New Poll Regarding GPU Application of N-Body  |
Hey everyone,
We are currently looking at making a GPU version of N-Body. This code has been under development for quite some time, and the base code is finally working, though we would still need to implement some other features to run it alongside the CPU version. However, due to the complexity of our code and our need for double precision, the GPU version has a similar runtime to that of the CPU version, though there may be some speed-up on professional grade GPU cards. For reference, the GPU version of the Separation code is roughly 50-60 times faster than its CPU counterpart depending on the machine. Keeping that in mind, do you guys still want a GPU version of N-Body? I have put up a basic straw poll on https://www.strawpoll.me/45510486. If you wish to elaborate on your choice, please feel free to comment below.
Thank you all for your input, time, and consideration,
-Eric
|
|
| New badges - higher levels |
|
As requested there are now new badges / levels available. These will compensate the TF app with 500M, 1G, 2G, 5G and 10G max. You can find all in the FAQ.
|
|
| 12-digit factor of P2203 has now been found by the project... |
|
...304538 times
|
|
| Separation Validator Updates/Brief Server Outage(s) |
Hello Everyone,
I will be updating the separation validator starting at 3PM ET. The server will go down for a short time and then come back up. In the case that the new validator causes problems, the server will go back down again to revert to the old validator. I will be monitoring the situation and would appreciate input on any workunits that fail validation after the new validator goes live.
The server may go down/back up a few times during this process. Thanks for your patience. I'll keep you all posted on the status of things as they happen.
Best,
Tom
|
|
| World Emoji Day Challenge starts July 17th |
|
The fifth challenge of the 2021 Series will be a 3-day challenge in celebration of what is arguably the internet's most momentous and culturally significant holiday: World Emoji Day. The challenge will be offered on the GFN-17-Low subproject, beginning 17 July 22:00 UTC and ending 20 July 22:00 UTC.
To participate in the Challenge, please select only the GFN-17-Low subproject in your PrimeGrid preferences section.
For more info, check out the forum thread for this challenge: https://www.primegrid.com/forum_thread.php?id=9706&nowrap=true#150796
Best of luck!
|
|
| Nebula progress report |
|
... wherein it's asserted that we're in the home stretch.
|
|
| New NBody Runs! |
Hey everyone,
The last set of N-body runs has converged beautifully. Thus, we have replaced the old runs with a set of new ones:
-de_nbody_06_16_2021_v176_40k__data__4
-de_nbody_06_16_2021_v176_40k__data__5
-de_nbody_06_16_2021_v176_40k__data__6
Since the last set ran without problems, we expect this one to run smoothly, too. Thank you all for your continued support.
-Eric
|
|
| Project status: July 2021 |
Dear all,
Our research is going on! Collaborators just started biological evaluation of 30 chemical compounds selected in SiDock@home for targets 3CLpro and PLpro. Biological evaluation in vitro works with the most prospective results that your computers have selected in silico. The process is iterative and will involve hundreds of experiments.
With your help, we have already processed 4 targets and 43% of the fifth one. Further workunits will be created basing on the biological evaluation and the latest scientific knowledge about the targets.
Many thanks and best wishes,
Team of SiDock@home
|
|
| Donations to SiDock@home |
|
Drug development is a time-consuming and costly process. With help of your computers, our team is able to speed up discovery of prospective candidates for in vitro tests. At the same time, the volume of biological evaluation is limited by funding. We are in constant search for additional sources, and, since SiDock@home allows the participants to earn Gridcoins, we find it a good idea to accept voluntary donations. If you feel like donating to the project, please use one of the listed options.
|
|
| Server problems |
|
We are experiencing multiple issues, e.g. hitting an undocumented upload size limit, and others. Please be patient.
|
|
| Trial Factoring 73-74bit range started |
|
The range 72-73bit is nearly done. There is starting new work for 73-74bit. The runtime is around 45min on a RX5500XT and decreasing.
|
|
| Update acemd3 app |
|
I deployed the new app, which now requires cuda 11.2 and hopefully support all the latest cards. Touching the cuda versions is always a nightmare in boinc scheduler so expect problems.
|
|
| SiDock@home is whitelisted in Gridcoin |
Hello everyone,
since today, SiDock@home is whitelisted in Gridcoin. It means that you can receive the Gridcoin cryptocurrency for contributing to SiDock@home. For instructions, please refer to the official website of Gridcoin. For a description how Gridcoin works, refer to their whitepaper. This technology is yet new to our team, but we will do our best to understand it and help you to use it.
|
|
| P2203:180000000 wu's (of 10000 trials each equiv.) processed! |
|
Thanks to all the users. (Please keep crunching!)
|
|
| 12-digit factor of P2203 has now been found by the project... |
|
...300316 times - still no sign of any larger factors
|
|
| Experimental Python tasks (beta) - task description |
|
Hello everyone, just wanted to give some updates about the machine learning - python jobs that Toni mentioned earlier in the "Experimental Python tasks (beta) " thread.
