Alternative Number Formats
Contents:
MultipleBase Composite Integers
LevelIndex and Symmetric LevelIndex
Knuth's Binary Lexicographic Strings
Alternative Algorithms for Standard Formats
Residue Number System (RNS)
A base is chosen consisting of a set of relativelyprime numbers. Each normal integer is represented in the RNS as a set of digits, each digit is the number's value modulo each of the relativelyprime numbers in the base. For example, if the base is {2,3,5} the numbers 0 through 29 can be represented as follows:
{2,3,5} {2,3,5} {2,3,5} 0 0,0,0 10 0,1,0 20 0,2,0 1 1,1,1 11 1,2,1 21 1,0,1 2 0,2,2 12 0,0,2 22 0,1,2 3 1,0,3 13 1,1,3 23 1,2,3 4 0,1,4 14 0,2,4 24 0,0,4 5 1,2,0 15 1,0,0 25 1,1,0 6 0,0,1 16 0,1,1 26 0,2,1 7 1,1,2 17 1,2,2 27 1,0,2 8 0,2,3 18 0,0,3 28 0,1,3 9 1,0,4 19 1,1,4 29 1,2,4For example, 17 is (1,2,2) because 17 = 1 mod 2, 17 = 2 mod 3 and 17 = 2 mod 5. Notice that none of the other numbers in the table has the representation (1,2,2), in fact no two numbers in the table have the same representation.
Residue number systems closely resemble certain astronomical and calendrical calculations.
The calculation of Easter uses a {19,7} residue system with additional periodic corrections to achieve greater agreement with the actual phases of the moon. With all the corections the current Gregorian system has a cycle of 2081882250=2×3^{4}×5^{3}×7×19×773 solar days, equivalent to 5700000 Gregorian years of 365.2425 solar days.
The Akan calendar involves a base {6,7} system formed by the use of a traditional sixday week concurrently with the sevenday week to form a 42day period. The Rokuyo of the Japanese calendar are a repeating 6day cycle, but are reset at the beginning of every month, before even a single 42day cycle has taken place.
Similar to the Akan situation, the Javanese calendar involves a traditional fiveday cycle combined with the 7day week, creating a 35day period called the Wetonan cycle.
A base {13,20} system is used in the Mayan liturgical year (see [12] pp 312313 and Maya calendar), a daycounting system still in use by some residents of Oaxaca and the Guatemala. In ancient usage, combined with a 365day civil year it constituted a base{365,13,20} system with the same range as a {73,13,20} system; 73×13×20=18980, the number of days in the 52year "calendar wheel" (see [12] pp. 314315). By contrast, the "Long Count" (referenced in the movie 2012) is a comparatively much simpler {20,20,20,18,20} system, a cycle of 2880000 days (see [12] pp. 316322).
Residue number systems have been proposed as a way to make computers faster because addition, subtraction and multiplication can be done with far less circuit complexity. However, other operations, notably division and conversion to/from a standard base for input and output, make the system impractical for most applications.
To add, subtract or multiply, you perform a modulo addition, subtraction or multiplication within each field. For example, to multiply 3 by 7:
{2,3,5} 3 = (1,0,3) 1 x 1 = 1 mod 2 (2's column) 7 = (1,1,2) because 0 x 1 = 0 mod 3 (3's column) 21 = (1,0,1) 3 x 2 = 1 mod 5 (5's column)Converting between binary and RNS is a little difficult, and is based on the Chinese Remainder Theorem.
Division is very difficult, and is the subject of much research.
Articles on residue number systems:
"Residue Number System Based Multiple Code DSCDMA Systems", LieLiang Yang and Lajos Hanzo, The IEEE Vehicular Technology Conference, Houston, Texas, USA, May 1619, 1999, pp14501454 http://wwwmobile.ecs.soton.ac.uk/lly/papers/VTC99_RNS1web.pdf
"Novel HighRadix Residue Number System Processors", V. Paliouras and T. Stouraitis, IEEE Transactions on Circuits and SystemsPart II, vol. 47, no. 10, pp. 10591073, October 2000. http://www.vlsi.ee.upatras.gr/Users/Associates/Paliuras/47csii10paliouras.pdf
http://www.dspvlsi.uniroma2.it/pubs/iscas01a/iscas01a.html
padic Numbers
A padic number system extends integer and rational numbers to a system of "real numbers" in a different way from the ordinary real numbers, by defining "closeness" in a very different way: Given a prime number p, two numbers that differ by any multiple of p are equally close, unless they differ by a multiple of p^{2} in which case they are considered closer (and even closer if their difference is a multiple of p^{3}, and so on). Since differences of p^{n} are less and less significant as n increases, numbers can be written in "basep" notation with the digits in reverse order, with higher powers of p trailing off to the right.
Arithmetic with padic numbers is similar in ways to the residue number systems.
