Top person sorted by score

At this site we keep several lists of primes, most notably the list of the 5,000 largest known primes. Who found the most of these record primes? We keep separate counts for persons, projects and programs. To see these lists click on 'number' to the right.

Clearly one 100,000,000 digit prime is much harder to discover than quite a few 100,000 digit primes. Based on the usual estimates we score the top persons, provers and projects by adding ‎(log n)³ log log n‎ for each of their primes n. Click on 'score' to see these lists.

Finally, to make sense of the score values, we normalize them by dividing by the current score of the 5000th prime. See these by clicking on 'normalized score' in the table on the right.

rank person primes score 101 Anthony Templin 8 50.1628 102 Jan Kožíšek 21 50.1476 103 Hiroki Saito 12 50.1330 104 Greg Miller 15 50.0891 105 Ian Johns 26 50.0818 106 Bill Cavnaugh 18 50.0753 107 Hans Joachim Böhm 23 50.0514 108 Takahiko Ogawa 15 50.0012 109 Andrew M Farrow 4 49.9997 110 James Winskill 3 49.9926 111 Masashi Kumagai 1 49.9772 112 Michael Curtis 15 49.9695 113 Tim Terry 14 49.9596 114 Ricky L Hubbard 15 49.9380 115 Tim McArdle 1 49.9091 116 Peyton Hayslette 1 49.8982 117 Jonathan Seeley 15 49.8958 118 Marshall Bishop 12 49.8882 119 Michael Goetz 7 49.8819 120 Bob Calvin 15 49.8725

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Notes:

Score for Primes

To find the score for a person, program or project's primes, we give each prime n the score (log n)³ log log n; and then find the sum of the scores of their primes. For persons (and for projects), if three go together to find the prime, each gets one-third of the score. Finally we take the log of the resulting sum to narrow the range of the resulting scores. (Throughout this page log is the natural logarithm.)

How did we settle on (log n)³ log log n? For most of the primes on the list the primality testing algorithms take roughly O(log(n)) steps where the steps each take a set number of multiplications. FFT multiplications take about

O( log n ^. log log n ^. log log log n )

operations. However, for practical purposes the O(log log log n) is a constant for this range number (it is the precision of numbers used during the FFT, 64 bits suffices for numbers under about 2,000,000 digits).

Next, by the prime number theorem, the number of integers we must test before finding a prime the size of n is O(log n) (only the constant is effected by prescreening using trial division).  So to get a rough estimate of the amount of time to find a prime the size of n, we just multiply these together and we get

O( (log n)³ log log n ).

Finally, for convenience when we add these scores, we take the log of the result.  This is because log n is roughly 2.3 times the number of digits in the prime n, so (log n)³ is quite large for many of the primes on the list. (The number of decimal digits in n is floor((log n)/(log 10)+1)).