Recent items from the news feeds of various BOINC projects.
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
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
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
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View article · Sun, 16 Mar 2025 12:53:22 +0000
Dear Einstein@Home volunteers,
You may remember that we launched a Zooniverse project called “Einstein@Home: Pulsar Seekers” in October 2023. Now we have the first promising results.
Zooniverse volunteers have made millions of classifications for more than 240,000 candidates found in the Arecibo telescope's PALFA pulsar survey. More than 4400 candidates have been classified as promising after review by our scientists.
View article · Fri, 14 Mar 2025 16:08:20 +0000
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View article · Thu, 13 Mar 2025 17:05:22 +0000
Our BOINC services will be unavailable for a while this morning between 8 and 9AM CET for a database upgrade.
View article · Tue, 11 Mar 2025 06:16:13 +0000
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View article · Thu, 6 Mar 2025 20:30:08 +0000
First, we’d like to thank all our contributors who have supported and continue to contribute to our scientific projects through Rosetta@home. We want to update you on the status of Rosetta@home projects and our future plans.
With the advancement of AI models like AlphaFold and RosettaFold for protein structure predictions, Rosetta@home has been less used for this purpose. However, researchers are now utilizing Rosetta@home for small molecule and peptide designs, where even the current state-of-the-art AI models struggle due to limitations in generalizability to novel small molecules and non-canonical peptides.
Recently, we have developed a virtual screening protocol in Rosetta, named RosettaVS, for small molecule drug discovery. This work has been published in Nature Communications (https://doi.org/10.1038/s41467-024-52061-7), demonstrating that RosettaVS is one of the best physics-based virtual screening protocols. Combined with deep learning techniques, it can effectively screen multi-billion compound libraries and discover novel compounds for pharmaceutical targets.
While deep learning models like AlphaFold and RosettaFold can predict canonical peptide structures, they cannot handle peptides with non-canonical amino acids or mixed chirality. The physics-based force field in Rosetta has specialized terms to simulate these amino acids. Rosetta will be used to sample hundreds of thousands of different conformations of the designed peptide to validate the structure.
Looking ahead, Rosetta@home will be an invaluable platform for large-scale virtual screening and peptide simulations for drug discovery. We plan to launch more virtual screening jobs and peptide simulations on Rosetta@home in the near future.
Thank you!
View article · Tue, 4 Mar 2025 04:50:22 +0000
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View article · Thu, 20 Feb 2025 17:36:16 +0000
I want to congratulate all of our Einstein@Home volunteers, developers, and scientists: our project is 20 years old today. We officially launched Einstein@Home on February 19th 2005, exactly 20 years ago, at the annual meeting of the American Association for the Advancement of Science (AAAS) in Washington DC.
View article · Wed, 19 Feb 2025 17:20:21 +0000
New version 212.02 for Linux.
If the app is in a later running phase and a nfs.dat file was created, resume should also work.
View article · Mon, 27 Jan 2025 20:16:26 +0000
Dear Volunteers,
As you may have noticed, the task hiatus has extended longer than we would have liked. I apologize for not writing sooner, as I was hoping we could launch something earlier. However, given our current workload and the steps required for the new simulations, there will be no new tasks at least until the end of February.
I apologize for this decrease in activity; we will return as soon as possible.
Sincerely,
Jesús.
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Estimados voluntarios:
Como habéis podido comprobar, el parón de tareas se ha extendido más de lo que nos habría gustado. Disculpad que no haya escrito antes porque esperaba que pudieramos lanzar algo antes, pero vista la carga de trabajo que tenemos y los pasos que requieren las nuevas simulaciones, no habrá nuevas tareas por lo menos hasta finales de febrero.
Disculpad esta bajada de actividad, volveremos lo antes posible.
Atentamente,
Jesús.
View article · Mon, 27 Jan 2025 09:45:23 +0000
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View article · Sun, 26 Jan 2025 13:36:23 +0000
The workunits of the current S31 batch requiring 3 GB of RAM or more. Also workunits of the next R19 batch using much RAM.
View article · Tue, 21 Jan 2025 23:00:00 +0000
Hi everyone,
I wanted to put this out there as I'm seeing a high error rate on tasks running on older GPUs.
I've spent a chunk of time digging into these, and the most prevalent error is a launch timeout error from the cuda driver.
This is a possible indication of either hardware failure, or a buggy driver.
If you see a lot of "Error 702" failing tasks, please update your drivers to the latest possible for your OS and video card, as there's potentially an issue with your current driver preventing work from completing on your card.
Please let us know if you have any questions, or need any help determining the issue.
Thanks!
View article · Fri, 17 Jan 2025 05:02:26 +0000
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View article · Thu, 16 Jan 2025 21:17:21 +0000
Hi Folks!
We're back with another round of tasks. This time for Nvidia GPUs.
The first batch will have 262144 workunits (524288 tasks), but we anticipate that there will be far more in the future for this project fed in gradually. (Estimated 17.1 billion workunits possible).
Apologies for the lack of communication/delays the last few months. These days I'm the primary administrator on this project, and while I do pay attention to our discord more, I should do better to keep the community on the BOINC platform in the loop about future updates.
Beyond this project, we may have one other lined up (much smaller in scope than this 17.1 billion workunit project) and an overall platform upgrade that I've slowly been widdling away at these last few months as I find time.
I'll be pushing the new work tonight, so get your GPUs ready! They're available on Windows and Linux x86_64 machines.
Best Regards,
BoySanic
View article · Sun, 12 Jan 2025 03:57:41 +0000
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