Examples of 5adic arithmetic:
5adic multiplication table

Notice the similarity to base5 multiplication; for example in base 5, 2_{5}×4_{5} = 13_{5}; and in 5adic representation .2×.4 = .31; the answer is the same but with the digits reversed.
Computing the padic Expansion of a Ratio A/B
We'll continue with the p=5 or "5adic" system. For this example we want to represent 14/105, so a=10 and b=105.
First, remove any common factor(s): reduce 14/105 to 2/15.
Next, remove any power(s) of p, and remember them for later. Here we have a power of 5 in the denominator: a/b = c/5d, where c=2 and d=3. We'll compute 2/3, then multiply the result by 1/5 (which is as simple as shifting the "decimal point", because padic representation is like basep).
If the numerator is not 1, we will first compute 1/d, then multiply by c using padic multiplication. So in the example, we compute 1/3 then multiply by 2 afterwards.
At this point we've reduced the problem to computing the reciprocal of an integer 1/d. Let e = d mod p and use division modulo p to determine 1/e mod p. In this case, 1/3 = 2 mod 5 (in row 2, colmn 1 of the following table):
Division modulo 5

We will first compute 1/de, then multiply by e to get e/de = 1/d. Since e is 2, we need to compute 1/6.
Now we have a fraction of the form 1/(Np+1) for some N. The full padic representation can be computed by a process similar to long division:
.1404040404.. _____________________________ .11 ) .1 .11 .4344444..  .0444444.. .044 .0104444..  .00044444..We are computing 1/6 which is .1/.11 in 5adic representation. Using the division table above, 1/1 = 1 mod 5, so the first digit of the quotient is 1. Multiply this by the divisor to get .11 and subtract. To subtract .11 from .1, we first complement .11 getting .434444... and add this to .1, carrying one to the right: .1 plus .434444... equals .0444444...
Divide the first digit of the remainer by the first digit of the dividend. Using the division table again, 1/4 = 4 mod 5. So the second digit of the quotient is 4. Multiplying by the dividend we get .044, complement to get .0104444... and add to get .0004444...
The next digit of the partial remainder is 0, giving a quotient digit of 0 and no change to the partial remainder. Then we get 4 for the next quotient digit, and the same calculation repeats.
So the 5adic representation of 1/6 is .14040404... and to get 1/3 = 1/d, we multiply this by 2:
.14040404.. x .2  .23131313..To get 2/3 = c/d, we multiply again by 2:
.23131313.. x .2  .41313131..To get 2/15 = a/b = c/5d, we divide by 5, which is just a shift of the "decimal point". Thus, the 5adic representation of 2/15 is 0.04131313...
DoubleBase Number System (DBNS)
This is a form of alternate representation being studied for use in digital signal processing at the University of Windsor, Ontario Canada. Numbers are expressed as a sum of one or more terms of the form 2^{a}3^{b}. Each term is either present or absent, represented by a coefficient of 1 or 0 respectively. DBNS representation allows for addition, subtraction and multiplication that are very efficient (by gate count) so is well suited for signal processing or similar applications; the only drawback is conversion to and from standard representation.
"Theory and Applications of the DoubleBase Number System", Vassil S. Dimitrov, Graham A. Jullien, and William C. Miller, IEEE Transactions on Computers, Vol. 48, No. 10, October 1999
MultipleBase Composite Integers
This system was suggested by George Spelvin^{1}, who also suggested some integeronly microfloat formats. MBCI integers are used to express integer values from a universe in which all the prime factors are in a fairly small set. For example, if the eligible primes are 2, 3, and 5, an MBCI system will represent any integer of the form 2^{a}3^{b}5^{c} for integer values of a, b and c. Such values form the sequence: 1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 15, 16, 18, 20, 24, 25, 27, 30, 32, 36, 40, 45, 48, 50, 54, 60, 64, 72, 75, 80, 81, 90, ... (Sloane's A051037).
For fixedlength representations, the typical strategy is to limit the exponent of each eligible prime to a range that can be expressed in a given number of binary digits. For example, using the primes 2 3 and 5 again, one could limit the exponent of 2 to fall in the range 0..15, and the exponents of 3 and 5 to fall in the range 0..3; this allows the exponents to be stored in 4, 2, and 2 bits respectively for a total of 8 bits. Such a system encodes lots of commonly used integer values, including most of the commonlyencountered highlycomposite numbers (such as 24, 144, 3600, etc.), all of the common video resolution heights and widths (like 640, 480, 1024, 768, 720 and 1080), and all of the common modem baud rates (like 2400, 19200, and 57600).
Another fixedwidth representation does not use explicit bit fields for each exponent, but instead simply indexes into a (hypothetical infinite) list of all integers with the eligible prime factors. For example, a 5bit representation would be used to specify any of the 32 smallest positive integers of the form 2^{a}3^{b}5^{c}, which are the 32 values of sequence A051037 shown above.
