementation aaaSum returns a faithfully rounded floating-point approximation of the true sum. We also discuss possible modifications for the computation of reproducible, correctly rounded, and multiple precision floating-point approximations. The computational overhead for any of these modifications is kept comparably small. Numerical tests demonstrate that aaaSum performs well for very small till large problem sizes, independent of the condition number of the problem. We compare our algorithm with other accurate and high-precision summation approaches.
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