What are we trying to accomplish?
We are trying to train populations of intelligent agents in a distributed computational setting to solve reinforcement learning problems. This idea is inspired in the fact that human societies are knowledgeable as a whole, while individual agents have limited information. Also, every new generation of individuals attempts to expand and refine the knowledge inherited from previous ones, and the most interesting discoveries become part of a corpus of common knowledge. The idea is that small groups of agents will train in GPUgrid machines, and report their discoveries and findings. Information of multiple agents can be put in common and conveyed to new generations of machine learning agents. To the best of our knowledge this is the first time something of this sort is attempted in a GPUGrid-like platform, and has the potential to scale to solve problems unattainable in smaller scale settings.
Why most jobs were failing a few weeks ago?
It took us some time and testing to make simple agents work, but we managed to solve the problems in the previous weeks. Now, almost all agents train successfully.
Why are GPUs being underutilized? and why are CPU used for?
In the previous weeks we were running small scale tests, with small neural networks models that occupied little GPU memory. Also, some reinforcement learning environments, especially simple ones like those used in the test, run on CPU. Our idea is to scale to more complex models and environments to exploit the GPU capacity of the grid.
More information:
We use mainly PyTorch to train our neural networks. We only use Tensorboard because it is convenient for logging. We might remove that dependency in the future.
|
|
| IMPORTANT NBODY UPDATE! |
Hey everyone,
Due to unforeseen circumstances, we at MilkyWay@home are temporarily deprecating our newest version of Nbody (v1.80) and un-deprecating the previous version (v1.76) in its place. Since the lua files associated with each version are incompatible with each other, we have replaced the previous optimizations with a new set:
-de_nbody_06_16_2021_v176_40k__data__1
-de_nbody_06_16_2021_v176_40k__data__2
-de_nbody_06_16_2021_v176_40k__data__3
We have cancelled all jobs pertaining to the previous set of runs. However, there still may be a few which we were not able to cancel in time. These runs will most likely error out if you get them, but should do so rather quickly (about 2 seconds).
If any complications arise from this, please notify us immediately, and we will quickly find a solution.
Thank you all for your time and continued support,
-Eric Mendelsohn
|
|
| 14 new Einstein@Home pulsar discoveries in Fermi-LAT data |
Congratulations to all Einstein@Home volunteers, whose computers have found 14 new pulsars in data from the Large Area Telescope (LAT) on board the Fermi gamma-ray satellite. These new pulsars are listed on the FGRP discoveries webpage, along with the names of the volunteers whose computers identified the new systems with the highest significance. In total, Einstein@Home has now found 39 new gamma-ray pulsars in Fermi LAT data. read more |
|
| Nebula progress report |
|
Check out recent advances in drifting RFI removal.
|
|
| New Separation Runs 6/9/2021 |
Hello Everyone,
I've just put some new separation runs up on the server. Remember those stripe 84 and 85 runs that would start to throw validate errors as they became more optimized? I've been testing and comparing runs on different builds and *hopefully* that problem has been resolved.
The names of the new runs are:
de_modfit_84_bundle4_4s_south4s_gapfix
de_modfit_84_bundle4_4s_south4s_gapfix_bgset2
de_modfit_84_bundle4_4s_south4s_gapfix_bgset3
de_modfit_85_bundle4_4s_south4s_gapfix
de_modfit_85_bundle4_4s_south4s_gapfix_bgset2
de_modfit_85_bundle4_4s_south4s_gapfix_bgset3
Please keep an eye on these runs and let me know if anything odd happens (validate errors or otherwise). With any luck, everything will work perfectly! These are the last runs that need to optimized before the latest results of separation can be submitted to a journal to be published.
Additionally, I have taken down the following runs:
de_modfit_80_bundle4_4s_south4s_bgset_7
de_modfit_81_bundle4_4s_south4s_bgset_7
de_modfit_82_bundle4_4s_south4s_bgset_7
de_modfit_83_bundle4_4s_south4s_bgset_7
de_modfit_86_bundle4_4s_south4s_bgset_7
As always, the stopped runs will continue to show up in your workunit queue for a few days as they finish up. This is normal and expected. Thank you all for your support and help with this project.
Best,
Tom
|
|