Factorial Base Number System (FBNS)
Like MultipleBase systems but taking the idea further, a
factorial base number system (or "factorial number system" as called
by Knuth)
has digits in each position that range from 0 to N1,
with the "place value" of each position being (N1)! with N
starting at 1. Counting in this system looks like this: 0, 10, 100,
110, 200, 210, 1000, 1010, 1100, 1110, 1200, 1210, 2000, 2010, 2100,
2110, 2200, 2210, 3000, 3010, 3100, 3110, 3200, 3210, 10000, 10010,
... (Sloane's sequence A124252). The rightmost digit is always
0; to avoid this one might say that N starts at 2, in which case you
get the same thing without the final 0: 0, 1, 10, 11, 20, 21, 100,
101, 110, 111, 120, 121, 200, 201, 210, 211, 220, 221, 300, 301, 310,
311, 320, 321, 1000, 1001, ... (Sloane's sequence A7623).
If you want to handle fractions, then an FBNS provides a way to express any rational number with a finite number of digits to the right of the "decimal point". The digits to the right are for multiples of the fraction 1/N!=, with N starting at 2. The expansions are often a bit nonintuitive, as these examples from the Wikipedia page show:
1/2=0.1_{!}
1/3=0.02_{!}
2/3=0.11_{!}
1/4=0.012_{!}
3/4=0.112_{!}
1/5=0.0104_{!}
1/6=0.01_{!}
5/6=0.12_{!}
1/7=0.003206_{!}
1/8=0.003_{!}
1/9=0.00232_{!}
1/10=0.0022_{!}
1/11=0.002053140A_{!}
2/11=0.0101462819_{!}
9/11=0.1133105082_{!}
10/11=0.1214036491_{!}
Brent Sanders has made an implementation in Ruby (for integers only, no fractions), here is the source code
Logarithmic Number System (LNS)
Some articles on "logarithmic" number systems:
M. Arnold, T. Bailey, J. Cowles: "Comments on 'An Architecture for Addition and Subtraction of Long Word Length Numbers in the Logarithmic Number System'. , IEEE Trans. on Computers, Jun 1992, pp 786788
"SemiLogarithmic Number Systems", J.M. Muller, A. Scherbyna, and A. Tisserand, IEEE Trans. on Computers, Feb 1998 http://www.enslyon.fr/~jmmuller/IEEETCFev98.pdf
"Efficient algorithms for binary logarithmic conversion and addition", Y. Wan and C.L. Wey, IEEE Trans. on Computers and Digital Techniques, May 1999, p.168
"Efficient conversion algorithms for longwordlength binary logarithmic numbers and logic implementation", Y. Wan, M.A. Khalil and C.L. Wey, IEEE Trans. on Computers and Digital Techniques, Nov 1999, p.295
R. Zimmermann, "Computer Arithmetic: Principles, Architectures, and VLSI Design," Lecture notes, Integrated Systems Laboratory, ETH Zurich, 1997.
LevelIndex (LI) and Symmetric LevelIndex (SLI) Number Systems
This is also called the antitetrational number system.
LI and SLI use the same presentation for numbers greater than or equal to 1: A realnumber base is chosen (usually e=2.71828... but sometimes 2, or could be any other base). A number x is represented as a triple (s,l,i). s is a sign (0 or 1), l is the level and is a positive integer, i is the index and is in the range [0,1). l and i are related to x via a "generalized exponential function" F_{ge} in the following way:
x = 1^{s} F_{ge}(l,i)
F_{ge}(l,i) = e^{i}, if i=0
F_{ge}(l,i) = e^{Fge(l1,i)}, if i>0
To represent numbers in the range (1,1), the LI system uses l=0 and i=x. To represent numbers in the range [1,e), it uses l=1, and i=ln(x) where i is in the range [0,1). l=2 is used for values from e through e^{e}, and so on. Here are a few examples:

Any real number can be turned into a unique combination of sign s, integer portion l and fraction i and used to represent a (usually larger) quantity via this definition. This could be used as a mapping from reals onto reals, that "compresses" a large range of values into a fairly small range of function arguments. This function is shaped sort of like the hyperbolic sine sinh(x) but with a straight line section in the area from 1 to 1. It has several nice properties, including that it is continuous, differentiable, and monotonic (everywhere increasing).
The differentiable property makes LevelIndex particularly good for a computer number format, because it means that the roundoff error does not suddenly change at certain values. Normal floatingpoint numbers are not as wellbehaved: the maximum (and mean expected) magnitude of roundoff errors doubles at every power of 2.
Symmetric LevelIndex
The original purpose of LevelIndex was to avoid roundoff and overflow errors in calculations. One common cause of roundoff error comes from calculating a very very small quantity, such as 10^{1000}. This can happen as an intermediate result in calculations whose correct final answer depends on not rounding this value to 0.
