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The Perils of Floating Pointby Bruce M. BushCopyright (c) 1996 Lahey Computer Systems, Inc. Permission to copy is granted with acknowledgement of the source. Many great engineering and scientific advances of recent decades would not have been possible without the floatingpoint capabilities of digital computers. Still, some results of floatingpoint calculations look pretty strange, even to people with years of mathematical experience. I will attempt to explain the causes of some of these strange results and give some suggestions where appropriate. Floatingpoint representations and arithmetic are inexact, but I don't believe that is particularly troublesome to most programmers. Many input values are measurements, which are inherently inexact, so the question about the output values isn't whether there is error, but how much error should be expected. However, when you can compute a more accurate result in your head than your computer can with its floatingpoint, you start to get suspicious.
I have programmed my examples in FORTRAN for a couple of reasons:
Binary FloatingPointAt the heart of many strange results is one fundamental: floatingpoint on computers is usually base 2, whereas the external representation is base 10. We expect that 1/3 will not be exactly representable, but it seems intuitive that .01 would be. Not so! .01 in IEEE singleprecision format is exactly 10737418/1073741824 or approximately 0.009999999776482582. You might not even notice this difference until you see a bit of code like the following:REAL X DATA X /.01/ IF ( X * 100.d0 .NE. 1.0 ) THEN PRINT *, 'Many systems print this surprising result. ' ELSE PRINT *, 'And some may print this.' ENDIFBase10 floatingpoint implementations don't have this anomaly. However, base10 floatingpoint implementations are rare because base2 (binary) arithmetic is so much faster on digital computers. InexactnessFloatingpoint arithmetic on digital computers is inherently inexact. The 24 bits (including the hidden bit) of mantissa in a 32bit floatingpoint number represent approximately 7 significant decimal digits. Unlike the real number system, which is continuous, a floatingpoint system has gaps between each number. If a number is not exactly representable, then it must be approximated by one of the nearest representable values.Because the same number of bits are used to represent all normalized numbers, the smaller the exponent, the greater the density of representable numbers. For example, there are approximately 8,388,607 singleprecision numbers between 1.0 and 2.0, while there are only about 8191 between 1023.0 and 1024.0. On any computer, mathematically equivalent expressions can produce different values using floatingpoint arithmetic. In the following example, Z and Z1 will typically have different values because (1/Y) or 1/7 is not exactly representable in binary floatingpoint: REAL X, Y, Y1, Z, Z1 DATA X/77777/, Y/7/ Y1 = 1 / Y Z = X / Y Z1 = X * Y1 IF (Z .NE. Z1) PRINT *, 'Not equal!' END Insignificant DigitsThe following code example illustrates the phenomenon of meaningless digits that could seem to be significant:REAL A, Y DATA Y /1000.2/ ! About 7 digits of precision in Y A = Y  1000.0 ! About 3 significant digits in result PRINT *, A ! Prints 0.200012 ENDA singleprecision (REAL) entity can represent a maximum of about 7 decimal digits of precision, so the subtraction above represents (1000.200  1000.000). The result, therefore, can only represent about 3 decimal digits. The program, however, will happily print out "0.200012". Because 1000.2 is not exactly representable in binary floatingpoint and 1000.0 is, the result A is a little larger than 0.2. The computer doesn't know that the digits beyond ".200" have no meaning. Perhaps someday the computer will keep track of the number of bits in a result that are truly significant. For now, it is still the responsibility of the programmer. If you stay aware of the number of decimal digits represented by a data type, approximating the number of significant digits is a straightforward, but perhaps timeconsuming, task. Give the most attention to:
1. subtractions of numbers that are nearly equal,
Crazy ConversionsConversions to integer can unmask inaccuracies in a floatingpoint number, as is demonstrated by the next example. The closest singleprecision floatingpoint number to 21.33 is slightly less than 21.33, so when it is multiplied by 100., the result Y is slightly less than 2133.0. If you print Y in a typical floatingpoint format, rounding causes it to be displayed as 2133.00. However, if you assign Y to an integer I, no rounding is done, and the number is truncated to 2132.REAL X, Y INTEGER I X = 21.33 ! Slightly less than 21.33 Y = X * 100. ! Slightly less than 2133.0 I = Y ! Truncates to 2132 PRINT *, Y, I ! Prints "2133.00 2132" ENDThe following program prints "1.66661000251770" when compiled with Lahey's LF90: DOUBLE PRECISION D REAL X X = 1.66661 ! Assign to single precision D = X ! Convert to double precision PRINT *, D ENDYou ask, "Why do you extend the singleprecision number with the seemingly random '000251770'?" Well, the number isn't extended with random values; the computer's floatingpoint does the conversion by padding with zeros in the binary representation. So D is exactly equal to X, but when it is printed out to 15 decimal digits, the inexactness shows up. This is also another example of insignificant digits. Remember that assigning a singleprecision number to a doubleprecision number doesn't increase the number of significant digits. Too Many DigitsYou may decide to check the previous program example by printing out both D and X in the same format, like this:30 FORMAT (X, 2G25.15) PRINT 