I have shared with this forum two algorithms very dear to me: GC60_M30x8 and GC60_LRLN_x8.
These two algorithms have been combined, and the result highlights something fundamental: a different way of looking at how individual numbers move on the number line is far more powerful than just software-level optimization on a segmented sieve.
Julia has outdone itself with this combination. Thanks to its high-level nature, the program almost doubles the performance of primesieve, which is written in low-level, super-optimized C++.
I hope you enjoy this work, because I am truly proud of it.
You can find the script, benchmarks, and verification tests in my Codeberg repository:
By the way, for a small algorithm like this, vendoring is a common use case. Would you perhaps consider MIT-0? It makes reuse simpler by removing MIT’s notice-retention requirement.
Thank you for the excellent suggestion! I have already updated the license from MIT to MIT-0 to make the algorithm as accessible and easy to integrate as possible for anyone who wishes to implement, improve, or integrate it into their own projects
Grazie per la risposta e la tua segnalazione su eachprime.
In realtà non sono propenso a lavorare su un unico algoritmo o a inseguire benchmark competitivi. Il mio lavoro di ricercatore indipendente mi porta a studiare i movimenti dei numeri all’interno della scala numerica, che poi cerco di tradurre in modelli logici.
Avendo modificato la licenza in MIT-0, lascio volentieri a voi e alla community il compito di testarlo, confrontarlo o migliorarlo a seconda delle vostre esigenze e capacitĂ di programmazione.