The symmetric levelindex system (SLI) was created to address this. In the SLI system, a reciprocal bit r is added. As in levelindex, there is a sign bit s and a level l. If r=0 we have the same interpretation as in LI: the value is 1^{s}F_{ge}(l,i). If r=1, the quantity being represented is 1^{s}/F_{ge}(l,i).
Once you have the reciprocal bit, values from 0 to 1 can be handled by setting the bit with a value above 1. For example, 0.5 and 2.0 are the same except that 0.5 has the reciprocal bit set. This means that a level l=0 no longer happens. In order not to "waste" the value l=0, the level is just decreased by 1.
Here are some examples of numbers in the SLI system listed in ascending numerical order. These are all 32bit word representations, and the level l is a 3bit quantity that can range from 0 to 7. This leaves 27 bits for the i (index) field, which is here shown in hexadecimal. In the rightmost column, extra digits are used to show the size of the difference between adjacent values. You can clearly see how there is more precision for small numbers (like 5) than for big numbers (like 5×10^{100}). I have not seen any descriptions of the handling of special numbers like infinity or negative 0, so I had to guess.

Papers about LI and SLI: [2], [3], [4], [5], [6], [7], [8], [9], [10]
Lexicographic Strings
The following systems all share the properties:
 Each number can be represented as a string of symbols drawn from some fixed alphabet,
 The strings can be arranged in order like words in a dictionary ("lexicographic ordering") according to the order of its particular alphabet,
 When so arranged, given any two numbers A and B, the string representing A comes before the string representing B if and only if A<B.
The Ubiquitous Practice of Communicating the Biggest Part First
In Ifrah's 1999 book The Universal History of Numbers [12] there are dozens of examples of written and spoken number systems, covering 6000 years of history and including indigenous cultures from five continents. In 37 cases there are illustrations of specific large numbers made of parts like "one hundred and fortythree". All but three present the largest part of a number first, and two of those three (Turkish 8^{th} century and Inca 12^{th} century) are unclear. In this table, the location in [12] is given as a page number with "a" or "b" to indicate which column contains the illustration/example, and B or S indicates whether the bigger or smaller part of the number is written first:
page and column  culture and date  B/S  page and column  culture and date  B/S  
26a  English present  B  238b  Jewish 3c CE  B  
26b  Tibetan present  B  244b  Arabia 10c CE  B  
27a  Mongolian present  B  247b  Ethiopia 4c CE  B  
27b  Turkey 8c CE  ?  264a  China 15c CE  B  
29a  Sanskrit 5c BCE  S  267a  China 1c CE  B  
68b  Inca 12c CE  ?  270b  China 12c BCE  B  
88a  Sumeria 4m BCE  B  277a  China, Japan current  B  
118a  ProtoElamite 3m BCE  B  309b  Mayan 7c CE  B  
138b  SumeroAkkadian 17c BCE  B  319a  Mayan 4c CE  B  
147b  Babylonia 19c BCE  B  326b  Hittite 14c BCE  B  
152b  Uruk Seleucid 2c BCE  B  327a  Aztec 12c CE  B  
166b  Egypt 26c BCE  B  329b  Armenia 5c CE  B  
171b  Hieratic 3m BCE  B  331a  Egypt 2c BCE  B  
180a  Crete 16c BCE  B  332a  Aramaea 8c BCE  B  
182b  Greece 5c BCE  B  332a  Sighalese 7c CE  B  
186b  Sheba 2c BCE  B  334b  Tamil 7c CE  B  
189b  Rome 3c BCE  B  335a  Malay 7c CE  B  
190a  Etruscan 4c BCE  B  336a  Mari 19c BCE  B  
221b  Greece 3c BCE  B 
To a modern observer it is easy to see why so many cultures chose to put the biggest part of a number first: bigger is usually more important, and in speech one usually benefits from delivering the most important part of a communication upfront.
Following this "most important first" principle, a lexicographic string representation makes it easy to distinguish large numbers from small just by looking at the beginning of the text representing each number.
To illustrate how our written numerals fail to meet this ideal, consider the numbers 1111 and 999. The first is larger, but you cannot tell just by looking at the first few digits. It is only after examining all of the digits of both numbers that you can see which is larger.
Lexicographic string representations have two properties: they are textstring representation systems, and they are lexicographic.
In a textstring representation system, any number N has a text representation consisting of a sequence of characters from some type of "alphabet". For example, we normally use an alphabet of ten symbols, the digits 0 through 9.
Lexicographic is defined in the following way: For any two numbers A and B, consider their written forms w(A) and w(B). In a lexicographic system, the following rules always hold true:
If the first character of the w(A) comes after the first character of w(B), then A is larger than B.
If the first character of the w(A) comes before the first character of w(B), then A is smaller than B.
Furthermore, if the first characters of w(A) and w(B). are the same, then the size of A relative to B can be determined by looking at the second character. In general:
If A is greater than B, then the text representing A comes after the text representing B, using a standard dictionarytype ordering.
