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  2. Floating-point error mitigation - Wikipedia

    en.wikipedia.org/wiki/Floating-point_error...

    Variable length arithmetic represents numbers as a string of digits of a variable's length limited only by the memory available. Variable-length arithmetic operations are considerably slower than fixed-length format floating-point instructions.

  3. Subnormal number - Wikipedia

    en.wikipedia.org/wiki/Subnormal_number

    In a normal floating-point value, there are no leading zeros in the significand (also commonly called mantissa); rather, leading zeros are removed by adjusting the exponent (for example, the number 0.0123 would be written as 1.23 × 10 −2). Conversely, a denormalized floating-point value has a significand with a leading digit of zero.

  4. Arithmetic underflow - Wikipedia

    en.wikipedia.org/wiki/Arithmetic_underflow

    Arithmetic underflow can occur when the true result of a floating-point operation is smaller in magnitude (that is, closer to zero) than the smallest value representable as a normal floating-point number in the target datatype. [1] Underflow can in part be regarded as negative overflow of the exponent of the floating-point value. For example ...

  5. IEEE 754-1985 - Wikipedia

    en.wikipedia.org/wiki/IEEE_754-1985

    Some operations of floating-point arithmetic are invalid, such as taking the square root of a negative number. The act of reaching an invalid result is called a floating-point exception. An exceptional result is represented by a special code called a NaN, for "Not a Number". All NaNs in IEEE 754-1985 have this format: sign = either 0 or 1.

  6. Unum (number format) - Wikipedia

    en.wikipedia.org/wiki/Unum_(number_format)

    Unums provide only two kinds of numerical exception, quiet and signaling NaN (Not-a-Number). Unum computation may deliver overly loose bounds from the selection of an algebraically correct but numerically unstable algorithm. The benefits of unum over short precision floating point for problems requiring low precision are not obvious.

  7. Floating-point arithmetic - Wikipedia

    en.wikipedia.org/wiki/Floating-point_arithmetic

    A floating-point system can be used to represent, with a fixed number of digits, numbers of very different orders of magnitude — such as the number of meters between galaxies or between protons in an atom. For this reason, floating-point arithmetic is often used to allow very small and very large real numbers that require fast processing times.

  8. NaN - Wikipedia

    en.wikipedia.org/wiki/NaN

    Floating-point operations other than ordered comparisons normally propagate a quiet NaN (qNaN). Most floating-point operations on a signaling NaN ( sNaN ) signal the invalid-operation exception ; the default exception action is then the same as for qNaN operands and they produce a qNaN if producing a floating-point result.

  9. Unit in the last place - Wikipedia

    en.wikipedia.org/wiki/Unit_in_the_last_place

    The IEEE 754 specification—followed by all modern floating-point hardware—requires that the result of an elementary arithmetic operation (addition, subtraction, multiplication, division, and square root since 1985, and FMA since 2008) be correctly rounded, which implies that in rounding to nearest, the rounded result is within 0.5 ulp of ...