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 121 Todd Pickering 5 49.6774 122 Daniel Greeley 17 49.6735 123 Takashi Iwasaki 15 49.6365 124 Greg Miller 8 49.6300 125 Marshall Bishop 8 49.5929 126 Ivy F. Tennant 17 49.5862 127 Per Provencher 7 49.5637 128 Jochen Beck 11 49.5585 129 Leon Bird 3 49.5565 130 Bruce E. Slade 15 49.5249 131 Grzegorz Granowski 15 49.5185 132 Philipp Bliedung 3 49.4832 133 Andrew Dunchouk 8 49.4785 134 Ruben Steinberg 12 49.4598 135 Gregory Coscia 8 49.4591 136 Igor Karpenko 1 49.4585 137 Itsuki Kadowaki 4 49.4361 138 Jordan Romaidis 6 49.4237 139 Barry Walsh 11 49.4132 140 Gary Barnes 33 49.4117

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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)).