Knuth's Systems
Three systems described by Knuth [1] use an alphabet of just two symbols: the binary digits 0 and 1. All comprise encodings of the nonnegative integers.
The first and simplest represents each N with by a string of N 1's followed by a single 0:
0 = 0
1 = 10
2 = 110
3 = 1110
4 = 11110
5 = 111110
. . .
Interpreted as binary numbers these strings are the terms of Sloane's sequence A000918(N+1).
The second Knuth system is based on the ordinary binary representation of N, but replaces the initial 1 with the the string from the above system, representing the value of length(N)1 where "length(N)" represents the number of binary digits in the ordinary representation of N. So, for example the number 5 has the binary representation 101, and the length of this is 3 and length1 is 2. So Knuth takes the initial 1 and replaces it with the representation of 2 above: 110 followed by 01, making the 5character string 11001. Here is an illustration of this system:
0 = 00
1 = 01
2 = 100
3 = 101
4 = 11000
5 = 11001
6 = 11010
7 = 11011
8 = 1110000
9 = 1110001
10 = 1110010
11 = 1110011
. . .
15 = 1110111
16 = 111100000
17 = 111100001
. . .
31 = 111101111
32 = 11111000000
. . .
Interpreted as binary numbers these strings are the terms of Sloane's sequence A171885(N).
In each string, the length of the initial 111111... portion is the same as the length of the 110101... portion. At each power of 2, both parts increase in length by 1 symbol. That means that on average, this system uses about twice as many binary digits as a normal binary number, but it does it without needing to know in advance how many binary digits you're going to use.
The third Knuth system takes this one step further: the initial "11110..." string is replaced with a more compact representation expressing how many 1's appear in that string, and (the really clever bit) uses its own representation for that encoding. This never results in problems because each needed value is smaller than the value we're trying to define, so as long as we start with 0=0 the rest of the system lifts itself up by its bootstraps, so to speak:
N  structure  string 
0  (0)  0 
1  (1 0) = (1 0)  10 
2  (1 1 0) = (1 10 0)  1100 
3  (1 1 1) = (1 10 1)  1101 
4  (1 2 00) = (1 1100 00)  1110000 
5  (1 2 01) = (1 1100 01)  1110001 
6  (1 2 10) = (1 1100 10)  1110010 
7  (1 2 11) = (1 1100 11)  1110011 
8  (1 3 000) = (1 1101 000)  11101000 
9  (1 3 001) = (1 1101 001)  11101001 
10  (1 3 010) = (1 1101 010)  11101010 
11  (1 3 011) = (1 1101 011)  11101011 
12  (1 3 100) = (1 1101 100)  11101100 
13  (1 3 101) = (1 1101 101)  11101101 
14  (1 3 110) = (1 1101 110)  11101110 
15  (1 3 111) = (1 1101 111)  11101111 
16  (1 4 0000) = (1 1110000 0000)  111100000000 
17  (1 4 0001) = (1 1110000 0001)  111100000001 
...  . . .  . . . 
31  (1 4 1111) = (1 1110000 1111)  111100001111 
32  (1 5 00000) = (1 1110001 00000)  1111000100000 
...  . . .  . . . 
255  (1 7 1111111) = (1 1110011 1111111)  111100111111111 
256  (1 8 00000000) = (1 11101000 00000000)  11110100000000000 
...  . . .  . . . 
511  (1 8 11111111) = (1 11101000 11111111)  11110100011111111 
512  (1 9 000000000) = (1 11101001 000000000)  111101001000000000 
...  . . .  . . . 
65535  (1 15 111111111111111) = (1 11101111 111111111111111)  111101111111111111111111 
65536  (1 16 0000000000000000) = (1 111100000000 0000000000000000)  11111000000000000000000000000 
...  . . .  . . . 
Interpreted as binary numbers these strings are the terms of Sloane's sequence A010097(N). According to that entry, these strings are also known as "prefix" or Levenshtein codes.
Munafo Alphabetic PT System
I invented this system around the time I began my numbers webpage, to have a consistent way to name the labels for crossreferences in the RHTF. The alphabet for this system has seventeen symbols: letters a through e and p, digits 0 through 9, and underscore _.
It represents nonnegative values of the form A/10^{N} where A and N are both nonegaive integers. In other words, anything that can be expressed with a finite number of digits and no signs or symbols except perhaps a single decimal point.
The basic approach is to divide numbers into a balanced set of "ranges" that each start with a different letter, and make sure that the numbers within each range are in order.
The initial design for the system had seven ranges, distinguished by the first character of the string representation, as follows:
09 : numbers less than 10.0
a : numbers from 10.0 to 99.999...
b : numbers from 100.0 to 999.999...
c : numbers from 1000.0 to 9999.999...
d : numbers from 10000.0 to 99999.999...
e : numbers from 10^{5} to 10^{300}
p : numbers from 10^{300} to 10^{10300}
Later I modified it to extend the p range to include everything from 10^{300} on up.