30, X, DIn some FORTRAN implementations, both numbers print out the same. You may walk away satisfied, but you are actually being misled by loworder digits in the display of the singleprecision number. In Lahey FORTRAN the numbers are printed out as: 1.66661000000000 1.66661000251770The reason for this is fairly simple: the formattedI/O conversion routines know that the absolute maximum decimal digits that have any significance when printing a singleprecision entity is 9. The rest of the field is filled with the current "precisionfill" character, which is "0" by default. The precisionfill character can be changed to any ASCII character, e.g., asterisk or blank. Changing the precisionfill character to "*" emphasizes the insignificance of the loworder digits: 1.66661000****** 1.66661000251770 Too Much PrecisionThe IEEE singleprecision format has 24 bits of mantissa, 8 bits of exponent, and a sign bit. The internal floatingpoint registers in Intel microprocessors such as the Pentium have 64 bits of mantissa, 15 bits of exponent and a sign bit. This allows intermediate calculations to be performed with much less loss of precision than many other implementations. The down side of this is that, depending upon how intermediate values are kept in registers, calculations that look the same can give different results.In the following example, a compiler could generate code that calculates A/B, stores the intermediate result into a singleprecision temporary, calculates X/Y, performs a reversed subtract of the temporary, then stores the result. Z will not be zero, because precision will be lost in storing into a singleprecision temporary. If the generated code keeps the intermediate result in registers, no precision will be lost, and Z will be zero. REAL A, B, X, Y, Z DATA A/10./, B/3./, X/10./, Y/3./ Z = (A/B)  (X/Y) PRINT *, Z ! Could be zero or not. ENDThe next example illustrates a variation on the previous example. A compiler can still generate code that keeps the intermediate result C in a register, which means that Z will be zero. If precision is lost by storing A/B into C, then Z will be nonzero. REAL A, B, C, X, Y, Z DATA A/10./, B/3./, X/10./, Y/3./ C = A/B Z = C  (X/Y) PRINT *, Z ! Could be zero or not. ENDThe slight variation of adding the statement label 100 foils the optimization of keeping C in a register, so Z will probably be nonzero with almost any compiler. REAL A, B, C, X, Y, Z DATA A/10./, B/3./, X/10./, Y/3./ C = A/B 100 Z = C  (X/Y) PRINT *, Z IF ( ... ) GO TO 100 END Safe ComparisonsDifferent computers use different numbers of bits to store floatingpoint numbers. Even when the same IEEE formats are used for storing numbers, differences in calculations can occur because of the size of intermediate registers. To increase portability and to ensure consistent results, I recommend against comparing for exact equality of real numbers in FORTRAN. A better technique is to compare the absolute value of the difference of two numbers with an appropriate epsilon to get relations like approximately equal, definitely greater than, etc.Example: REAL EPSILON, X, A, B PARAMETER (EPSILON = .000001) DATA A/13.9/, B/.000005/ X = (A * B) / B IF (ABS(X  A) .LE. (ABS(X)*EPSILON)) THEN PRINT *, 'X is approximately equal to A' ENDIF IF ((X  A) .GT. (ABS(X)*EPSILON)) THEN PRINT *, 'X is definitely greater than A' ENDIF IF ((A  X) .GT. (ABS(X)*EPSILON)) THEN PRINT *, 'X is definitely less than A' ENDIF ENDMultiplying the epsilon by one of the comparands adjusts the comparison to the range of the numbers, allowing a single epsilon to be used for many, or perhaps all compares. For the most predictable results, use an epsilon half as large and multiply it by the sum of the comparands, as in the following example: REAL EPSILON, X, A, B PARAMETER (EPSILON = .0000005) ! Smaller epsilon DATA A/13.9/, B/.000005/ X = (A * B) / B IF (ABS(X  A) .LE. (ABS(X+A)*EPSILON)) THEN PRINT *, 'X is approximately equal to A' ENDIFEven comparisons of greaterthan, lessthanorequalto, etc., can produce unexpected results, because a floatingpoint computation can produce a value that is not mathematically possible. In the following example X is always mathematically greater than J, so X/J should always be greater than 1.0. For large values of J, however, the addition of delta is not representable by X, because of limited mantissa size. REAL X, DELTA DATA DELTA/.001/ DO 10 J = 1, 100 000 X = J + DELTA ! Make X bigger than J CALL SUB (X) ! Force X out of register IF ( X/J .LE. 1.0 ) THEN ! X/J always > 1 ? PRINT *, 'Error !' STOP END IF 10 CONTINUE END SUBROUTINE SUB (X) END Programming with the PerilsThere are no easy answers. It is the nature of binary floatingpoint to behave the way I have described. In order to take advantage of the power of computer floatingpoint, you need to know its limitations and work within them. Keeping the following things in mind when programming floatingpoint arithmetic should help a lot:
1. Only about 7 decimal digits are representable in singleprecision
IEEE format, and about 16 in doubleprecision IEEE format.
I hope that I have given you a little more awareness of what is happening in the internals of floatingpoint arithmetic, and that some of the strange results you have seen make a little more sense. While some of the "perils" can be avoided, many just need to be understood and accepted.
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) has
defined standards for floatingpoint representations and computational
results (IEEE Std 7541985). This section is an overview of the IEEE
standard for representing floatingpoint numbers. The data contained
herein helps explain some of the details in the rest of the article,
but is not required for understanding the basic concepts.
