The result of any operation on a fixed-point number is typically stored in a register that is longer than the number's original format. When the result is put back into the original format, the extra bits must be disposed of. That is, the result must be rounded. Rounding involves going from high precision to lower precision and produces quantization errors and computational noise.
To choose the most suitable rounding mode for your application, you need to consider your system requirements and the properties of each rounding mode. The most important properties to consider are:
Cost — Independent of the hardware being used, how much processing expense does the rounding method require?
Bias — What is the expected value of the rounded values minus the original values?
Possibility of overflow — Does the rounding method introduce the possibility of overflow?
For more information on when to use each rounding mode, see Rounding Methods.
Rounding toward ceiling and rounding toward floor are sometimes useful for diagnostic purposes. For example, after a series of arithmetic operations, you may not know the exact answer because of word-size limitations, which introduce rounding. If every operation in the series is performed twice, once rounding to positive infinity and once rounding to negative infinity, you obtain an upper limit and a lower limit on the correct answer. You can then decide if the result is sufficiently accurate or if additional analysis is necessary.