For the first range (numbers less than 10), the string has one digit, an optional _ symbol representing the decimal point, and (optionally) more digits representing the fractional part.
For the next four ranges, the letter tells you how many digits to expect before the optional _ character. The string for 27 is a27; the string for 123.45 is b123_45, and so on.
I could have defined more letters beyond a, b, c and d this way, but I felt it was becoming difficult to keep track of how many digits are indicated by each letter, and the letter e has a familiar meaning related to scientific notation.
So the next range, using strings starting with e, is for numbers that are expressed in "exponential notation" on a calculator. The e is followed by a threedigit exponent representing a power of 10, then after that are the first one (or several) digits of the number. For example, 6.02×10^{23} is represented by the string e023_602. A decimal point is not given with a _ character because it must always be after the first digit of the number (in this case, right after the 6). However, I do include a _ character right after the 3digit exponent to make it clear where the exponent ends. If the number is an exact power of ten, I still have a digit "1", so googol is e100_1, not simply "e100".
Above 10^{300}, a letter p is added to the beginning to indicate that we start with "ten to the power of..." (the p stands for "power"). Immediately after this p should be a string representing what 10 is raised to the power of. So googolplex 10^{10100} could be represented by the string "pe100_1".
To go higher than 10^{10300} I considered adding more p's to the beginning of the string to represent extra 10's, but I needed to discuss powertowers high enough to make that impracticl for use as labels. I wanted a system that could go a further on a single p, and then add a second p to extend the range much more. (The numbers I wanted to represent are discussed on my large numbers page starting around the hyper5 section.)
Therefore, instead of just using n consecutive p's to make a powertower of height n+1 or n+2, I made the rule for the p group require following the p immediately with the encoding of how many powers of 10 are added at the beginning; then an underscore _. This means that we start with p1_ instead of just "p", then p2_ (instead of "pp") to make the powertower one step higher, then p3_, and so on. Thus, a string that starts with p has symbols representing two numbers — the height of the tower and the "thing at the top".
So to make the string for googolplex we put "p1_" in front of the string for googol (which is e100_1 as above); the whole string is p1_e100_1. 10 to the power of googolplex, 10^{1010100}, is p2_e100_1, and 10 to the power of that is p3_e100_1, and so on.
That first number after the p can any string in this format: "a22" means 22, so pa22e033184 is a tower with twentytwo 10's and 1.84×10^{33} on top. Think of it as "p(a22)_e033_184".
We get to the numbers requiring two p's when the height of the tower is 10^{300} or more. For example, given that 10↑↑10 is represented by p8_e010_1, we can represent 10↑↑↑3 = 10↑↑(10↑↑10) concisely with the string pp8_e010_1_1. This should be thought of as "p(p8_e10_1)_1", to indicate a powertower of 10's of height p8_e010_1, with a 1 on top. This effectively means that the result of a hyper5 or "pentation" operation a↑↑↑b will start with about b copies of p. In such cases it is likely to end with a lot of ..._1_1_1. I decided that extra duplicates of "_1" at the end can be left off.
Until recently I didn't need any RHTF labels with multiple p's at the beginning, as my numbers page did not go beyond 2↑↑1000. There is now a single doublep entry, thanks to
Patashu
's break_eternity.js library, which has a computational limit of 10↑↑(1.8×10^{308}).
Here is an extensive list of examples to show how the system handles these cases. For convenience, scientific notation is used in the explanations here, so for example I use "6.02×10^{23}" for the integer value 602000000000000000000000.

The system continues in this way, adding one p for each repetition of "... where X is a power tower of 10's of height Y, ...". When the number of p's becomes unwieldy, the numbers are hard to work with in any system because the numbers I wanted to discuss were not equal to two numbers with a single "hyper" operator. In general it will eventually occur that definitive ordering is nonobvious, so I suppose any mechanical lexicographic system will fail to reach as far as the fastgrowing functions used to define the largest numbers.
OrderPreserving Verbal Names
As mentioned above using the examples from [12], in the vast majority of cultures, the largest part of a number is said or written first. For example, consider 2^{20}=1048576. In standard English, this number is said "one million, fortyeight thousand, five hundred and seventysix". Saying the largest part first has a benefit that is shared with the lexicographic string systems: the listener gets the most important part of the communication upfront. In describing his bitstring systems, Knuth calls this attribute orderpreserving.
However this custom breaks down in speech when we communicate large numbers using scientific notation. When someone says "6.02×10^{23}" in words, they say "six point zero two times ten to the power of twentythree". Listening to these words, it initially sounds as though they are describing a number close to "six". The listener has no idea how large the number might be until the very end.

