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  2. Floating-point arithmetic - Wikipedia

    en.wikipedia.org/wiki/Floating-point_arithmetic

    For numbers with a base-2 exponent part of 0, i.e. numbers with an absolute value higher than or equal to 1 but lower than 2, an ULP is exactly 2 −23 or about 10 −7 in single precision, and exactly 2 −53 or about 10 −16 in double precision. The mandated behavior of IEEE-compliant hardware is that the result be within one-half of a ULP.

  3. Fixed-point arithmetic - Wikipedia

    en.wikipedia.org/wiki/Fixed-point_arithmetic

    A fixed-point representation of a fractional number is essentially an integer that is to be implicitly multiplied by a fixed scaling factor. For example, the value 1.23 can be stored in a variable as the integer value 1230 with implicit scaling factor of 1/1000 (meaning that the last 3 decimal digits are implicitly assumed to be a decimal fraction), and the value 1 230 000 can be represented ...

  4. Double-precision floating-point format - Wikipedia

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

    The exponents 000 16 and 7ff 16 have a special meaning: 00000000000 2 =000 16 is used to represent a signed zero (if F = 0) and subnormal numbers (if F ≠ 0); and; 11111111111 2 =7ff 16 is used to represent ∞ (if F = 0) and NaNs (if F ≠ 0), where F is the fractional part of the significand. All bit patterns are valid encoding.

  5. Arbitrary-precision arithmetic - Wikipedia

    en.wikipedia.org/wiki/Arbitrary-precision_arithmetic

    The computer may also offer facilities for splitting a product into a digit and carry without requiring the two operations of mod and div as in the example, and nearly all arithmetic units provide a carry flag which can be exploited in multiple-precision addition and subtraction. This sort of detail is the grist of machine-code programmers, and ...

  6. Single-precision floating-point format - Wikipedia

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

    Conversion of the fractional part: Consider 0.375, the fractional part of 12.375. To convert it into a binary fraction, multiply the fraction by 2, take the integer part and repeat with the new fraction by 2 until a fraction of zero is found or until the precision limit is reached which is 23 fraction digits for IEEE 754 binary32 format.

  7. Exponentiation by squaring - Wikipedia

    en.wikipedia.org/wiki/Exponentiation_by_squaring

    x 1 = x; x 2 = x 2 for i = k - 2 to 0 do if n i = 0 then x 2 = x 1 * x 2; x 1 = x 1 2 else x 1 = x 1 * x 2; x 2 = x 2 2 return x 1. The algorithm performs a fixed sequence of operations (up to log n): a multiplication and squaring takes place for each bit in the exponent, regardless of the bit's specific value. A similar algorithm for ...

  8. IEEE 754-1985 - Wikipedia

    en.wikipedia.org/wiki/IEEE_754-1985

    Now we can read off the fraction and the exponent: the fraction is .01 2 and the exponent is −3. As illustrated in the pictures, the three fields in the IEEE 754 representation of this number are: sign = 0, because the number is positive. (1 indicates negative.) biased exponent = −3 + the "bias".

  9. Minifloat - Wikipedia

    en.wikipedia.org/wiki/Minifloat

    A minifloat in 1 byte (8 bit) with 1 sign bit, 4 exponent bits and 3 significand bits (in short, a 1.4.3 minifloat) is demonstrated here. The exponent bias is defined as 7 to center the values around 1 to match other IEEE 754 floats [3] [4] so (for most values) the actual multiplier for exponent x is 2 x−7. All IEEE 754 principles should be ...