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  2. NaN - Wikipedia

    en.wikipedia.org/wiki/NaN

    In particular, IEEE 754 already uses "canonical NaN" with the meaning of "canonical encoding of a NaN" (e.g. "isCanonical(x) is true if and only if x is a finite number, infinity, or NaN that is canonical." page 38, but also for totalOrder page 42), thus a different meaning from what is used here. Please help clarify the section.

  3. Half-precision floating-point format - Wikipedia

    en.wikipedia.org/wiki/Half-precision_floating...

    E min = 00001 2 − 01111 2 = −14; E max = 11110 2 − 01111 2 = 15; Exponent bias = 01111 2 = 15; Thus, as defined by the offset binary representation, in order to get the true exponent the offset of 15 has to be subtracted from the stored exponent. The stored exponents 00000 2 and 11111 2 are interpreted specially.

  4. IEEE 754 - Wikipedia

    en.wikipedia.org/wiki/IEEE_754

    NaN is treated as if it had a larger absolute value than Infinity (or any other floating-point numbers). (−NaN < −Infinity; +Infinity < +NaN.) qNaN and sNaN are treated as if qNaN had a larger absolute value than sNaN. (−qNaN < −sNaN; +sNaN < +qNaN.) NaN is then sorted according to the payload.

  5. IEEE 754-1985 - Wikipedia

    en.wikipedia.org/wiki/IEEE_754-1985

    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. biased exponent = all 1 bits. fraction = anything except all 0 bits (since all 0 bits represents infinity).

  6. Single-precision floating-point format - Wikipedia

    en.wikipedia.org/wiki/Single-precision_floating...

    However, float in Python, Ruby, PHP, and OCaml and single in versions of Octave before 3.2 refer to double-precision numbers. In most implementations of PostScript, and some embedded systems, the only supported precision is single.

  7. bfloat16 floating-point format - Wikipedia

    en.wikipedia.org/wiki/Bfloat16_floating-point_format

    3f80 = 0 01111111 0000000 = 1 c000 = 1 10000000 0000000 = −2 7f7f = 0 11111110 1111111 = (2 8 − 1) × 2 −7 × 2 127 ≈ 3.38953139 × 10 38 (max finite positive value in bfloat16 precision) 0080 = 0 00000001 0000000 = 2 −126 ≈ 1.175494351 × 10 −38 (min normalized positive value in bfloat16 precision and single-precision floating point)

  8. Division by zero - Wikipedia

    en.wikipedia.org/wiki/Division_by_zero

    A NaN (not a number) value represents undefined results. In IEEE arithmetic, division of 0/0 or ∞/∞ results in NaN, but otherwise division always produces a well-defined result. Dividing any non-zero number by positive zero (+0) results in an infinity of the same sign as the dividend.

  9. 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.