# Parallel Prime Sieving at High Magnitude with Julia (Project GC-60)

**URL:** <https://discourse.julialang.org/t/parallel-prime-sieving-at-high-magnitude-with-julia-project-gc-60/138200>\
**Category:** Performance\
**Created:** [July 14, 2026, 10:17pm UTC](https://discourse.julialang.org/t/parallel-prime-sieving-at-high-magnitude-with-julia-project-gc-60/138200 "2026-07-14T22:17:03Z")\
**Posts on this page:** 5\
**Page:** 1

<div class="post-metadata">

**Author:** ![Claugo](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/claugo/32/222296_2.png) [@Claugo](https://discourse.julialang.org/u/Claugo)\
**Post date:** [July 14, 2026, 10:17pm UTC](https://discourse.julialang.org/t/parallel-prime-sieving-at-high-magnitude-with-julia-project-gc-60/138200/1 "2026-07-14T22:17:03Z")

</div>

Hello everyone,

**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:

> **[2-GC60\_LRLN\_x8](https://codeberg.org/claugo/2-GC60_LRLN_x8)**
>
> GC60\_LRLN (Local Residue Lattice Navigation) (GC60\_M30x8 Algoritmo di Sieve Segmentato a Finestra)

Thank you all!

---

<div class="post-metadata">

**Author:** ![Oscar\_Smith](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/oscar_smith/32/25343_2.png) [@Oscar\_Smith](https://discourse.julialang.org/u/Oscar_Smith)\
**Post date:** [July 15, 2026, 12:57am UTC](https://discourse.julialang.org/t/parallel-prime-sieving-at-high-magnitude-with-julia-project-gc-60/138200/2 "2026-07-15T00:57:29Z")

</div>

How does this perform relative to Primes.jl eachprime (new in a not yet released version but if you `]add Primes#main` you should get it).

---

<div class="post-metadata">

**Author:** ![karei](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/karei/32/214809_2.png) [@karei](https://discourse.julialang.org/u/karei)\
**Post date:** [July 15, 2026, 1:37am UTC](https://discourse.julialang.org/t/parallel-prime-sieving-at-high-magnitude-with-julia-project-gc-60/138200/3 "2026-07-15T01:37:03Z")

</div>

Wow, codeberg.

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.

---

<div class="post-metadata">

**Author:** ![Claugo](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/claugo/32/222296_2.png) [@Claugo](https://discourse.julialang.org/u/Claugo)\
**Post date:** [July 15, 2026, 8:12am UTC](https://discourse.julialang.org/t/parallel-prime-sieving-at-high-magnitude-with-julia-project-gc-60/138200/4 "2026-07-15T08:12:33Z")

</div>

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

---

<div class="post-metadata">

**Author:** ![Claugo](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/claugo/32/222296_2.png) [@Claugo](https://discourse.julialang.org/u/Claugo)\
**Post date:** [July 15, 2026, 8:38am UTC](https://discourse.julialang.org/t/parallel-prime-sieving-at-high-magnitude-with-julia-project-gc-60/138200/5 "2026-07-15T08:38:10Z")

</div>

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 capacità di programmazione.