To address this, the speaker can reverse the two parts of 6.02×10^{23}, as if written "10^{23}×6.02". When spoken, this becomes "ten to the power of twentythree, times six point zero two" with the comma naturally serving the same function as it does after "one million" in my previous example:

In general, the "inverted scientific notation grammar" is:
ten to the power of some number times some number
This works pretty well for a while, until we get to numbers like 10^{10100}. This would normally be said "ten to the power of ten to the power of one hundred". Once we begin to encounter such "power towers" that contain two or more numbers, a new problem arises: the listener won't have a clue how big the number is until discovering how many times the speaker is going to repeat the words "ten to the power of...". Here is the old value of the larger Skewes' number:

Once again we can address the problem by giving the most important bit of information upfront. This time, the most important bit of information is the number of 10's in the exponent stack before the final number at the top, which is then spoken using the existing convention for normal of "scientific notation" numbers. So, for example, to express 10^{101034}, we can use the following (somewhat awkward) construction: "A power tower of three tens, with thirtyfour at the top".

For reasons that become clear when working with such numbers, the "number at the top" can be pretty nearly anything that would normally be expressed in scientific notation (see the latter parts of my numbers pages for plenty of examples). Therefore we generalize the grammar:
a powertower of some number tens, with some number at the top
where in this case "some number" can be anything from the "inverted scientific notation grammar" above.
Let us devise a way to describe the value of 10^{101010101.1}, which comes up in a discussion by Don Page of the quantum Poincare recurrence time of a hypothetical huge universe, and works out to 10^{101.51×103883775501690} or 10^{10103.88×1012}. The powertower is five numbers high, and the first three are tens. The part at the top is 3.88×10^{12}, but as previously discussed, this would be better if rearranged to 10^{12}×3.88. So the full name works out to:
A powertower of three tens, with ten to the power of twelve times three point eight eight at the top.
Now I'll present some more examples in increasing numerical order. For these examples I imagine that the following abbreviations have been adopted through gradual evolution and speaker laziness: tenpow for "ten to the power of"; powtow for "a powertower of" (rhymes with kowtow); with for "tens, with"^{2}; and omitting the final words "at the top".

After this point, the repeated "powtow" again causes a problem — the reader may wish to speculate on how to extend the grammar further.
However, if we stick with just one repetition of "power tower", we have a grammar that is orderpreserving, that communicates the "most important bits" about a number's size right from the start, preserves the standard wording for the most common sizes of numbers, and also allows the communication of almost incomprehensibly large values on the high end.
Alternative Algorithms for Standard Formats
For now, this section is just a bibliography listing.
Exception Handling
John R. Hauser, "Handling FloatingPoint Exceptions in Numeric Programs", ACM trans. on Prog. Lang. and Systems, v 18 no 2 (Mar 1996), 139174
CORDIC algorithms
C. W. Schelin, "Calculator function approximation", Amer. Math. Monthly 90 (1983), 317325.
Neil Eklund, "CORDIC: Elementary Function Computation Using Recursive Sequences"
"A Survey of CORDIC Algorithms for FPGA Based Computers", Ray Andraka, FPGA '98. Proceedings of the 1998 ACM/SIGDA sixth international symposium on Field programmable gate arrays, Feb. 2224, 1998, Monterey, CA. pp. 191200 http://www.andraka.com/files/crdcsrvy.pdf
Composite and Hybrid Systems
"Towards Exact Geometric Computation" (To Appear, Computational Geometry: Theory and Applications) CheeKeng Yap, 1994
"Floating Point and Composite Arithmetics", W.N.Holmes, Proceedings of the Eighth Biennial Computational Techniques and Applications Conference, Adelaide, South Australia, Sep 20  Oct 1, 1997
Neville Holmes, "Composite Arithmetic: Proposal for a New Standard." IEEE Computer 30(3): 6573 (1997) Not online, but see: HolmesArith.pdf, HolmesDisp.pdf and HolmesStor.pdf
Unevaluated Sum of Multiple Native FloatingPoint Values
This format is a simple way to get higher precision, without higher range, from another existing format. It relies on certain properties of rounding that are followed by most hardware floating point formats including IEEE 754. See my page on f161 "tripledouble" precision for more details.
Dekker, T. (1971) A floatingpoint technique for extending the available precision. In Numerische Mathematik 18, 224242.
Wyatt, W.T., et. al. (1976) A portable extended precision arithmetic package and library with Fortran precompiler. ACM Trans. Math. Softw. 2(3), 209231.
Brent, R. (1978) A Fortran multipleprecision arithmetic package. ACM Trans. Math. Softw. 4(1), 5770.
Briggs, K. (1998) Doubledouble floating point arithmetic. http://keithbriggs.info/software.html
Li, X., et. al. (2000). Design, implementation and testing of extended and mixed precision BLAS. Lawrence Berkeley National Laboratory. Technical Report LBNL45991
Hida, Y., et. al. (2001) Quaddouble arithmetic: algorithms, implementation, and application. Lawrence Berkeley National Laboratory, Technical Report LBNL46996.
