[Minecraft@Home] Xoroshiro128++ Guessing Order Optimization Project

You may have noticed some new workunits hit the queue. As of the time of writing, the workunits that are available should be considered "beta" as we're working out a few kinks.
That said, this is a new CPU app! As time goes on, we'll be replacing configuration files for it to further refine the results, so work will be sent out in stages and we'll feel out how much workunits make sense for each stage.

I've included a fairly technical explanation from the primary author of the app, MC, longtime contributor to Minecraft@Home:

The Xoroshiro Guessing Order project aims to uncover exploitable statistical weaknesses in the xoroshiro128++ pseudorandom number generator (PRNG). Recent versions of Minecraft now use xoroshiro128++ for specific aspects of world generation, replacing Java's default PRNG. Unfortunately, this shift creates significant challenges for seedcracking projects, as no efficient algorithm currently exists to reconstruct the internal state of xoroshiro from its outputs.

Our primary goal is to develop a reliable method for recovering the PRNG's 128-bit seed from just two sequential 64-bit outputs. Currently, the most efficient known method (excluding SAT solvers) involves guessing 54 bits of the state, after which the remaining bits can be derived, a process requiring roughly 9 quadrillion attempts on average. Our aim is to significantly reduce this computational requirement.

Xoroshiro128++ belongs to the "xorshift" family of PRNGs, which means its core operations are linear. The internal state can therefore be represented as a binary vector within a 128-dimensional vector space, with state transitions represented as matrix multiplications. However, the primary challenge arises from its use of a non-linear "scrambler" function, which maps the internal state to its output. Due to this scrambler, there is no straightforward one-to-one relationship between the 128-dimensional internal state and the 128-bit output.

Fortunately, the scrambler function is imperfect. Most of the information provided by each output bit is concentrated in a relatively small subset, approximately a dozen dimensions, of the total 128-dimensional state. Even with a partial, lower-dimensional guess of the state, it is frequently possible to determine whether the guess is incorrect based on the known output. If we can identify low-dimensional subsets of the state space that are significantly constrained by the outputs, we can efficiently guess and verify these smaller portions first, rapidly eliminating incorrect possibilities.

Participants in our project analyze these lower-dimensional subsets of the state space, identifying those subsets most constrained by observed outputs. To facilitate this process, we use a large precomputed table that estimates the amount of information (in bits) xoroshiro's output data provides about each subset of state dimensions.

By ultimately identifying an optimal sequence of highly constrained subspaces, we can incrementally guess the state one dimension at a time, discarding incorrect possibilities early on. Achieving this optimal guessing order would produce a highly efficient algorithm capable of recovering the PRNG's internal state within a small number of attempts, significantly advancing future efforts in Minecraft seedcracking and related research.

View article · Sat, 29 Mar 2025 05:56:02 +0000

[Minecraft@Home] Loneliest Seed v1.08

Hi folks!

I know it's been a bit, life gets in the way sometimes. However, I've released v1.08 of the Loneliest Seed project which aims to address the checkpointing issue.

We're experimenting with a CPU project separate from this as well, so stay tuned for that! If the experiments go well on our end we're anticipating launching a CPU app that will aid us in our years-long research into finding methods of reversing PRNG states in the Xoroshiro128++ algorithm.

Best regards,
Minecraft@Home Team

View article · Wed, 26 Mar 2025 15:09:17 +0000

[DENIS@Home] We are back! // ¡Estamos de vuelta!

Dear volunteers,
We are back! The wait has been longer than we would have liked, but surprisingly, what we bring comes sooner than we expected. Ivan Royo has been working this term on the 1D version of DENIS (denis-fiber) and we already have a first version to start testing. It is a fairly advanced version of what will be the final one (still without checkpoints), but at this point it is very useful to start testing to see that everything is going well and to polish the last details.

Thanks to a grant from the Ministry, Iván is studying how cellular differences (interpatient variations) affect the propagation of the electrical impulse in the heart, and for this we need to scale up. If the tests go well, we will start running simulations within this new project. For now, they are all functional tests. We will start with manageable simulation sizes and gradually increase the number based on how the server responds and our post-processing capacity.

The optimization of the models is still pending as well, I hope we can resume it soon.

Best regards,
Jesús.

================================================================================

Estimados voluntarios:
¡Estamos de vuelta! La espera ha sido más larga de lo que habríamos querido, pero sorprendentemente, lo que traemos viene antes de lo que esperábamos. Ivan Royo ha estado este curso trabajando en la versión 1D de DENIS (denis-fiber) y ya tenemos una primera versión para empezar a hacer pruebas. Es una versión bastante avanzada de lo que será la final (aún sin chekpoints), pero en este punto nos viene muy bien poder empezar a hacer pruebas para ver que todo marcha bien e ir puliendo los últimos detalles.

Gracias a una beca del Ministerio, Iván está estudiando cómo afectan las diferencias celulares (variaciones interpaciente) en la propagación del impulso eléctrico en el corazón, y para ello necesitamos subir de escala. Si las pruebas van bien, comenzaremos a lanzar simulaciones dentro de este nuevo proyecto. De momento son todo pruebas de funcionamiento. Vamos a empezar con tamaños de simulaciones manejables e ir aumentando el número para cómo va respondiendo el servidor y nuestras capacidad de postprocesado.

La parte de la optimización de los modelos está aún pendiente también, espero que podamos retomarla en breve.

Un saludo,
Jesús.

View article · Wed, 26 Mar 2025 09:09:41 +0000

Learn
Science projects
Science publications
User manual
Add-ons
Web resources

Communicate
Message boards
Help
Email lists
Report bugs

Help
Translate
Test
Publicize

Computing power · Certificate · Poll

• Create and maintain a BOINC project.
  
• Run Autodock Vina jobs with BOINC Central and Raccoon2

Programmers:

• Visit the BOINC github repo

Lines of code visualization
Vitalii made a visualization of the number of lines of code in BOINC going back to 2002.
16 Mar 2025, 9:07:20 UTC · Discuss

Grafana project dashboards
Check out Grafana project dashboards showing time-varying graphs of project info such as number of unsent and in-progress jobs.
15 Feb 2025, 21:03:13 UTC · Discuss

Contributor history video
Vitalii made a video showing the top 25 BOINC committers, based on CVS, Subversion and Git data, every month going back to 2002.
2 Jan 2025, 3:33:26 UTC · Discuss

News is available as an RSS feed