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Double-precision floating-point format (sometimes called FP64 or float64) is a floating-point number format, usually occupying 64 bits in computer memory; it represents a wide range of numeric values by using a floating radix point. Double precision may be chosen when the range or precision of single precision would be insufficient.
These include: as noted above, computing all expressions and intermediate results in the highest precision supported in hardware (a common rule of thumb is to carry twice the precision of the desired result, i.e. compute in double precision for a final single-precision result, or in double extended or quad precision for up to double-precision ...
Fast2Sum is often used implicitly in other algorithms such as compensated summation algorithms; [1] Kahan's summation algorithm was published first in 1965, [3] and Fast2Sum was later factored out of it by Dekker in 1971 for double-double arithmetic algorithms. [4] The names 2Sum and Fast2Sum appear to have been applied retroactively by ...
For summing [, +,,] in double precision, Kahan's algorithm yields 0.0, whereas Neumaier's algorithm yields the correct value 2.0. Higher-order modifications of better accuracy are also possible. For example, a variant suggested by Klein, [ 12 ] which he called a second-order "iterative Kahan–Babuška algorithm".
A property of the single- and double-precision formats is that their encoding allows one to easily sort them without using floating-point hardware, as if the bits represented sign-magnitude integers, although it is unclear whether this was a design consideration (it seems noteworthy that the earlier IBM hexadecimal floating-point representation ...
The following simple example demonstrates the advantage of using SSE. Consider an operation like vector addition, which is used very often in computer graphics applications. To add two single precision, four-component vectors together using x86 requires four floating-point addition instructions.
Relative precision of single (binary32) and double precision (binary64) numbers, compared with decimal representations using a fixed number of significant digits. Relative precision is defined here as ulp(x)/x, where ulp(x) is the unit in the last place in the representation of x, i.e. the gap between x and the next representable number.
In 1963, the GE-235 featured an "Auxiliary Arithmetic Unit" for floating point and double-precision calculations. [ 6 ] Historically, some systems implemented floating point with a coprocessor rather than as an integrated unit (but now in addition to the CPU, e.g. GPUs – that are coprocessors not always built into the CPU – have FPUs as a ...