Priest (1991) Algorithms for Arbitrary Preciion Floating Point Arithmetic. Proc. 10th Symposium on Computer Arithmetic, IEEE Computer Society Press, Caif., 1991.
Priest (1992) On Properties of Floating Point Arithmetics: Numerical Stability and the Cost of Accurate Computations.
Shewchuk (1997) Adaptive Precision FloatingPoint Arithmetic and Fast Robust Geometric Predicates. Discrete and Computational Geometry 18(3), 305363.
Arbitrary Precision
R.P. Brent, "Fast multipleprecision evaluation of elementary functions", Journal of the ACM, 23(2), 1976, pp. 242251.
General and Survey papers
"Computer Arithmetic: Principles, Architectures, and VLSI Design (1999)", Reto Zimmermann, Lecture notes March 16, 1999
Incremental Games
Many online (browserbased) games of the "idle" or "incremental" variety require very large numbers, and for these a variety of special representation formats has been invented. These include simple modifications such as storing a number as its logarithm (extending the overflow limit to 10^{21024}), the use of a levelindex system for repeated exponentiation, and more elaborate multilevelindex systems to handle repeated use of the tetration or "hyper4" function a↑↑b and higher operators with more arrows. The ExpantaNum.js library by Naruyoko can handle anything up to Graham's number
As an example, I discuss the Omagenum.js library in more detail, in the online game section of my large numbers notes.
Bibliography
[1] Donald E. Knuth, Supernatural numbers. Appears on pages 310325 in The Mathematical Gardner, ed. David A. Klarner (1981).
[2] Clenshaw, C. W. and Olver, F. W. J.: "Beyond floating point." J. Assoc. Comput. Mach. 31 (1984), 319328.
[3] C. W. Clenshaw, D. W. Lozier, F. W. J. Olver, and P. R. Turner, "Generalized exponential and logarithmic functions", Comput. Math. Appl. 12B (1986), 10911101.
[4] C.W. Clenshaw, F.W.J. Olver, and P.R. Turner. "Levelindex arithmetic: an introductory survey." In P.R. Turner, editor, Numerical Analysis and Parallel Processing, pages 95168. SpringerVerlag, 1987. Lecture Notes in Mathematics, No.1397.
[5] Clenshaw, C. W. and Olver, F. W. J. 1987. "Levelindex arithmetic operations." SIAM J. Num. Anal. 24, 2 (Apr.), 470485.
[6] C. W. Clenshaw and P. R. Turner, "The symmetric levelindex system", IMA J. Numer. Anal. 8 (1988), 517526.
[7] Peter R. Turner. "A software implementation of SLI arithmetic." In Proc. 9th Symp. on Computer Arithmetic, pp. 1824. IEEE Computer Society Press, 1989.
[8] D.W.Lozier and F.W.J.Olver, "Closure and precision in levelindex arithmetic", SIAM J. Numer. Anal. 27 (1990), 12951304.
*In this paper he shows that arithmetic using the four standard operators is closed in a levelindex system with level <= 6 and mantissa <= 5,500,000 bits.*
[9] D.W. Lozier and P.R. Turner, "ErrorBounding in LevelIndex Computer Arithmetic", in G. Alefeld and J. Herzberger, eds., Numerical Methods and Error Bounds, Akademie Verlag, Berlin, 1996, pp. 138145.
[10] Michael A. Anuta, Daniel W. Lozier, Nicolas Schabanel, And Peter R. Turner, "Basic Linear Algebra Operations In Sli Arithmetic", EuroPar '96, LNCS 1124, Vol. II, SpringerVerlag (1996), pp. 193202.
[11] Michael A. Anuta, Daniel W. Lozier, and Peter R. Turner, "The MasPar MP1 As a Computer Arithmetic Laboratory" J. Res. Nat. Inst. Standards and Technology 101,2 (1996) pp. 165174. http://nvl.nist.gov/pub/nistpubs/jres/101/2/j2anut.pdf
Very good survey, includes several other number systems as well
[12] Georges Ifrah, The Universal History of Numbers, ISBN 0471375683. (1999). (A concise summary of many ancient systems, all with dates and illustrated examples appears in chapter 23, "The Final Stage of Numerical Notation".)
Footnotes
1 : George Spelvin, email correspondence.
2 : Due to the power tower paradox, the number of items in a power tower matters far more than their exact values, so tens is unnecessarily specific. For the purposes of this naming system, as well as for nearly all computational purposes, a power tower of three 10's with googol at the top is equivalent to a power tower of three 2's with googol at the top, or three 1000's with googol at the top:
2^{22googol} ≈ 10^{1010googol} ≈ 1000^{10001000googol}
See also the uncomparably larger discussion on my large numbers page.
This page was written in the "embarrassingly readable" markup language RHTF, and was last updated on 2022 Jul 25. s.27